Economic Effectiveness of Implementing a Statewide
Building Code The Case of Florida
Kevin Simmons
Austin College
Jeffrey Czajkowski Wharton School Risk Center
University of Pennsylvania
James M Done
National Center for Atmospheric
Research Boulder CO
May 2016
Working Paper 2016-01
_____________________________________________________________________
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Philadelphia PA 19104 USA
Phone 215-898-5688 Fax 215-573-2130
httpsriskcenterwhartonupennedu ___________________________________________________________________________
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The Risk Centerrsquos neutrality allows it to undertake large-scale projects in conjunction with other researchers and organizations in the public and private sectors Building on the disciplines of economics decision sciences finance insurance marketing and psychology the Center supports and undertakes field and experimental studies of risk and uncertainty to better understand how individuals and organizations make choices under conditions of risk and uncertainty Risk Center research also investigates the effectiveness of strategies such as risk communication information sharing incentive systems insurance regulation and public-private collaborations at a national and international scale From these findings the Wharton Risk Centerrsquos research team ndash over 50 faculty fellows and doctoral students ndash is able to design new approaches to enable individuals and organizations to make better decisions regarding risk under various regulatory and market conditions
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1
Economic Effectiveness of Implementing a Statewide Building Code The Case of Florida
Kevin M Simmons PhD
Austin College
ksimmonsaustincollegeedu
Jeffrey Czajkowski PhD
Wharton Risk Management and Decision Processes Center
University of Pennsylvania
jczajwhartonupennedu
James M Done PhD
National Center for Atmospheric Research Boulder CO
doneucaredu
May 1 2017
Abstract
Hurricane Andrew revealed inadequate construction practices were
utilized in Florida for decades In response Florida adopted a new
statewide code ndash the 2001 Florida Building Code (FBC) which became
one of the strictest in the nation We use ten years of insured loss data
to show that the FBC reduced windstorm losses by up to 72 then use
our results to conduct a benefit-cost analysis (BCA) The FBC passes
the BCA by a margin of 5 dollars in reduced loss to 1 dollar of added
cost with a payback period of approximately 10 years
The authors would like to acknowledge the assistance of the Insurance Services Office the Florida
Department of Emergency Management and Florida International University for data and research support
2
I Introduction
Despite the recognition that strong building codes are a key risk reduction strategy in reducing
total property damage due to natural disaster occurrence as well as making communities more
resilient (Mills et al 2005 Kunreuther and Useem 2010 McHale and Leurig 2012 Vaughn and
Turner 2014 NIBS 2015 Rochman 2015 Jain 2009) in the United States there is not a single
national building code for all states to follow Rather building code adoption and enforcement is
left to individual state discretion Consequently across the country there is a spectrum of building
code implementation (both commercial and residential) where on one end there are states
implementing a mandatory statewide code on the other end building codes are left up to local
jurisdictions and a mix in-betweeni
Moreover even for those states that do have a statewide code in place there is much
variation in the overall effectiveness of its implementation The Insurance Institute for Business
and Home Safety (IBHS) ranks the residential building codes adopted in 18 states along the
Atlantic and Gulf Coasts most vulnerable to hurricane damages on a scale of 0 (worst) to 100 (best)
with the ranking accounting for each statersquos code strength and enforcement building official
certification and training and contractor licensing For the 14 states having some notion of a
mandatory residential statewide code in place scores ranged from 28 (Mississippi) to 95
(Virginia) with 43 percent of the 14 mandatory states scoring below 80 (IBHS 2015) Given the
increasing attention natural disasters receive this is surprising as public sector involvement can be
an important element toward reducing disaster losses in a cost effective manner (Kunreuther
2006)
Florida is highly vulnerable to hurricane damages ndash approximately $18 trillion of
residential property exposure (Hamid et al 2011) ndash as well as the oft-referenced gold standard of
3
a strong statewide building code ndash IBHS score of 94 in 2015 (2nd) and 95 in 2012 (1st) (IBHS
2015) Although the extensive property exposure at risk to hurricanes relative to other states has
been continual for Florida since the early part of the 20th century a strong and uniform building
code standard has not Hurricane Andrew which made landfall in South Florida as a category 5
hurricane in 1992 destroyed more than 25000 homes and damaged 100000 others causing $26
billion in total damage (inflation adjusted) making it the costliest catastrophic event in history at
that time (Fronstin and Holtmann 1994) Eleven insurance companies became insolvent as a
result
After Hurricane Andrew it became clear that construction practices in place during the
1980s had not been sufficient to withstand such a powerful wind storm (Sparks et al 1994) Post-
storm inspections detected inferior construction practices which had thus unnecessarily magnified
the extensive damage (Fronstin and Holtmann 1994 Keith and Rose 1994) In the aftermath of
Hurricane Andrew Florida began enacting statewide building code change that wrested away
building code adoption control from individual localities The first communities to strengthen
their building code were the counties of Broward Dade and Monroe all of which already adhered
to the stronger South Florida Building Code (SFBC) Standards for the SFBC were upgraded in
1994 with an emphasis on improving the integrity of the building envelope including impact
protection for exterior windows and doors Beyond the counties in the SFBC some communities
began adopting stronger local codes as well In 1996 the Florida Building Code Commission
began a study to make recommendations on a statewide basis in consultation with wind engineers
The Florida Legislature in 1998 authorized the recommended changes statewide creating the
2001 Florida Building Code The FBC is based on the national model codes developed by the
International Code Council (ICC) and is among the strictest in the nation heavily emphasizing
4
wind engineering standards and other additions for Floridarsquos specific needs including for hurricane
protection (Dixon 2009)
In this study we first quantify the reduction of residential property wind damages due to
the implementation of the FBC utilizing realized insurance policy claim and loss data across the
entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity
(RD) model using a treatment of Post FBC construction and a rating variable of structure age
Following from our claim-based empirical loss estimations we then further assess the economic
effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet
important research component in wholly assessing building code augmentations Especially as
enhanced building codes increase new construction costs moving forward both pieces of
information are critical in not only highlighting the value of a statewide building code but also in
generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and
Turner 2014 NIBS 2015)
The article proceeds as follows Section 2 is a discussion of existing assessments of
windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides
a discussion of the econometric methodology Section 5 discusses regression results and provides
an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section
7 concludes the article
II Review of Existing Assessments of Windstorm Building Code Effectiveness
Several studies have identified the reduction in windstorm losses due to stronger building
codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)
in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes
built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an
5
eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and
2005 in Florida provided a natural opportunity to test how well the implemented FBC performed
A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after
1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to
homes built before 1996 This suggests the trend of an eroding building code reversed after
Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from
eight different insurance companies following the 2004 and 2005 hurricane seasons and found
similar results with post-2002 homes showing significant loss reduction results compared to pre-
2002 homes They further found that overall losses were reduced in year built from the mid-1990s
onward Although only indirectly associated with actual damages incurred stronger building
codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at
least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm
Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that
for the 264 residential properties in a coastal building zone in Lee County following Hurricane
Charley there is no evidence of less damage for homes built after the revised 1992 Florida building
code
Our study advances this building code literature in several important ways First we
collect annualized private market insured policy and loss data (number of claims and total damages
for all represented earned house years in the insured portfolio) from the Insurance Services Office
(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010
inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO
industry data represents a significant percent of total private propertycasualty insurance annual
market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000
6
to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number
space and time ndash insured loss and premium data for this analysis than previous studies Lastly
Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and
2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond
2004 and 2005
Finally following from our claim and loss analysis we perform a BCA on the
implementation of the FBC Our BCA is unique in that we use actual loss data rather than
probabilistic estimates of future loss as previous studies have and our loss data spans a longer time
period of 10 years in order to control for the effect of post FBC construction
III Florida Windstorm Losses and Associated Data
We quantify historical Florida wind event loss reductions due to the implemented FBC
through an econometric driven loss methodology that systematically accounts for relevant wind
hazard exposure and vulnerability characteristics evolving over time from the adoption of the
new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade
of construction In addition to insured loss data we have several variables from ISO collected by
insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and
represents the number of policyholders in each ZIP code Premiums is the total annual premiums
collected and BrickMasonry is the percent of homes that have exterior cladding made from brick
or other masonry products
Florida Insured Loss Data
For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data
from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in
any one year ranging from 669000 to just over 1 million insured policyholders representing
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Benefit Comparison Study
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Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
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Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
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Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
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Dixon R (2009) Florida Building Commission Presentation Available at -
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0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
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Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
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Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
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Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
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Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
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Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
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Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
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Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
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Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
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Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
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Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
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The Risk Centerrsquos neutrality allows it to undertake large-scale projects in conjunction with other researchers and organizations in the public and private sectors Building on the disciplines of economics decision sciences finance insurance marketing and psychology the Center supports and undertakes field and experimental studies of risk and uncertainty to better understand how individuals and organizations make choices under conditions of risk and uncertainty Risk Center research also investigates the effectiveness of strategies such as risk communication information sharing incentive systems insurance regulation and public-private collaborations at a national and international scale From these findings the Wharton Risk Centerrsquos research team ndash over 50 faculty fellows and doctoral students ndash is able to design new approaches to enable individuals and organizations to make better decisions regarding risk under various regulatory and market conditions
The Center is also concerned with training leading decision makers It actively engages multiple viewpoints including top-level representatives from industry government international organizations interest groups and academics through its research and policy publications and through sponsored seminars roundtables and forums
More information is available at httpsriskcenterwhartonupennedu
1
Economic Effectiveness of Implementing a Statewide Building Code The Case of Florida
Kevin M Simmons PhD
Austin College
ksimmonsaustincollegeedu
Jeffrey Czajkowski PhD
Wharton Risk Management and Decision Processes Center
University of Pennsylvania
jczajwhartonupennedu
James M Done PhD
National Center for Atmospheric Research Boulder CO
doneucaredu
May 1 2017
Abstract
Hurricane Andrew revealed inadequate construction practices were
utilized in Florida for decades In response Florida adopted a new
statewide code ndash the 2001 Florida Building Code (FBC) which became
one of the strictest in the nation We use ten years of insured loss data
to show that the FBC reduced windstorm losses by up to 72 then use
our results to conduct a benefit-cost analysis (BCA) The FBC passes
the BCA by a margin of 5 dollars in reduced loss to 1 dollar of added
cost with a payback period of approximately 10 years
The authors would like to acknowledge the assistance of the Insurance Services Office the Florida
Department of Emergency Management and Florida International University for data and research support
2
I Introduction
Despite the recognition that strong building codes are a key risk reduction strategy in reducing
total property damage due to natural disaster occurrence as well as making communities more
resilient (Mills et al 2005 Kunreuther and Useem 2010 McHale and Leurig 2012 Vaughn and
Turner 2014 NIBS 2015 Rochman 2015 Jain 2009) in the United States there is not a single
national building code for all states to follow Rather building code adoption and enforcement is
left to individual state discretion Consequently across the country there is a spectrum of building
code implementation (both commercial and residential) where on one end there are states
implementing a mandatory statewide code on the other end building codes are left up to local
jurisdictions and a mix in-betweeni
Moreover even for those states that do have a statewide code in place there is much
variation in the overall effectiveness of its implementation The Insurance Institute for Business
and Home Safety (IBHS) ranks the residential building codes adopted in 18 states along the
Atlantic and Gulf Coasts most vulnerable to hurricane damages on a scale of 0 (worst) to 100 (best)
with the ranking accounting for each statersquos code strength and enforcement building official
certification and training and contractor licensing For the 14 states having some notion of a
mandatory residential statewide code in place scores ranged from 28 (Mississippi) to 95
(Virginia) with 43 percent of the 14 mandatory states scoring below 80 (IBHS 2015) Given the
increasing attention natural disasters receive this is surprising as public sector involvement can be
an important element toward reducing disaster losses in a cost effective manner (Kunreuther
2006)
Florida is highly vulnerable to hurricane damages ndash approximately $18 trillion of
residential property exposure (Hamid et al 2011) ndash as well as the oft-referenced gold standard of
3
a strong statewide building code ndash IBHS score of 94 in 2015 (2nd) and 95 in 2012 (1st) (IBHS
2015) Although the extensive property exposure at risk to hurricanes relative to other states has
been continual for Florida since the early part of the 20th century a strong and uniform building
code standard has not Hurricane Andrew which made landfall in South Florida as a category 5
hurricane in 1992 destroyed more than 25000 homes and damaged 100000 others causing $26
billion in total damage (inflation adjusted) making it the costliest catastrophic event in history at
that time (Fronstin and Holtmann 1994) Eleven insurance companies became insolvent as a
result
After Hurricane Andrew it became clear that construction practices in place during the
1980s had not been sufficient to withstand such a powerful wind storm (Sparks et al 1994) Post-
storm inspections detected inferior construction practices which had thus unnecessarily magnified
the extensive damage (Fronstin and Holtmann 1994 Keith and Rose 1994) In the aftermath of
Hurricane Andrew Florida began enacting statewide building code change that wrested away
building code adoption control from individual localities The first communities to strengthen
their building code were the counties of Broward Dade and Monroe all of which already adhered
to the stronger South Florida Building Code (SFBC) Standards for the SFBC were upgraded in
1994 with an emphasis on improving the integrity of the building envelope including impact
protection for exterior windows and doors Beyond the counties in the SFBC some communities
began adopting stronger local codes as well In 1996 the Florida Building Code Commission
began a study to make recommendations on a statewide basis in consultation with wind engineers
The Florida Legislature in 1998 authorized the recommended changes statewide creating the
2001 Florida Building Code The FBC is based on the national model codes developed by the
International Code Council (ICC) and is among the strictest in the nation heavily emphasizing
4
wind engineering standards and other additions for Floridarsquos specific needs including for hurricane
protection (Dixon 2009)
In this study we first quantify the reduction of residential property wind damages due to
the implementation of the FBC utilizing realized insurance policy claim and loss data across the
entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity
(RD) model using a treatment of Post FBC construction and a rating variable of structure age
Following from our claim-based empirical loss estimations we then further assess the economic
effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet
important research component in wholly assessing building code augmentations Especially as
enhanced building codes increase new construction costs moving forward both pieces of
information are critical in not only highlighting the value of a statewide building code but also in
generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and
Turner 2014 NIBS 2015)
The article proceeds as follows Section 2 is a discussion of existing assessments of
windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides
a discussion of the econometric methodology Section 5 discusses regression results and provides
an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section
7 concludes the article
II Review of Existing Assessments of Windstorm Building Code Effectiveness
Several studies have identified the reduction in windstorm losses due to stronger building
codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)
in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes
built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an
5
eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and
2005 in Florida provided a natural opportunity to test how well the implemented FBC performed
A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after
1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to
homes built before 1996 This suggests the trend of an eroding building code reversed after
Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from
eight different insurance companies following the 2004 and 2005 hurricane seasons and found
similar results with post-2002 homes showing significant loss reduction results compared to pre-
2002 homes They further found that overall losses were reduced in year built from the mid-1990s
onward Although only indirectly associated with actual damages incurred stronger building
codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at
least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm
Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that
for the 264 residential properties in a coastal building zone in Lee County following Hurricane
Charley there is no evidence of less damage for homes built after the revised 1992 Florida building
code
Our study advances this building code literature in several important ways First we
collect annualized private market insured policy and loss data (number of claims and total damages
for all represented earned house years in the insured portfolio) from the Insurance Services Office
(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010
inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO
industry data represents a significant percent of total private propertycasualty insurance annual
market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000
6
to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number
space and time ndash insured loss and premium data for this analysis than previous studies Lastly
Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and
2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond
2004 and 2005
Finally following from our claim and loss analysis we perform a BCA on the
implementation of the FBC Our BCA is unique in that we use actual loss data rather than
probabilistic estimates of future loss as previous studies have and our loss data spans a longer time
period of 10 years in order to control for the effect of post FBC construction
III Florida Windstorm Losses and Associated Data
We quantify historical Florida wind event loss reductions due to the implemented FBC
through an econometric driven loss methodology that systematically accounts for relevant wind
hazard exposure and vulnerability characteristics evolving over time from the adoption of the
new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade
of construction In addition to insured loss data we have several variables from ISO collected by
insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and
represents the number of policyholders in each ZIP code Premiums is the total annual premiums
collected and BrickMasonry is the percent of homes that have exterior cladding made from brick
or other masonry products
Florida Insured Loss Data
For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data
from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in
any one year ranging from 669000 to just over 1 million insured policyholders representing
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
1
Economic Effectiveness of Implementing a Statewide Building Code The Case of Florida
Kevin M Simmons PhD
Austin College
ksimmonsaustincollegeedu
Jeffrey Czajkowski PhD
Wharton Risk Management and Decision Processes Center
University of Pennsylvania
jczajwhartonupennedu
James M Done PhD
National Center for Atmospheric Research Boulder CO
doneucaredu
May 1 2017
Abstract
Hurricane Andrew revealed inadequate construction practices were
utilized in Florida for decades In response Florida adopted a new
statewide code ndash the 2001 Florida Building Code (FBC) which became
one of the strictest in the nation We use ten years of insured loss data
to show that the FBC reduced windstorm losses by up to 72 then use
our results to conduct a benefit-cost analysis (BCA) The FBC passes
the BCA by a margin of 5 dollars in reduced loss to 1 dollar of added
cost with a payback period of approximately 10 years
The authors would like to acknowledge the assistance of the Insurance Services Office the Florida
Department of Emergency Management and Florida International University for data and research support
2
I Introduction
Despite the recognition that strong building codes are a key risk reduction strategy in reducing
total property damage due to natural disaster occurrence as well as making communities more
resilient (Mills et al 2005 Kunreuther and Useem 2010 McHale and Leurig 2012 Vaughn and
Turner 2014 NIBS 2015 Rochman 2015 Jain 2009) in the United States there is not a single
national building code for all states to follow Rather building code adoption and enforcement is
left to individual state discretion Consequently across the country there is a spectrum of building
code implementation (both commercial and residential) where on one end there are states
implementing a mandatory statewide code on the other end building codes are left up to local
jurisdictions and a mix in-betweeni
Moreover even for those states that do have a statewide code in place there is much
variation in the overall effectiveness of its implementation The Insurance Institute for Business
and Home Safety (IBHS) ranks the residential building codes adopted in 18 states along the
Atlantic and Gulf Coasts most vulnerable to hurricane damages on a scale of 0 (worst) to 100 (best)
with the ranking accounting for each statersquos code strength and enforcement building official
certification and training and contractor licensing For the 14 states having some notion of a
mandatory residential statewide code in place scores ranged from 28 (Mississippi) to 95
(Virginia) with 43 percent of the 14 mandatory states scoring below 80 (IBHS 2015) Given the
increasing attention natural disasters receive this is surprising as public sector involvement can be
an important element toward reducing disaster losses in a cost effective manner (Kunreuther
2006)
Florida is highly vulnerable to hurricane damages ndash approximately $18 trillion of
residential property exposure (Hamid et al 2011) ndash as well as the oft-referenced gold standard of
3
a strong statewide building code ndash IBHS score of 94 in 2015 (2nd) and 95 in 2012 (1st) (IBHS
2015) Although the extensive property exposure at risk to hurricanes relative to other states has
been continual for Florida since the early part of the 20th century a strong and uniform building
code standard has not Hurricane Andrew which made landfall in South Florida as a category 5
hurricane in 1992 destroyed more than 25000 homes and damaged 100000 others causing $26
billion in total damage (inflation adjusted) making it the costliest catastrophic event in history at
that time (Fronstin and Holtmann 1994) Eleven insurance companies became insolvent as a
result
After Hurricane Andrew it became clear that construction practices in place during the
1980s had not been sufficient to withstand such a powerful wind storm (Sparks et al 1994) Post-
storm inspections detected inferior construction practices which had thus unnecessarily magnified
the extensive damage (Fronstin and Holtmann 1994 Keith and Rose 1994) In the aftermath of
Hurricane Andrew Florida began enacting statewide building code change that wrested away
building code adoption control from individual localities The first communities to strengthen
their building code were the counties of Broward Dade and Monroe all of which already adhered
to the stronger South Florida Building Code (SFBC) Standards for the SFBC were upgraded in
1994 with an emphasis on improving the integrity of the building envelope including impact
protection for exterior windows and doors Beyond the counties in the SFBC some communities
began adopting stronger local codes as well In 1996 the Florida Building Code Commission
began a study to make recommendations on a statewide basis in consultation with wind engineers
The Florida Legislature in 1998 authorized the recommended changes statewide creating the
2001 Florida Building Code The FBC is based on the national model codes developed by the
International Code Council (ICC) and is among the strictest in the nation heavily emphasizing
4
wind engineering standards and other additions for Floridarsquos specific needs including for hurricane
protection (Dixon 2009)
In this study we first quantify the reduction of residential property wind damages due to
the implementation of the FBC utilizing realized insurance policy claim and loss data across the
entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity
(RD) model using a treatment of Post FBC construction and a rating variable of structure age
Following from our claim-based empirical loss estimations we then further assess the economic
effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet
important research component in wholly assessing building code augmentations Especially as
enhanced building codes increase new construction costs moving forward both pieces of
information are critical in not only highlighting the value of a statewide building code but also in
generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and
Turner 2014 NIBS 2015)
The article proceeds as follows Section 2 is a discussion of existing assessments of
windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides
a discussion of the econometric methodology Section 5 discusses regression results and provides
an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section
7 concludes the article
II Review of Existing Assessments of Windstorm Building Code Effectiveness
Several studies have identified the reduction in windstorm losses due to stronger building
codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)
in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes
built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an
5
eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and
2005 in Florida provided a natural opportunity to test how well the implemented FBC performed
A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after
1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to
homes built before 1996 This suggests the trend of an eroding building code reversed after
Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from
eight different insurance companies following the 2004 and 2005 hurricane seasons and found
similar results with post-2002 homes showing significant loss reduction results compared to pre-
2002 homes They further found that overall losses were reduced in year built from the mid-1990s
onward Although only indirectly associated with actual damages incurred stronger building
codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at
least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm
Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that
for the 264 residential properties in a coastal building zone in Lee County following Hurricane
Charley there is no evidence of less damage for homes built after the revised 1992 Florida building
code
Our study advances this building code literature in several important ways First we
collect annualized private market insured policy and loss data (number of claims and total damages
for all represented earned house years in the insured portfolio) from the Insurance Services Office
(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010
inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO
industry data represents a significant percent of total private propertycasualty insurance annual
market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000
6
to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number
space and time ndash insured loss and premium data for this analysis than previous studies Lastly
Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and
2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond
2004 and 2005
Finally following from our claim and loss analysis we perform a BCA on the
implementation of the FBC Our BCA is unique in that we use actual loss data rather than
probabilistic estimates of future loss as previous studies have and our loss data spans a longer time
period of 10 years in order to control for the effect of post FBC construction
III Florida Windstorm Losses and Associated Data
We quantify historical Florida wind event loss reductions due to the implemented FBC
through an econometric driven loss methodology that systematically accounts for relevant wind
hazard exposure and vulnerability characteristics evolving over time from the adoption of the
new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade
of construction In addition to insured loss data we have several variables from ISO collected by
insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and
represents the number of policyholders in each ZIP code Premiums is the total annual premiums
collected and BrickMasonry is the percent of homes that have exterior cladding made from brick
or other masonry products
Florida Insured Loss Data
For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data
from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in
any one year ranging from 669000 to just over 1 million insured policyholders representing
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
2
I Introduction
Despite the recognition that strong building codes are a key risk reduction strategy in reducing
total property damage due to natural disaster occurrence as well as making communities more
resilient (Mills et al 2005 Kunreuther and Useem 2010 McHale and Leurig 2012 Vaughn and
Turner 2014 NIBS 2015 Rochman 2015 Jain 2009) in the United States there is not a single
national building code for all states to follow Rather building code adoption and enforcement is
left to individual state discretion Consequently across the country there is a spectrum of building
code implementation (both commercial and residential) where on one end there are states
implementing a mandatory statewide code on the other end building codes are left up to local
jurisdictions and a mix in-betweeni
Moreover even for those states that do have a statewide code in place there is much
variation in the overall effectiveness of its implementation The Insurance Institute for Business
and Home Safety (IBHS) ranks the residential building codes adopted in 18 states along the
Atlantic and Gulf Coasts most vulnerable to hurricane damages on a scale of 0 (worst) to 100 (best)
with the ranking accounting for each statersquos code strength and enforcement building official
certification and training and contractor licensing For the 14 states having some notion of a
mandatory residential statewide code in place scores ranged from 28 (Mississippi) to 95
(Virginia) with 43 percent of the 14 mandatory states scoring below 80 (IBHS 2015) Given the
increasing attention natural disasters receive this is surprising as public sector involvement can be
an important element toward reducing disaster losses in a cost effective manner (Kunreuther
2006)
Florida is highly vulnerable to hurricane damages ndash approximately $18 trillion of
residential property exposure (Hamid et al 2011) ndash as well as the oft-referenced gold standard of
3
a strong statewide building code ndash IBHS score of 94 in 2015 (2nd) and 95 in 2012 (1st) (IBHS
2015) Although the extensive property exposure at risk to hurricanes relative to other states has
been continual for Florida since the early part of the 20th century a strong and uniform building
code standard has not Hurricane Andrew which made landfall in South Florida as a category 5
hurricane in 1992 destroyed more than 25000 homes and damaged 100000 others causing $26
billion in total damage (inflation adjusted) making it the costliest catastrophic event in history at
that time (Fronstin and Holtmann 1994) Eleven insurance companies became insolvent as a
result
After Hurricane Andrew it became clear that construction practices in place during the
1980s had not been sufficient to withstand such a powerful wind storm (Sparks et al 1994) Post-
storm inspections detected inferior construction practices which had thus unnecessarily magnified
the extensive damage (Fronstin and Holtmann 1994 Keith and Rose 1994) In the aftermath of
Hurricane Andrew Florida began enacting statewide building code change that wrested away
building code adoption control from individual localities The first communities to strengthen
their building code were the counties of Broward Dade and Monroe all of which already adhered
to the stronger South Florida Building Code (SFBC) Standards for the SFBC were upgraded in
1994 with an emphasis on improving the integrity of the building envelope including impact
protection for exterior windows and doors Beyond the counties in the SFBC some communities
began adopting stronger local codes as well In 1996 the Florida Building Code Commission
began a study to make recommendations on a statewide basis in consultation with wind engineers
The Florida Legislature in 1998 authorized the recommended changes statewide creating the
2001 Florida Building Code The FBC is based on the national model codes developed by the
International Code Council (ICC) and is among the strictest in the nation heavily emphasizing
4
wind engineering standards and other additions for Floridarsquos specific needs including for hurricane
protection (Dixon 2009)
In this study we first quantify the reduction of residential property wind damages due to
the implementation of the FBC utilizing realized insurance policy claim and loss data across the
entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity
(RD) model using a treatment of Post FBC construction and a rating variable of structure age
Following from our claim-based empirical loss estimations we then further assess the economic
effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet
important research component in wholly assessing building code augmentations Especially as
enhanced building codes increase new construction costs moving forward both pieces of
information are critical in not only highlighting the value of a statewide building code but also in
generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and
Turner 2014 NIBS 2015)
The article proceeds as follows Section 2 is a discussion of existing assessments of
windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides
a discussion of the econometric methodology Section 5 discusses regression results and provides
an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section
7 concludes the article
II Review of Existing Assessments of Windstorm Building Code Effectiveness
Several studies have identified the reduction in windstorm losses due to stronger building
codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)
in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes
built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an
5
eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and
2005 in Florida provided a natural opportunity to test how well the implemented FBC performed
A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after
1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to
homes built before 1996 This suggests the trend of an eroding building code reversed after
Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from
eight different insurance companies following the 2004 and 2005 hurricane seasons and found
similar results with post-2002 homes showing significant loss reduction results compared to pre-
2002 homes They further found that overall losses were reduced in year built from the mid-1990s
onward Although only indirectly associated with actual damages incurred stronger building
codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at
least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm
Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that
for the 264 residential properties in a coastal building zone in Lee County following Hurricane
Charley there is no evidence of less damage for homes built after the revised 1992 Florida building
code
Our study advances this building code literature in several important ways First we
collect annualized private market insured policy and loss data (number of claims and total damages
for all represented earned house years in the insured portfolio) from the Insurance Services Office
(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010
inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO
industry data represents a significant percent of total private propertycasualty insurance annual
market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000
6
to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number
space and time ndash insured loss and premium data for this analysis than previous studies Lastly
Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and
2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond
2004 and 2005
Finally following from our claim and loss analysis we perform a BCA on the
implementation of the FBC Our BCA is unique in that we use actual loss data rather than
probabilistic estimates of future loss as previous studies have and our loss data spans a longer time
period of 10 years in order to control for the effect of post FBC construction
III Florida Windstorm Losses and Associated Data
We quantify historical Florida wind event loss reductions due to the implemented FBC
through an econometric driven loss methodology that systematically accounts for relevant wind
hazard exposure and vulnerability characteristics evolving over time from the adoption of the
new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade
of construction In addition to insured loss data we have several variables from ISO collected by
insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and
represents the number of policyholders in each ZIP code Premiums is the total annual premiums
collected and BrickMasonry is the percent of homes that have exterior cladding made from brick
or other masonry products
Florida Insured Loss Data
For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data
from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in
any one year ranging from 669000 to just over 1 million insured policyholders representing
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
3
a strong statewide building code ndash IBHS score of 94 in 2015 (2nd) and 95 in 2012 (1st) (IBHS
2015) Although the extensive property exposure at risk to hurricanes relative to other states has
been continual for Florida since the early part of the 20th century a strong and uniform building
code standard has not Hurricane Andrew which made landfall in South Florida as a category 5
hurricane in 1992 destroyed more than 25000 homes and damaged 100000 others causing $26
billion in total damage (inflation adjusted) making it the costliest catastrophic event in history at
that time (Fronstin and Holtmann 1994) Eleven insurance companies became insolvent as a
result
After Hurricane Andrew it became clear that construction practices in place during the
1980s had not been sufficient to withstand such a powerful wind storm (Sparks et al 1994) Post-
storm inspections detected inferior construction practices which had thus unnecessarily magnified
the extensive damage (Fronstin and Holtmann 1994 Keith and Rose 1994) In the aftermath of
Hurricane Andrew Florida began enacting statewide building code change that wrested away
building code adoption control from individual localities The first communities to strengthen
their building code were the counties of Broward Dade and Monroe all of which already adhered
to the stronger South Florida Building Code (SFBC) Standards for the SFBC were upgraded in
1994 with an emphasis on improving the integrity of the building envelope including impact
protection for exterior windows and doors Beyond the counties in the SFBC some communities
began adopting stronger local codes as well In 1996 the Florida Building Code Commission
began a study to make recommendations on a statewide basis in consultation with wind engineers
The Florida Legislature in 1998 authorized the recommended changes statewide creating the
2001 Florida Building Code The FBC is based on the national model codes developed by the
International Code Council (ICC) and is among the strictest in the nation heavily emphasizing
4
wind engineering standards and other additions for Floridarsquos specific needs including for hurricane
protection (Dixon 2009)
In this study we first quantify the reduction of residential property wind damages due to
the implementation of the FBC utilizing realized insurance policy claim and loss data across the
entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity
(RD) model using a treatment of Post FBC construction and a rating variable of structure age
Following from our claim-based empirical loss estimations we then further assess the economic
effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet
important research component in wholly assessing building code augmentations Especially as
enhanced building codes increase new construction costs moving forward both pieces of
information are critical in not only highlighting the value of a statewide building code but also in
generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and
Turner 2014 NIBS 2015)
The article proceeds as follows Section 2 is a discussion of existing assessments of
windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides
a discussion of the econometric methodology Section 5 discusses regression results and provides
an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section
7 concludes the article
II Review of Existing Assessments of Windstorm Building Code Effectiveness
Several studies have identified the reduction in windstorm losses due to stronger building
codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)
in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes
built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an
5
eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and
2005 in Florida provided a natural opportunity to test how well the implemented FBC performed
A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after
1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to
homes built before 1996 This suggests the trend of an eroding building code reversed after
Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from
eight different insurance companies following the 2004 and 2005 hurricane seasons and found
similar results with post-2002 homes showing significant loss reduction results compared to pre-
2002 homes They further found that overall losses were reduced in year built from the mid-1990s
onward Although only indirectly associated with actual damages incurred stronger building
codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at
least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm
Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that
for the 264 residential properties in a coastal building zone in Lee County following Hurricane
Charley there is no evidence of less damage for homes built after the revised 1992 Florida building
code
Our study advances this building code literature in several important ways First we
collect annualized private market insured policy and loss data (number of claims and total damages
for all represented earned house years in the insured portfolio) from the Insurance Services Office
(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010
inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO
industry data represents a significant percent of total private propertycasualty insurance annual
market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000
6
to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number
space and time ndash insured loss and premium data for this analysis than previous studies Lastly
Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and
2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond
2004 and 2005
Finally following from our claim and loss analysis we perform a BCA on the
implementation of the FBC Our BCA is unique in that we use actual loss data rather than
probabilistic estimates of future loss as previous studies have and our loss data spans a longer time
period of 10 years in order to control for the effect of post FBC construction
III Florida Windstorm Losses and Associated Data
We quantify historical Florida wind event loss reductions due to the implemented FBC
through an econometric driven loss methodology that systematically accounts for relevant wind
hazard exposure and vulnerability characteristics evolving over time from the adoption of the
new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade
of construction In addition to insured loss data we have several variables from ISO collected by
insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and
represents the number of policyholders in each ZIP code Premiums is the total annual premiums
collected and BrickMasonry is the percent of homes that have exterior cladding made from brick
or other masonry products
Florida Insured Loss Data
For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data
from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in
any one year ranging from 669000 to just over 1 million insured policyholders representing
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
4
wind engineering standards and other additions for Floridarsquos specific needs including for hurricane
protection (Dixon 2009)
In this study we first quantify the reduction of residential property wind damages due to
the implementation of the FBC utilizing realized insurance policy claim and loss data across the
entire state of Florida spanning the years 2001 to 2010 We utilize a Regression Discontinuity
(RD) model using a treatment of Post FBC construction and a rating variable of structure age
Following from our claim-based empirical loss estimations we then further assess the economic
effectiveness of the FBC through a benefit-cost analysis (BCA) a relatively underserved yet
important research component in wholly assessing building code augmentations Especially as
enhanced building codes increase new construction costs moving forward both pieces of
information are critical in not only highlighting the value of a statewide building code but also in
generating political and consumer support for its implementation (Kunreuther 2006 Vaughan and
Turner 2014 NIBS 2015)
The article proceeds as follows Section 2 is a discussion of existing assessments of
windstorm building code effectiveness Section 3 is an overview of the data and Section 4 provides
a discussion of the econometric methodology Section 5 discusses regression results and provides
an evaluation of the regression model Section 6 is a BenefitCost Analysis of the FBC and Section
7 concludes the article
II Review of Existing Assessments of Windstorm Building Code Effectiveness
Several studies have identified the reduction in windstorm losses due to stronger building
codes utilizing event-based realized loss or insurance claim dataii Fronstin and Holtmann (1994)
in their analysis of 1992 Hurricane Andrew damages in southeast Florida find that older homes
built prior to the 1960rsquos suffered less damage on average than those built after 1960 due to an
5
eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and
2005 in Florida provided a natural opportunity to test how well the implemented FBC performed
A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after
1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to
homes built before 1996 This suggests the trend of an eroding building code reversed after
Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from
eight different insurance companies following the 2004 and 2005 hurricane seasons and found
similar results with post-2002 homes showing significant loss reduction results compared to pre-
2002 homes They further found that overall losses were reduced in year built from the mid-1990s
onward Although only indirectly associated with actual damages incurred stronger building
codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at
least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm
Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that
for the 264 residential properties in a coastal building zone in Lee County following Hurricane
Charley there is no evidence of less damage for homes built after the revised 1992 Florida building
code
Our study advances this building code literature in several important ways First we
collect annualized private market insured policy and loss data (number of claims and total damages
for all represented earned house years in the insured portfolio) from the Insurance Services Office
(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010
inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO
industry data represents a significant percent of total private propertycasualty insurance annual
market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000
6
to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number
space and time ndash insured loss and premium data for this analysis than previous studies Lastly
Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and
2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond
2004 and 2005
Finally following from our claim and loss analysis we perform a BCA on the
implementation of the FBC Our BCA is unique in that we use actual loss data rather than
probabilistic estimates of future loss as previous studies have and our loss data spans a longer time
period of 10 years in order to control for the effect of post FBC construction
III Florida Windstorm Losses and Associated Data
We quantify historical Florida wind event loss reductions due to the implemented FBC
through an econometric driven loss methodology that systematically accounts for relevant wind
hazard exposure and vulnerability characteristics evolving over time from the adoption of the
new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade
of construction In addition to insured loss data we have several variables from ISO collected by
insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and
represents the number of policyholders in each ZIP code Premiums is the total annual premiums
collected and BrickMasonry is the percent of homes that have exterior cladding made from brick
or other masonry products
Florida Insured Loss Data
For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data
from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in
any one year ranging from 669000 to just over 1 million insured policyholders representing
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
5
eroding building code over time Post-Andrew the catastrophic hurricane seasons of 2004 and
2005 in Florida provided a natural opportunity to test how well the implemented FBC performed
A study by IBHS following Hurricane Charley in 2004 (IBHS 2004) found that homes built after
1996 had lower claim frequency (60 percent less) and severity (42 percent less) as compared to
homes built before 1996 This suggests the trend of an eroding building code reversed after
Hurricane Andrew Applied Research Associates (2008) investigated policy level claim data from
eight different insurance companies following the 2004 and 2005 hurricane seasons and found
similar results with post-2002 homes showing significant loss reduction results compared to pre-
2002 homes They further found that overall losses were reduced in year built from the mid-1990s
onward Although only indirectly associated with actual damages incurred stronger building
codes reduced post-storm federal disaster spending in 795 unique Florida ZIP codes impacted at
least once by the 2004 hurricanes of Charley Frances Ivan and Jeanne as well as Tropical Storm
Bonnie (Deryugina 2013) However contrary to these results Dehring and Halek (2013) find that
for the 264 residential properties in a coastal building zone in Lee County following Hurricane
Charley there is no evidence of less damage for homes built after the revised 1992 Florida building
code
Our study advances this building code literature in several important ways First we
collect annualized private market insured policy and loss data (number of claims and total damages
for all represented earned house years in the insured portfolio) from the Insurance Services Office
(ISO) aggregated at the ZIP code level for all Florida ZIP codes spanning the years 2001 to 2010
inclusive We are therefore able to analyze a decade of data post-FBC implementation ISO
industry data represents a significant percent of total private propertycasualty insurance annual
market share in FLiii and we utilize aggregated policy data in any one year ranging from 669000
6
to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number
space and time ndash insured loss and premium data for this analysis than previous studies Lastly
Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and
2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond
2004 and 2005
Finally following from our claim and loss analysis we perform a BCA on the
implementation of the FBC Our BCA is unique in that we use actual loss data rather than
probabilistic estimates of future loss as previous studies have and our loss data spans a longer time
period of 10 years in order to control for the effect of post FBC construction
III Florida Windstorm Losses and Associated Data
We quantify historical Florida wind event loss reductions due to the implemented FBC
through an econometric driven loss methodology that systematically accounts for relevant wind
hazard exposure and vulnerability characteristics evolving over time from the adoption of the
new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade
of construction In addition to insured loss data we have several variables from ISO collected by
insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and
represents the number of policyholders in each ZIP code Premiums is the total annual premiums
collected and BrickMasonry is the percent of homes that have exterior cladding made from brick
or other masonry products
Florida Insured Loss Data
For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data
from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in
any one year ranging from 669000 to just over 1 million insured policyholders representing
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
6
to just over 1 million insured policyholders Thus we utilize more comprehensive ndash in number
space and time ndash insured loss and premium data for this analysis than previous studies Lastly
Florida was affected by 18 tropical cyclones over the period 2001-2010 not just those in 2004 and
2005 and our study utilizes a more comprehensive set of extreme wind events extending beyond
2004 and 2005
Finally following from our claim and loss analysis we perform a BCA on the
implementation of the FBC Our BCA is unique in that we use actual loss data rather than
probabilistic estimates of future loss as previous studies have and our loss data spans a longer time
period of 10 years in order to control for the effect of post FBC construction
III Florida Windstorm Losses and Associated Data
We quantify historical Florida wind event loss reductions due to the implemented FBC
through an econometric driven loss methodology that systematically accounts for relevant wind
hazard exposure and vulnerability characteristics evolving over time from the adoption of the
new uniform codes ISO provided annual insured loss data aggregated at the ZIP code by decade
of construction In addition to insured loss data we have several variables from ISO collected by
insurers EHY Premiums and BrickMasonry EHY is an acronym for earned house years and
represents the number of policyholders in each ZIP code Premiums is the total annual premiums
collected and BrickMasonry is the percent of homes that have exterior cladding made from brick
or other masonry products
Florida Insured Loss Data
For the years 2001 to 2010 we obtained Florida propertycasualty insurance industry data
from ISO aggregated at the ZIP code Again the ISO industry data has aggregated policy data in
any one year ranging from 669000 to just over 1 million insured policyholders representing
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
7
125 of all residential structures in Floridaiv A total of $8023 billion (2010 inflation adjusted)
of property losses was incurred over this time (net of deductibles) from 593663 total property loss
claims incurred From 2001 to 2010 windstorm hazards are the largest cause of loss in Florida
totaling $5178 billion in losses (65 percent of total hazard damage) as well as being the most
frequent source of a loss claim with 317005 claims incurred (53 percent of total hazard claims
incurred) Clearly windstorm is a significant source of losses for Florida property insurers and
owners
Of course Florida windstorm losses vary over time and as expected are significantly
linked to the occurrence of hurricanes Table 1 provides a further detailed view of the ISO Florida
windstorm incurred losses and claims over time Across all years an average of $517 million in
losses and 31701 claims are incurred each year with an average windstorm claim being $10089
incurred at the rate of 324 claims per 1000 insured exposures (earned house years) However
excluding the significant hurricane years of 2004 and 2005 an average of $25 million in losses
and 3900 claims are incurred each year with an average windstorm claim of $8353 per claim
incurred at the rate of 48 claims per 1000 insured exposures (earned house years) Although
windstorm losses and claims are considerably higher in significant hurricane years they are still a
substantial annual property risk For example 2007 had average windstorm claims of $25399 per
claim and 2001 had 131 windstorm claims per 1000 insured ndash both outside the significant
hurricane years of 2004 and 2005 Lastly average annual premiums collected over this timeframe
(data not shown) are just over $1 billion per year Although these premiums are sufficient to cover
incurred loss amounts in non-hurricane years major windstorm year loss amounts (for example
2004 windstorm losses are nearly 4 times higher than annual average premiums collected) indicate
the critical role of further windstorm risk reduction measures in Florida
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
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Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
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Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
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Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
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Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
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Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
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Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
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httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
8
Insert Table 1 Here
One further split of the ISO loss data obtained is by decade of construction That is for
each year of ISO data from 2001 to 2010 each Florida ZIP code in that year contains a split of the
losses claims premiums and earned house years by the year of construction decade beginning in
1900 up to 2010 Given the loss timeframe of the ISO data from 2001 to 2010 in any one year
the majority of the overall ISO portfolio (ie proportion of earned house years EHY) is
represented by properties built prior to the year 2000 However given the growth of new
construction in Florida during this decade over time newer construction practices make up a more
significant portion of the ISO portfolio (Figure 1)v For example in 2001 post-2000 year of
construction (YOC) properties are less than 10 percent of the total ISO portfolio of 869645 total
EHYs but by 2010 they represent over 30 percent of the total ISO portfolio of 669770 total EHYs
And it is these newer housing units (ie primarily the post-2000 YOC properties) to which the
statewide FBC would have the most effect given its full implementation in 2002
Insert Figure 1 Here
Therefore as would be expected given the significant absolute portion of the EHY being
from pre-2000 YOC properties the majority of the 317005 total wind related claims and
associated $5178 billion in total wind-related losses (approximately 86 percent each) in identified
ZIP codes are incurred by properties that were built prior to the year 2000 But more importantly
the raw loss data on the numbers of claims and losses when normalized for the EHYs per YOC are
also higher on average for properties built prior to the year 2000 (Table 2) That is normalizing
for the number of policyholders in each YOC category (which again are significantly higher in
pre-2000 YOC as per Table 2) pre-2000 YOC buildings have a higher rate of claims incurred as
well as higher average incurred losses per each claim For example in 2004 206 percent of pre-
2000 YOC insured policyholders incurred a claim with an average loss of $3605 across all pre-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
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Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
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Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
9
2000 YOC policyholdersvi This compares to 104 percent of post-2000 YOC insured
policyholders incurring a claim with an average loss of $1211 across all post-2000 YOC
policyholders Although this is true for the normalized raw loss data a number of other hazard
exposure and vulnerability factors need to be controlled for to ascertain that post-2000 YOC losses
are indeed lower than pre-2000 construction
Insert Table 2 Here
Outcome Variable
Our dependent variable is aggregate loss for each ZIP code by year (2001-2010) and by
decade of construction In total we have 69442 observations We transform this variable by
taking the natural log While we do not have individual customer data we do have the number of
insured customers (EHY) for each ZIPyeardecade of construction that we include as an
explanatory variable to control for the differences between ZIPyeardecade of construction
observations with high numbers of insured customers versus those with lower numbers
Treatment Variable
To test for the effect of homes built after the introduction of the statewide building code
we construct a dummy variablecedil Post FBC for observations that are after 2000 By using this
dummy variable we can test the effect on losses for homes built after the statewide code was
implemented The dummy variable for Post FBC construction is related to structure age but does
not capture the separate effect age may have on loss So we add structure age into the regression
We only have data on structure age by decade which goes back to 1900 To introduce some
variability to this variable we calculate age by taking the difference between the year of loss and
the first year in the decade for the observation So for an observation that is for year 2004 where
the decade of construction was 1950-1959 age would equal 54 2004-1950 We turn now to the
other data
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
10
Wind Hazard Data
Florida was affected by 18 tropical cyclones over the period 2001-2010 Spatial wind
hazard data over Florida are sourced from the National Center for Environmental Predictionrsquos
(NCEP) North American Regional Reanalysis (NARR 2015 Mesinger et al 2006) NARR is a
dynamically consistent historical climate dataset based on historical climate observations Data are
available 3-hourly on a 32km grid Of importance to this study Mesinger et al (2006) showed that
the winds just above the surface compare well with surface station observations The 32-km grid
is too coarse to resolve high-impact small-scale features in the wind field such as thunderstorm
winds or tornadoes It is also too coarse to capture the intensity of the strongest hurricanes (as
discussed in Done et al 2015) Rather than downscaling the NARR data to obtain these small-
scale details using dynamical (eg Laprise et al 2008) or statistical (eg Tye et al 2014)
methods (that could introduce further uncertainties) we choose to sacrifice the small-scale details
of the wind field and peak hurricane intensity for location accuracy of the NARR data To account
for these missing wind extremes all wind speed values are normalized by the maximum value of
wind speed in the dataset
Specifically the 3-hourly wind data are interpolated from the 32-km grid to the ZIP-code
level and two wind field parameters are derived for use in the loss regressions the normalized
annual maximum wind speed and the annual number of times the wind speed exceeds the Florida
mean wind speed plus one standard deviation for at least 12 hours The choice of hazard variables
is based on recent work that highlighted the potential for wind parameters other than the maximum
wind to drive losses (Czajkowski and Done 2014 Zhai and Jiang 2014 Jain 2010)
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
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Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
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doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
11
Additional Data
We have 2000 and 2010 demographic data from the decennial census at the ZIP code level
for population area (in square miles) of the ZIP median household income and housing counts
Population growth across the decade is not even so we use building permits to help estimate
intervening years Each year is interpolated from decennial data for population and total housing
counts with an allocation factor based on the number of building permits for each ZIP and each
year Building permits are collected from census by place codes so we must re-allocate to ZIP
codes To convert from place to ZIP code we use allocation factors based on 2010 housing counts
provided by MABLE a service of the Missouri Census Data Center (MABLE 2015) For
example if a municipality has two ZIP codes with 60 of the homes in one and the remaining
40 in the other MABLE would use those percentages as the allocation factors from the
municipality to its corresponding ZIP codes In unincorporated areas we use allocation factors
from county to ZIP from the same service For median household income a straight-line
interpolation method is used adjusted for changes in the consumer price index (CPI-U) to 2010
CPI data are from the Bureau of Labor Statistics
Several factors were utilized to represent the overall geographic hazard risk of a ZIP code
The distance of the centroid of the ZIP to the coast was calculated to account for the overall
distance to the coast of each ZIP code Following Dehring and Halek (2013) dummy variables
that signifies whether a ZIP code contains a coastal construction control line (CCCL) were created
(1 equals CCCL in place) to account for stricter building codes in these areas Finally following
the 2005 hurricane season there was a significant increase in the number of policies underwritten
by Citizens the state-run wind-pool and insurer of last resort (Florida Catastrophic Storm Risk
Management Center 2013) Areas with large percentages of insured policies underwritten by
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
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Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
12
Citizens could represent inherently higher windstorm risk We spatially matched our Florida ZIP
codes to the Florida house districts and took the percentage of Citizens policies of the number of
occupied housing units as of December 31 2011 (Florida Catastrophic Storm Risk Management
Center 2013) Given the potential for adverse selection or offloading of high risk policies by the
private market in these areas it is unclear whether higher Citizensrsquo market penetration would lead
to a positive relationship with losses due to the higher risk or a negative relationship with private
losses as many of the bad risks have been transferred to the residual wind pool
IV Econometric Methodology
Better construction limits loss from windstorms through two channels first the direct effect
of decreasing loss on homes that experience damage and second through fewer claims on better
built homes Our data from ISO is aggregated at the ZIP codedecade of construction level So a
ZIP code where all homes experienced damage would have varying levels of damage between
homes built before and after implementation of the FBC Other ZIP codes may have damage for
older homes but little to no damage for homes built post FBC Our first challenge was to use
models that would provide an estimate of the full effect of the FBC lower levels of damage plus
the effect of fewer claims then an estimate for the direct effect alone To accomplish this we
employ two models The first includes all observations even if no claims have been filed and
second a hurdle model where the first stage models the probability of experiencing a loss and the
second stage isolates only the observations where a loss has been experienced
Base Model
The regression model is a semi-log ordinary least squares (OLS) fixed effects (time and
space) model with the natural log of loss as the dependent variable The base level of observation
is ZIP codeyeardecade of construction Explanatory variables include insurance information
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
13
(exposures and premiums) construction type demographic data based on the ZIP code measures
of the ZIP code hazard risk (how close to the coast the ZIP code is etc) and hazard data
concerning the wind speed and duration
Our test of the FBC creates a discontinuity that must be accounted for in the model All
observations with decade of construction post 2000 are considered under the new building code
regime But that dummy variable is a function of structure age so we employ a regression
discontinuity (RD) analysis to determine the best specification to estimate the effect of the FBC
allowing for the effect that structure age has on damage Intuitively structure age should increase
loss as older homes depreciate across their life making them more vulnerable to wind storms But
the effect of structure age is more than depreciation Over time construction practices and
materials used have changed which also affect how a structure responds to the stress of a violent
wind storm Indeed after Hurricane Andrew in 1992 it was noted that inferior construction
practices of the 1970rsquos and 1980rsquos had exacerbated the losses (Fronstin and Holtmann 1994 Keith
and Rose 1994)
This suggests that the effect of age is non-linear so a model that includes age as a
polynomial would be reasonable Determining the best specification requires testing a series of
models that include age as a polynomial andor interacted with our treatment variable Post FBC
(Lee and Lemieux 2010) (Jacob and Zhu 2012) The full analysis to choose our specification is
included in the Appendix The model that provided the best tradeoff between bias and precision
based on the AIC adds age and its square with the functional form
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
where the variable definitions are given in Table 3
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
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Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
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Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
14
Insert Table 3 Here
A positive sign is expected for both wind variables indicating that as wind speeds increase
andor the ZIP code is exposed to high winds for an extended period of time losses will increase
Post FBC construction should decrease loss so a negative sign is expected
Hurdle Model
One problem potentially encountered in attempting to model losses is there may be a
separate process occurring in the data that determines whether a loss is realized at all which could
affect the estimate of overall losses To address this issue hurdle models are used as they divide
the analysis into two stages We use a hurdle model to find the direct effect of the FBC The first
stage models the probability that a loss occurs and the second stage models the loss using only
observations that sustained a loss The dependent variable in the first stage equals one if there was
a loss and zero otherwise This binary dependent variable is then regressed against variables that
would affect the probability that a loss occurred Its form is
(2a)
Loss or No Loss = β0 + β1 Max Wind + β2 Wind Duration + β3 Population Density
+ β4 Post FBC
We expect that both wind variables max wind speed and duration as well as population
density will increase the probability of a loss Post FBC construction however should decrease
the probability of a loss
The second stage in the hurdle model is the same as Equation 1 with the exception that
only observations with a loss are included There are 19107 observations for the second stage and
its form is
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
15
(2b)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Model Validity
Regression models are limited by available data to understand how the dependent variable
varies as explanatory variables change If important variables are left out of the model some bias
can be expected This omitted variable bias is a common problem encountered with econometric
models Kuminoff et al 2010 found that one of the best approaches to reducing omitted variable
bias is to employ a spatial fixed effects model To accomplish this we use individual ZIP dummy
variables as a spatial fixed effect and dummy variables for each year in our data to control for
changes that may be related to time not otherwise controlled for within our co-variates These
dummy variables will contain all across-group variation leaving the remainder of the model to
contain the within-group variation (Greene 2003)
A second challenge to the validity of our model is another common problem
heteroscedasticity For Equation 1 we use clustered standard errors at the ZIP code through Proc
GLM in SAS Our hurdle model (Eq 2a and 2b) utilizes Proc Qlim which has a separate statement
(Hetero) that we invoked to model the error variance
V Regression Results
Our first regression (Equation 1) serves as a base from which we examine the effect of
basic explanatory variables on loss The results from this regression can be found in Regression
Table 4
Insert Table 4 Here
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
16
The performance of our regression model is satisfactory in terms of the performance of the
explanatory variables The goodness of fit measure adjusted R squared for our model is 046 and
the coefficient on our treatment variable Post FBC is -126 and highly significant
Overall our results show the strong effect the statewide FBC had on losses from wind
storms during this timeframe Using the results from the regression in Table 4 the coefficient on
the post 2000 dummy suggests that homes built since the year 2000 suffer 72 percent lower losses
than homes built prior to 2000 (Halvorsen and Palmquist 1980) This number is very close to the
results from a study conducted by the Insurance Institute for Business and Home Safety after
Hurricane Charley in 2004 (IBHS 2004) The IBHS study found that newer homes were 60
percent less likely to suffer damage at all and those that were damaged sustained 42 percent less
damage than older homes Our result of 72 percent lower damage reflects both those attributes as
our data included ZIP codeyearYOC observations that suffered damage as well as those that did
not
Our variables to measure the effect of wind hazard are wind speed and duration For both
variables we have a positive sign and each is highly significant Higher wind speed and higher
duration of high wind speeds increases damage and thus loss The remaining variables perform as
expected
Our second regression (Eq 2a and 2b) allow us to isolate the direct effect of the FBC In
the first stage variables such as Max Wind and Wind Duration significantly increase the
probability that the ZIP codeyearYOC observation suffered a loss The dummy variable for Post
FBC has a negative sign and is significant suggesting the probability of a loss is significantly lower
for homes built after new building codes were adopted In the second stage we see that our wind
variables continue to significantly increase the size of the loss and our treatment variable Post
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
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Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
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Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
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Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
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Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
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Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
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Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
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Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
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Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
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41
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on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
17
FBC dummy ndash continues to have a negative sign and is highly significant The coefficient is now
lower as only observations where a loss occurred are included In Table 4 for the Post 2000 dummy
we see that losses are reduced by about 47 as opposed to 72 when all observations are
includedvii These results confirm what IBHS found after Hurricane Charley suggesting that better
construction reduces loss in two ways First it lowers claims and reduces the amount of a loss
when a claim is filedviii
Model Evaluation
To evaluate our model we used the second stage of the hurdle models and broke our data
into two groups The first group represents 90 of the data randomly selected and was used to
run the model and collect parameter estimates The second group is an out of sample control group
to test the validity of the model Parameter estimates from the first group are applied to the control
group which gave us a predicted loss for each observation in the control group that can be
compared to the actual loss for each observation in the control group We then regressed the
predicted loss from the control group against the actual loss
Insert Figure 2 Here
Figure 2 plots the predicted loss against the actual loss and provides the fitted line with
95 confidence limits The adjusted R Squared for the regression is 4603 Our model appears
to do a good job of predicting most losses
Robustness of Table 4 Base Model Results
To test the robustness of our results we run three separate analyses 1) We first run a
regression with few co-variates 2) As wind design speeds have been used as a proxy for building
code strength (Deryugina 2013) we explicitly include this in our annualized windstorm loss
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
18
analyses and 3) We narrow the focus to windstorm losses from the seven hurricanes striking
Florida in 2004 and 2005
Regressions using Few Co-Variates
Additional relevant co-variates add precision to a model But the value of the focus
variable should be apparent with a smaller set So we ran a model with only insured customer
based variables EHY and paid premiums leaving out all other demographic and hazard related
variables Table 5 shows the result Our Post FBC treatment dummy retains its magnitude and
significance
Regressions Using Design Speed
The referenced standard for wind loads in the FBC is ASCESEI 7 Minimum Design Loads
for Buildings and Other Structures published by the American Society of Civil Engineers and the
Structural Engineering Institute ASCESEI 7 provides maps of appropriate reference wind speeds
for most regions of the United States and their territories These reference wind speeds are used in
calculations to determine design wind pressures for the primary structure of a building and the
cladding and components attached to a building These calculations take into account the building
geometry the importance of a building the exposuresurrounding terrain and other parameters that
influence the magnitude of the design wind pressure Deryugina (2013) utilized wind design
speeds as a proxy for building code strength and we similarly add this as an additional control in
our analysis Given the timeframe of our analysis from 2001 to 2010 ASCE 7-2010 wind maps
were provided by the Applied Technology Council (ATC) Although this version of the wind
speed map was not utilized during the period under consideration the relative values in general
between two locations would be the sameix
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
19
We received the ASCE 7-2010 maps and associated wind speed design data in GIS gridded
form from the ATC and spatially joined the values to our Florida ZIP codes We then further
categorized these mean ZIP code values into design speeds equating to Category 3 and below Cat
4 and Cat 5 hurricane levels
Insert Table 5 Here
The regression adds two dummy variables first for ZIP codes whose design speed exceeds
the wind sufficient for a Category 4 hurricane and second for ZIP codes whose design speed
reaches a Category 5 hurricane The omitted category is all other ZIP codes The dummy variables
for both a Cat 4 and Cat 5 design speed is negative but do not attain significance suggesting that
communities in higher wind zones may take further measures in local codes However the effect
is not significant Notably our variable for Post FBC construction maintains its negative sign
magnitude and significance
Regressions Limited to 2004 and 2005
Our next regression also shown in Table 5 is limited to observations that occurred during
the high hurricane years of 2004 and 2005 Florida had seven hurricanes in the years 2004 and
2005 with four of these hurricanes being Cat 3 or higher on the Saffir-Simpson scale Not
surprisingly the magnitude on wind speed increases while maintaining its significance and the
magnitude on age does the same But the effect of the FBC remains the same a 72 reduction
Summary of Results on the FBC
We have collected a comprehensive set of data on insured paid losses from 2001 to 2010
windstorms in Florida to assess the effectiveness of the FBC Using a Regression Discontinuity
model we estimate that the direct effect of the FBC is a 47 reduction in loss When the value of
the reduced claims are factored in we estimate that the full effect of the FBC is a 72 reduction
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
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Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
20
in loss Now we transition to evaluating the benefits of the FBC (lower losses) to its cost to
determine if the policy is one that is cost effective
VI Benefit and Costs of the FBC
Although the reduction in windstorm damages due to enhanced codes has been demonstrated in a
number of cases the economic effectiveness of the improved building codes has not been as well
documented especially from a statewide implementation perspective The multi-hazard
mitigation council report on savings from natural hazard mitigation activities (MMC 2005 Rose
et al 2007) concluded that a benefit-cost ratio of 4 (ie four dollars saved for every one dollar
spent) was appropriate for process activity grant spending related to improved building codes
However this information was gathered from a limited number of studies (mainly earthquake
oriented) so the range of a possible benefit-cost ratio is likely large reflecting the uncertainty in
generating it and the ratio provided due to improvement would not be the same as those for
adoption of building codes (MMC 2005 Rose et al 2007) Englehardt and Peng (1996) conducted
an economic assessment on adopted revisions in the 1990s to the South Florida Building Code for
ten related counties and determined that the net present value of the revisions was $7 billion or
benefit-cost ratio greater than 1 Importantly though this study did not have access to actual
building code damage reduction data to utilize in the analysis In 2002 Applied Research
Associates conducted a benefit-cost comparison study stemming from the enactment of the FBC
for three related housing types (ARA 2002) Loss reductions were estimated by evaluating how
the three types of FBC built houses would perform in probabilistic hurricane scenarios compared
to the same houses built under the previous code Given the probabilistic nature of the analysis
average annual losses were generated that demonstrated post-FBC housing having loss reductions
54 percent less on average (ranged from 26 percent to 61 percent less) These loss reductions were
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
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Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
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Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
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Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
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Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
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Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
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2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
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Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
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httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
21
then compared to their estimated cost impacts of the FBC for these housing types with at least
break-even BCA results (= 1) determined in the less hurricane intensive areas of the state and
above break-even BCA results (gt 1) for the more high risk hurricane areas Finally Torkian et al
(2014) conducted wind mitigation BCA on a synthetic portfolio of existing housing types with loss
reductions here also generated through probabilistic hurricane scenarios Benefit-cost ratio results
ranged from 041 to 183 for the retrofit mitigation activities to existing housing
We propose a BCA that differs from earlier work in several important ways First we use
realized loss data rather than estimates or probabilistic generated estimates of loss to estimate of
how much loss can be reduced by the FBC Second our loss data spans 10 years which include a
combination of major hurricanes and smaller wind storms
BenefitCost Methodology
The elements of a BCA requires three inputs 1) an estimate of the added cost to implement
the FBC 2) an estimate of the average annual loss (AAL) suffered by the state from wind related
storms from our realized ISO loss data and then from a statewide catastrophe model estimate and
3) the percentage of expected loss that will be mitigated due to implementation of the FBC We
first conduct a BCA using only the direct reduction in loss Next we conduct the same analysis
but use the full reduction in loss which includes the value of reduced claims Finally our ISO data
is paid losses and does not include deductibles so we add an estimate for deductibles
Additional Cost
In their 2002 benefit-cost comparison study of the enactment of the FBC for three related
housing types three actual sample homes were built to the FBC to evaluate the change in
construction costs (ARA 2002) For the purposes of code implementation the state was divided
into two main zones ndash a wind borne debris region (WBDR)x and a non-wind borne debris region
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
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Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
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Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
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Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
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Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
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41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
22
(N-WBDR) The homes used for the ARA 2002 study were in different parts of the state to account
for cost differences between the two regions
In the WBDR an added requirement is impact protection to windows and doors to reduce
damage from flying debris Along the coast and much of South Florida is classified as the WBDR
The N-WBDR is mainly classified in the interior of the state where impact protection is not
required Importantly the study provided a range of added costs for the N-WBDR and the WBDR
Three counties in South Florida Dade Broward and Monroe were under the South Florida
Building Code (SFBC) prior to the implementation of the FBC According to the ARA study
(2002) the FBC added no incremental cost to homes in those countiesxi Table 6 shows the ranges
of incremental cost per square foot for the N-WBDR and WBDR along with the percent of
residential units that reside in each area This allows a calculation of a weighted average added
cost Our weighted average of $137 is in 2002 dollars so we adjust to 2010 giving an average cost
per square foot of $166 The cost compares favorably with a similar building code enhancement
adopted by the City of Moore OK in 2014 after its third violent tornado in 14 years occurred in
2013 Consulting engineers and the Moore Association of Homebuilders estimated the code
enhancements cost $1 per square foot (Simmons et al 2015) The average home size in Florida is
1960 square feet which means that on average the FBC increases construction cost by $3254 per
structurexii
Insert Table 6 Here
Benefit of the FBC
Benefits stemming from the FBC are the expected reduction in losses from windstorms during
the life of the home We first find an average annual loss (AAL) use that number to estimate
losses for the next 50 years and then find the present value of those losses in 2010 Here we are
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
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Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
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Dixon R (2009) Florida Building Commission Presentation Available at -
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0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
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Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
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Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
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Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
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Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
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Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
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Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
23
assuming that wind hazard over the period 2001-2010 is representative of wind hazard over the
next 50 years A wealth of literature suggests the potential for changes to hurricane activity over
the next 50 years (see Walsh et al 2016 for a recent summary) but owing to the large uncertainty
on future changes in wind hazard on the scale of a single state we choose to assume a stationary
climate
Total loss from our ISO data is $5178 billion in 2010 dollars $479 billion is from homes
built prior to 2000 Our straightforward AAL then is $479 billion divided by the 10 years in our
data We use the EHY in 2005 for our sample of 1029461 to calculate a per structure AAL of
$466 From this $466 AAL with an inflation rate of 2 a discount rate of 225 (10 Year
Treasury) and an expected life of the home of 50 years we get a 2010 present value of future losses
per structure of $21474
Finally we use parameter estimates from our regression for the Post FBC dummy variable
(Table 4) to get an estimate of how much AALs would be reduced if homes are built to the FBC
The second stage of our hurdle model (Table 4) estimates that homes built under the FBC (Post
FBC) suffer 47 lower losses than homes built prior to 2000 everything else being equal or what
would be a reduction of $10093 from the projected $21474 in future losses
Insert Table 7 Here
BenefitCost Analysis
Comparing this $10093 in benefits versus the added $3254 in costs gives a benefit-cost ratio
of 310 for the FBC (Table 8) That is for every dollar spent on the implementation of the
statewide FBC 310 dollars are saved in the form of reduced windstorm losses or what is an
economically effective public policy following from our ISO loss data and results
Insert Table 8 Here
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Benefit Comparison Study
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Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
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Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
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Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
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Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
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Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
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FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
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Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
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Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
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Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
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Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
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Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
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httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
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Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
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Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
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Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
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Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
24
Albeit our ISO loss data is actual realized insured windstorm loss data it is only for ten years
This relatively short timeframe makes it difficult to truly approximate an AAL as would be
provided from a probabilistically based catastrophe model that generates an AAL from thousands
of future model year runs Hamid et al (2011) utilize a catastrophe model designed for the state
of Florida to estimate an average annual wind loss for all residential properties in Florida of
approximately $3 billion net of deductibles paid (When paid deductibles are included the AAL
estimate is approximately $5 billion) Adjusted to 2010 it becomes $3156 billion ($526 billion
with deductibles) Using this aggregate AAL and the number of residential units in Florida based
on the 2010 Census we calculate a per structure AAL of $356 The 2010 present value of losses
net of deductibles using an inflation rate of 2 a discount rate of 225 (10 Year Treasury) and
an expected life of the home of 50 years is $16405 Using the same reduced loss percentage as
before derived from our regression results 47 we find $7710 of reduced loss from the projected
$16405 in future losses due to the FBC Now comparing this $7710 benefit versus the added
$3254 in cost results in a benefit-cost ratio of 237 (Table 8) Again an economically effective
building code public policy
We run two additional analyses on our BCA results Our estimate of expected loss
reduction comes from the second stage of the hurdle model This is an estimate of the direct loss
reduction based on zip codeYOC observations that suffered a loss But the FBC also reduces the
number of claims as noted by IBHS in their study after Hurricane Charley Indeed Table 4 suggests
as much with a parameter estimate on the Post 2000 dummy of a 72 reduction in loss which
includes the reduced magnitude of loss from affected homes and the reduction in claims for Post
FBC homes When that level of loss reduction is used the BCA ratios show a sharp increase (Table
8) However a 72 loss reduction seems too dramatic an expectation when planning so far in
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
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Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
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Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
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Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
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Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
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Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
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Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
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on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
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pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
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Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
25
advance For that reason we offer a third level of expected loss reduction of 60 which is the
midpoint between our two loss reduction estimates This estimate captures the expected direct loss
reduction suggested by the second stage of our hurdle model but still recognizes that in some areas
the number of claims is reduced by the FBC This appears to be a reasonable assumption and
provides a BCA ratio of 396 for the ISO sample and 302 for all residential
The ISO data are net of deductibles so our BCA thus far only includes losses compensated by
the insurance industry The catastrophe model that provided our annual loss estimate of $3 billion
also provided an estimate when deductibles are included of $5 billion in 2007 dollars Using the
ratio of $5 billion to $3 billion we create a factor to modify losses in our BCA to account for all
loss from windstorms Table 8 shows the new estimate for BCA ratios and shows a range of BCA
values from a low of 237 to a high of 793
Payback of the FBC
Finally we use our BCA results to calculate a payback period for the investment of stronger
codes To convert our BCA ratio to a payback period we simply divide our 50-year planning
horizon by the BCA ratio So for the example of all residential assuming a 60 reduction in loss
and including deductibles the BCA ratio of 505 translates to a payback of just under 10 years
This is important for gauging potential political support or non-support for enactment of the new
codes Payback periods that approach the typical mortgage term 30 years would in theory be
difficult to achieve and that is not what our analysis indicates for the FBC
VI - Concluding Comments
In the aftermath of Hurricane Andrew which had exposed not only poor building
construction but also poor building code enforcement the state of Florida enacted statewide
building code changes that wrested away building code adoption control from individual localities
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
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Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
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Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
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Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
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Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
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2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
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41
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httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
26
With full implementation of the statewide building code associated expectations are that
windstorm losses from extreme events such as hurricanes should be reduced moving forward
There have been a few studies confirming these expectations following the 2004 and 2005
hurricane season In this article we further verify and quantify these findings and expand the
existing building code risk reduction research in several important ways
Overall we empirically test the statewide implementation of a building code in reducing
wind related damages in Florida controlling for other relevant wind hazard exposure and
vulnerability characteristics from a traditional risk assessment perspective Our results show the
strong effect the statewide FBC had on losses from wind storms during this timeframe From the
treatment variable that measures implementation of the statewide codes the post 2000 year of
construction losses are shown to be reduced by as much as 72 percent consistent with other
previous findings
Finally we have conducted a BCA of the FBC to determine if expected benefits exceed
the cost of implementation Using a direct estimate for mitigated losses and an estimate that
includes both direct and indirect mitigated losses our BCA suggests that the FBC is good public
policy from an economic perspective This result is close to that recommended by the multi-hazard
mitigation council of a 4 to 1 BCA It also expands on the ARA 2002 BCA result by providing a
statewide BCA Importantly this information is essential in generating political and consumer
support for such building code public policy implementation
For example the economic effectiveness results shown here have implications for ongoing
policy discussions about reforming building codes from a national US perspective Moore OK
independently adopted enhanced building codes after its third violent tornado in 14 years killed 24
including 7 children at Plaza Towers Elementary School in 2013 (Simmons et al 2015)
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
27
Construction practices in North Texas were brought under scrutiny after the December 2015
tornado revealed inadequate construction including an elementary school whose exterior walls
failed at wind speeds less than 90 mph (Thompson 2015) In May 2016 the White House
announced initiatives to increase community resilience with building codes as a major component
of that effortxiii Two pending congressional bills the Safe Building Code Incentive Act HR 1748
and the Disaster Savings and Resilient Construction Act HR 3397 provide incentives for better
construction HR 1748 incentivizes states to adopt enhanced statewide codes and HR 3397
would provide tax credits for owners andor contractors who use techniques designed for resiliency
in federally declared disaster areas (Vaughn and Turner 2014 Johnson 2014) Finally one
recommendation from the 2012 Presidentrsquos Hurricane Sandy Rebuilding Task Force is to
encourage states to use current building codes (Vaughn and Turner 2014)
Future research in the BCA of the FBC will further inform the public policy debate on
enhanced building codes The issue has national implications as other states find that wind hazards
impact them as well We have sufficient wind data to examine how the BCA performs under
different wind hazards Additionally it will be important to consider how future economic
development affects the BCA as well as varying climate change scenarios As the FBC is
mandatory for all new construction a statewide analysis was appropriate But individual
homeowners in older homes can invest in the retrofit of their home and qualify for discounts on
their homeowners insurance This topic is deserving of a robust analysis Although our BCA is
statewide regions within the state will likely have a spectrum of results For instance the ARA
2002 study suggested that the BCA in the WBDR would be higher than the N-WBDR Their
analysis did not use realized loss data so confirmation of how the BCA varies between those
regions would be an important contribution Finally our sensitivity analysis was limited to two
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
28
variables reduction in future loss and the inclusion of deductibles Additional work will highlight
other variables that could modify the results
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
29
Appendix
We use this appendix to conduct more detailed analysis on several topics First selection
of the model specification using a regression discontinuity approach Second we provide an in
depth examination of the relationship between structure age and losses Third we perform a
Balance Test to see if our co-variates are similar on both sides of our cut point Then we try an
alternative specification to see if our RD results are similar followed by regressions to examine
the year to year consistency of our Post FBC result Next we run a regression on claims to verify
the difference between our direct reduction result and our full reduction result Finally we perform
a regression on homes built to the SFBC which had adopted enhanced building codes in advance
of the FBC to assess the effect of earlier adoption of enhanced construction
Regression Discontinuity
Regression Discontinuity (RD) applies when an observation receives a treatment in our case
homes built under the FBC based on a rating variable in our case age of the structure at the year
of observation So for observations in 2005 homes built post 2000 received the treatment
adherence to the FBC but homes built during the 1980rsquos would not Our interest is to identify
how observations on either side of the implementation of the FBC (2000) perform in suffering loss
from windstorms The treatment variable is a function of the age of the home and age affects loss
in ways not related to the FBC such as depreciation and differences in materials and construction
practices across time To account for both the effect of age on loss as well as the implementation
of the FBC we use a cut point of year 2000 since all observations post 2000 receive the treatment
The data we have from ISO is aggregated loss data by zip code and decade of construction So
we cannot get an annualized age To approach a true age we set the year built for each decade of
construction at the beginning of the decade then subtract that from the year of each observation to
get an approximate agexiv
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
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Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
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0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
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Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
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Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
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httpwwwclaimsjournalcomnewsnational20140228245314htm
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Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
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Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
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Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
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Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
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Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
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Documentation Prepared for the Federal Emergency Management Agency of the US
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on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
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httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
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Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
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BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
30
To find the best specification we began with a simpler model which used a series of
categorical variables for each decade of construction to examine the effect of the code compared
to the omitted decade This method would approximate the changes in materials and construction
practices but was less effective in controlling for depreciation But it would give us a first
approximation of the code effect that we used as a benchmark when testing the best RD
specification Over 70 of the 2000 stock of residential structures in Florida were built after 1970
with more than 23 of those built from 1970- 1989 and under 13 built from 1990 to 1999 When
the omitted category is the 1990rsquos the effect of the code suggests a reduction in loss of 66 When
either the 1970rsquos or 1980rsquos are omitted the effect of the code suggests a reduction in loss of 81
A rough approximation of the codersquos effect from this approach would suggest a reduction in the
mid 70 percent range
Insert Table 1 ndash Appendix Here
Next we used a standard procedure with RD to search for the best way to include the rating
variable This process creates specifications that include age in increasing polynomials and
interacted with the treatment variable The goal is to find the specification with the lowest AIC
that comes close to the benchmark value of the treatment variable
Insert Tables 2 and 3 ndash Appendix Here
We did this first with regressions that limited the co-variates then with our full model In both
sets AIC reaches a minimum on the specification with age and age squared The interaction model
after that increases the AIC then the AIC goes down again with a cubed model and its interaction
model with the overall lowest AIC found on the cubed interaction model But we chose not to
use the cubed model or itrsquos interaction for two reasons First for the lower polynomial order
models the magnitude of the treatment variable in the models with just polynomials compared to
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
31
the corresponding interaction models were close with the interaction models providing a larger
magnitude When the cubed models were added the magnitude jumped where the polynomial
cubed model went down well below our benchmark and the interaction model went up above our
benchmark We felt this made use of the cubed model inappropriate So we now need to choose
between the squared model and the one with the interaction terms The squared model (Model 4)
had a lower AIC and the interaction variables on the interaction model (Model 5) were not
significant so we chose to use the squared model without the interaction term This model gave a
magnitude for the treatment variable of a 72 reduction somewhat lower than the expected
magnitude in the mid 70rsquos percent The general form of the model is
(1)
Natural log of losses = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Using Model 4 we then test the sensitivity of the coefficient of Post FBC by removing 1
of the observations on either end of our data sorted by loss Our treatment variable Post FBC
remains highly significant with a coefficient value of -117 which compares favorably to our
coefficient value of -126 when the entire sample is used
Structure Age and Wind Losses
Our study is similar to recent studies on the effect of energy efficiency building codes
adopted in the 1970rsquos in response to the oil price shocks of that decade The expectation was that
better insulation caulking and more efficient HVAC systems would result in lower energy
consumption But the change in energy consumption is less than engineering estimates projected
Jacobsen and Kotchen (2013) find a reduction of 4 for electricity and 6 for natural gas for
homes in Gainesville FL But Levinson (2015) points out that the Jacobsen and Kotchen study
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
32
may be confounding age with vintage and found a decrease in energy use related to the home
simply being new rather than the change in building code Indeed Kotchen (2015) revisited the
question with data 10 years older and found the effect on electricity had disappeared while the
reduction in natural gas use increased Something is occurring in energy use unrelated to the code
and could be explained by residents changing their use of energy as they adapt to their new home
Residents of an energy efficient home can undermine the intent of lower energy use by using the
efficient design to heat and cool their homes with a motivation toward increased comfort at the
same energy cost rather than energy savings Our study does not have the behavioral component
found in the case of energy efficiency In our application the construction elements that make the
structure able to withstand high winds are installed when the home is built and lie ldquobehind the
wallsrdquo making it unlikely for individual preferences to alter the homes performance against the
threat of wind stormsxv Our primary question becomes Is the improved performance of post FBC
homes due to the code or simply an artifact of new versus old construction when confronted with
a windstorm
To first address our analysis of age versus the FBC we rerun our base regression but limit
our observations to homes built in the 1990rsquos and post 2000 No home in this analysis is more
than 20 years old at the end of our analysis period of 2001-2010 and no home is older than 14
years during the highest loss year of 2004 Since this is a comparison between two adjacent
decades on either side of our cut point of year 2000 we remove age and age squared Results are
shown in Table 4-Appendix
Insert Table 4-Appendix Here
The coefficient on Post FBC is still negative highly significant with a magnitude very close to
what we saw with the entire database and the age variables This result suggests that the code
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
33
change did have an impact at least compared to homes built in the 1990rsquos Next we run a model
which tests for vintage effects This model has dummy variables for each decade omitting the
Post FBC dummy to examine how changing construction practices and materials across time have
impacted loss compared to Post FBC homes Pre 1950 decades are collapsed into 1 category
Results are also shown in Table 4-App Compared to the Post FBC construction the decades of
the 1970rsquos and 1980rsquos show the worst performance
Our final test on age compares loss by structure age and is found on Figure 1-App For
this graph we show how loss for similar aged homes varies by decade of construction where the
Post FBC era is shown in orange and the 1990rsquos in gray To get a sequential age between Post and
Pre FBC we calculate age at the end of the decade instead of the beginning as we have done till
now Instead of average loss we use the natural log of average loss in order to fit the graph Post
FBC and the 1990rsquos overlay but the Post FBC lies below indicating that for homes of similar ages
losses are lower for Post FBC In this way we illustrate how the loss performance for homes with
similar vintage and age compare with the only change being the code Consider the high point of
the gray line which is homes with an age of 5 years facing the hurricanes of 2004 and the high
point on the orange line which are Post FBC homes with an age of 4 years facing the same threat
The gray line hits a high of 829 or an average loss of $3983 compared to Post FBC homes with
a high of 707 or an average loss of $1176
Insert Figure 1-Appendix Here
Balance Test
To further test the reliability of our FBC result we perform a balance test on either side of
our cut point year 2000 First we do a simple test of two means on demographic features by ZIP
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
34
code before and after the year 2000 for several periods to see how time has altered the differences
Results are shown in Table 5-Appendix
Insert Table 5-Appendix Here
The table shows that there is little difference between the demographic characteristics of
the ZIP codes until you get to data prior to 1970 We then test the impact those differences may
have on our results by running a series of regressions using categorical dummy variables for
decades rather than including age as a separate variable Here there are 3 regressions the full
data 1900-2010 then 1970-2010 and 1980-2010 In each regression the 1990rsquos is omitted to
see how the FBC performance changes relative to the most recent decade between our full model
and recent time frames Those results are in Table 6-Appendix
Insert Table 6-Appendix Here
This analysis shows that differences in observations across time have little effect on our treatment
variable
Alternative Specification
Our reported models in Table 4 use structure age as an added variable in a specification
based on a discontinuity between age and our treatment variable Another way to approach this
would be to run a regression for the full model using decade dummies with the 1990rsquos omitted to
examine the effect of the FBC against the most recent decade Then run the same regression but
use our hurdle model to get the direct effect of the FBC Table 7-Appendix shows those results
Insert Table 7-Appendix Here
Using this specification to examine the effect of the FBC we get a 66 reduction in the full model
and a 45 reduction in the hurdle model Given that this result is only compared to the 1990rsquos
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
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httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
35
and not earlier decades with lower performance these results compare well to our results in the
models using structure age reported in Table 4
Year to Year Consistency of our Post FBC Result
As a final examination of our model we run regressions on each year separately to see how
the Post FBC variable changes from year to year While we do not have loss data prior to the
implementation of the FBC necessary to do a falsification test we can examine if the code lost its
significance or changed signs across the years of our study Also we approached this from the
reverse of a Post FBC effect by replacing the Post FBC dummy with the dummy variable
associated with the decade experiencing some of the worst results from wind storms the 1980rsquos
Insert Table 8-Appendix Here
Insert Table 9-Appendix Here
The Post FBC variable maintains its sign and significance in each of the ten years ranging
from a low during the high hurricane year of 2004 to a high in 2009 a low wind storm year When
we replace the Post FBC variable with the decade dummy for the 1980rsquos we see the expected
reverse effect posting positive and significant results across all ten years
Effect of the FBC on Claims
The main difference between the effect of the FBC between our full and hurdle model is
the full model includes all observations regardless of whether a claim has been filed and the second
stage of the hurdle model includes only observations that had a claim So we should be able to
test the difference in the coefficient on the FBC by running an analysis on claims To do this we
use the same equation as Equation 1 except that the dependent variable is not the natural log of
loss but claims Claims is an integer with the lowest possible value of zero and thus constitutes
count data Therefore we use a regression model appropriate for count data Further there is
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
36
evidence of overdispersion so rather than use a Poisson regression we employ a Negative
Binomial model with the form
(3)
Claims = β0 + β1EHY + β2Premium + β3BrickMasonry +
β4Income + β5Value + β6Unit Density + β7CCCL + β8Distance + β9Citizens
+ β10Max Wind + β11Wind Dur + β12Post FBC + β13Age + β14Age Squared
+ Vector of dummy variables for year + Vector of dummy variables for ZIP code
Table 10-Appendix reports the results
Insert Table 10-Appendix Here
Our treatment variable is negative highly significant and shows a reduction of 35 in claims due
to the FBC Assuming the average loss from an avoided claim would have been equal to average
losses from reported claims this result infers a full loss reduction of 72 from the direct loss
reduction of 47 There is enough variability with this assumption to question the apparent
precision in the estimate of full loss reduction to what our model suggests And we are not trying
to make a strong case for our ldquoback of the enveloperdquo result But the result does suggest that most
of the difference between our direct loss reduction estimate of the FBC and our full loss reduction
of the FBC can be explained by a reduction in claims for homes built to the FBC
SFBC Regressions
Three counties Dade Broward and Monroe adopted the South Florida Building Code as
early as the 1950rsquos with all 3 counties under the 1988 SFBC In 1994 the SFBC was upgraded to
include most of the provisions of the future 2001 FBC This would imply that ZIP codes in those
counties would have a more homogeneous stock of resilient housing providing a muted effect of
the FBC and a smaller difference between the direct and full effect of the FBC To test this we
ran our full regression and hurdle regression on observations that are in those counties alone This
reduces our observations from 69442 to 10001 Results are shown in Table 11-Appendix
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
37
Insert Table 11-Appendix Here
On the full regression the effect of the FBC is reduced from 72 statewide to 28 for these 3
counties On the second stage of the hurdle model we find that the effect of the FBC is reduced
from 47 statewide to 20 and this result does not attain significance These results suggest
that homes in Dade Broward and Monroe counties perform as expected if stronger construction
had been adopted prior to the FBC
38
References
Applied Research Associates Inc (2002) Florida Building Code Cost and Loss Reduction
Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
Available httpwwwfloircomsiteDocumentsARALossMitigationStudypdf
Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
Local Level through a Case Study Analysis Weather Climate and Society 6(2)202-217 2014
(DOI 101175WCAS-D-13-000241)
Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
high-impact weather and climate Lessons from a tropical cyclone perspective Climatic Change
doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
Insurance Market 2nd Annual Report Released January 2013 for the Florida Legislature
Available from
httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
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httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
38
References
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Benefit Comparison Study
Applied Research Associates Inc (2008) 2008 Florida Residential Wind Loss Mitigation Study
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Applied Technology Council (2015) Strategies to Encourage State and Local Adoption of
Disaster-Resistant Codes and Standards to Improve Resiliency Report prepared for the Federal
Emergency Management Agency ATC-117
Czajkowski J Done J 2014 As the Wind Blows Understanding Hurricane Damages at the
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Done JM Holland GJ Bruyegravere CL Leung LR and Suzuki-Parker A 2015 Modeling
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doi 101007s10584-013-0954-6
Dehring C A amp Halek M (2013) Coastal Building Codes and Hurricane Damage Land
Economics 89(4) 597-613
Deryugina T (2013) Reducing the Cost of Ex Post Bailouts with Ex Ante Regulation Evidence
from Building Codes Available at SSRN 2314665
Dixon R (2009) Florida Building Commission Presentation Available at -
httpwwwsbaflacommethodportalsmethodologyWindstormMitigationCommittee20092009
0917_DixonFLBldgCodepdf
Englehardt J D amp Peng C (1996) A Bayesian Benefit‐Risk Model Applied to the South
Florida Building Code Risk Analysis 16(1) 81-91
Florida Catastrophic Storm Risk Management Center 2013 The State of Floridarsquos Property
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httpwwwstormriskorgsitesdefaultfiles2nd20Annual20Insurance20Market20Rpt-
FSU20Storm20Risk20Centerpdf
Fronstin P AG Holtmann (1994) The Determinants of Residential Property Damage from
Hurricane Andrew Southern Economic Journal 61(2) 387-397 Oct
Greene William (2003) Econometric Analysis 5th Ed Prentice Hall Upper Saddle River NJ
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
39
Halvorsen Robert and Palmquist Raymond (1980) ldquoThe Interpretation of Dummy
Variables in Semilogarithmic Equationsrdquo American Economic Review Vol 70 No 3 June
1980 pp 474-475
Hamid Shahid S Jean-Paul Pinelli Shu-Ching Chen and Kurt Gurley Catastrophe model-
based assessment of hurricane risk and estimates of potential insured losses for the state of
Florida Natural Hazards Review 12 no 4 (2011) 171-176
Heckman J (1976) The Common Structure of Statistical Models of Truncation Sample
Selection and Limited Dependent Variables and a Simple Estimator for Such Models Annals of
Economic and Social Measurement 5 (4) 475-92
Heckman J (1979) Sample Selection as a Specification Error Econometrica 47 (1) 153-61
Insurance Institute for Business and Home Safety (IBHS) (2004) Hurricane Charley Executive
Summary Available at httpdisastersafetyorgwp-contentuploadshurricane_charleypdf
(last accessed February 10 2016)
Insurance Institute for Business and Home Safety (IBHS) (2015) New IBHS Report Rates
Building Codes in 18 Coastal States Available at httpsdisastersafetyorgibhs-news-
releasesnew-ibhs-report-rates-building-codes-18-coastal-states (last accessed February 10
2016)
Jacob Robin ZhucedilPei Somers Marie-Andree and Bloom Howard (2012) ldquoA Practical Guide
to Regression Discontinuityrdquo MDRC July 2012 Available online at
httpmdrcorgpublicationpractical-guide-regression-discontinuity
Jacobsen Grant D and Kotchen Matthew J (2013) ldquoAre Building codes Effective at Saving
Energy Evidence from Residential Billing Data in Floridardquo The Review of Economics and
Statistics Vol 95 No 1 pp 34-49 March 2013
Jain V Guin J and He H (2009) Statistical Analysis of 2004 and 2005 Hurricane Claims
Data Proceedings 11th American Conference on Wind Engineering
Jain V 2010 The role of wind duration in damage estimation AIR Currents 4 pp [Available
online at httpwwwair-worldwidecomPublicationsAIR-CurrentsattachmentsAIRCurrentsndash
The-Role-of-Wind-Duration-in-Damage-Estimation
Johnson Denise (2014) ldquoBetter Data is Key to Improved Building Codesrdquo Claims Journal
February 2014 Available at
httpwwwclaimsjournalcomnewsnational20140228245314htm
(last accessed February 12 2016)
Keith E J Rose (1994) Hurricane Andrew ndash Structural Performance of Buildings in South
Florida Journal of Performance of Constructed Facilities 8(3) 178-191
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
40
Kotchen Matthew J (2016) ldquoLonger-Run Evidence on Whether Building Energy Codes
Reduce Residential Energy Consumptionrdquo working paper June 2016
Kuminoff Nicolai V Parmeter Christopher F Pope Jaren C (2010) ldquoWhich Hedonic
Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental
Amenitiesrdquo Journal of Environmental Economics and Management Vol 60 No 3 November
2010
Kunreuther H Useem M (2010) Learning from Catastrophes Strategies for Reaction and
Response Upper SaddleRiver NJ Wharton School Publishing
Kunreuther H (2006) Disaster mitigation and insurance Learning from Katrina The Annals of
the American Academy of Political and Social Science604(1) 208-227
Laprise R R de Eliacutea D Caya S Biner P Lucas-Picher EP Diaconescu M Leduc A Alexandru
and L Separovic 2008 Challenging some tenets of regional climate modeling Meteorology and
Atmospheric Physics 100(1-4) 3-22
Lee David S and Lemieux Thomas (2010) ldquoRegression Discontinuity Designs in
Economicsrdquo Journal of Economic Literature Vol 48 pp 281-355 June 2010
Levinson Arik (2015) ldquoHow Much Energy Do Building Energy Codes Save Evidence from
Californiardquo working paper November 2015
Missouri Census Data Center MABLEGeocorr[90|2k|12] Version 12 Geographic
Correspondence Engine Web application accessed June 2015 at
httpmcdcmissourieduwebsasgeocorr[90|2k|12]html
McHale C Leurig S 2012 Stormy Future for US PropertyCasualty Insurers The Growing
Costs and Risks of Extreme Weather Events A Ceres Report
Mesinger F and CoAuthors 2006 North American Regional Reanalysis Bull Amer Meteor
Soc 87 343ndash360 doi httpdxdoiorg101175BAMS-87-3-343
Mills E Roth R Lecomte E 2005 Availability and Affordability of Insurance Under
Climate Change A Growing Challenge for the US A Ceres Report
Multihazard Mitigation Council (MMC) 2005 Natural Hazard Mitigation Saves Independent
Study to Assess the Future Benefits of Hazard Mitigation Activities Volume 2 ndash Study
Documentation Prepared for the Federal Emergency Management Agency of the US
Department of Homeland Security by the Applied Technology Council under contract to the
Multihazard Mitigation Council of the National Institute of Building Sciences Washington DC
NARR 2015 National Centers for Environmental PredictionNational Weather
ServiceNOAAUS Department of Commerce 2005 updated monthly NCEP North American
Regional Reanalysis (NARR) Research Data Archive at the National Center for Atmospheric
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
41
Research Computational and Information Systems Laboratory
httprdaucaredudatasetsds6080 Accessed May 22 2015
National Institute of Building Sciences (NIBS) 2015 Developing Pre-Disaster Resilience Based
on Public and Private Incentivization Multihazard Mitigation Council amp Council on Finance
Insurance and Real Estate Report Available at
httpscymcdncomsiteswwwnibsorgresourceresmgrMMCMMC_ResilienceIncentivesWP
pdf (accessed January 2016)
Rochman J 2015 Commentary Stronger Building Codes Make Communities More Resilient
Claims Journal Available at
httpwwwclaimsjournalcomnewsnational20150708264405htm
Rose A Porter K Dash N Bouabid J Huyck C Whitehead J Shaw D Eguchi R
Taylor C McLane T and Tobin LT 2007 Benefit-cost analysis of FEMA hazard mitigation
grants Natural hazards review 8(4) pp97-111
Simmons Kevin M Kovacs Paul Kopp Greg (2015) ldquoTornado Damage Mitigation
BenefitCost Analysis of Enhanced Building Codes in Oklahomardquo Weather Climate and Society
April 2015
Sparks P R S D Schiff and T A Reinhold 19921Wind Damage to Envelopes of Houses
and Consequent Insurance Losses Journal of Wind Engineering and Industrial Aerodynamics
53 (1 2) 45-55
Thompson Steve (2015) ldquoEngineer Finds Examples of ldquoHorrificrdquo Construction in Tornado
Wreckagerdquo Dallas Morning News December 30 2015
Tye MR DB Stephenson GJ Holland and RW Katz 2014 A Weibull Approach for Improving
Climate Model Projections of Tropical Cyclone Wind-Speed Distributions J Climate 27 6119ndash
6133
Vaughan E J Turner (2014) The Value and Impact of Building Codes Available
httpwwwcoalition4safetyorgtoolkithtml
Walsh KJE McBride JL Klotzbach PJ Balachandran S Camargo SJ Holland G
Knutson TR Kossin JP Lee TC Sobel A and Sugi M 2016 Tropical cyclones and
climate change WIREs Climate Change 7 65-89 doi101002wcc371
Zhai AR and JH Jiang 2014 Dependence of US hurricane economic loss on maximum wind
speed and storm size Environmental Research Letters 96 064019
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
42
Table 1 Windstorm Incurred Loss and Claim Detailed Overview by Year
Windstorm Windstorm Avg Wind Number of
Incurred Losses Incurred Loss Per Earned House claims per
Year (2010 Dollars) Claims Claim Years (EHY) 1000 EHY
2001 $ 41758462 11377 $ 3670 869645 131
2002 $ 13664281 3656 $ 3737 952238 38
2003 $ 12527758 3085 $ 4061 1024566 30
2004 $ 3715877513 207905 $ 17873 991491 2097
2005 $ 1261591875 77901 $ 16195 1029461 757
2006 $ 12217068 1479 $ 8260 739962 20
2007 $ 52296497 2059 $ 25399 711885 29
2008 $ 41420175 5860 $ 7068 685920 85
2009 $ 17681332 2297 $ 7698 694412 33
2010 $ 9604188 1386 $ 6929 669770 21
Averages all years $ 517863915 31701 $ 10089 836935 324
excluding 2004 amp 2005 $ 25146220 3900 $ 8353 793550 48
Table 2 Pre and Post 2000 Year of Construction number of claims and average loss amount normalized by the number of insured policyholders (earned house years)
Average Claims Per EHY Average Loss Per EHY
Year Pre2000 Post2000 Unclassified Pre2000 Post2000 Unclassified
2001 14 05 07 $ 45 $ 20 $ 29
2002 04 01 03 $ 14 $ 4 $ 11
2003 04 02 02 $ 10 $ 6 $ 10
2004 206 104 185 $ 3605 $ 1211 $ 2701
2005 82 37 71 $ 1116 $ 433 $ 841
2006 03 01 01 $ 26 $ 6 $ 4
2007 04 01 03 $ 40 $ 14 $ 19
2008 10 05 05 $ 73 $ 29 $ 18
2009 04 02 03 $ 29 $ 7 $ 21
2010 03 01 04 $ 18 $ 4 $ 17
Total 34 15 31 $ 512 $ 168 $ 405
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
43
Table 3 - Variable Definitions
Variable Description
Intercept
EHY Number of customers by ZIP decade of construction and by year
Premiums Natural log of total insurance premiums Adjusted to 2010 dollars
BrickMasonry The percent of brick and brickmasonry homes for the ZIP and year
Income Natural log of Median Household Income CPI adjusted to 2010
Population Density Population divided by the size of the ZIP code in miles by ZIP and year
Unit Density Number of residential structures divided by the size of the ZIP code in miles By ZIP and year
CCCL Equals 1 if the ZIP code has a construction control line
Distance Natural log of the mean distance in miles to the nearest coast
Citizens Percent of insurance customers using the state insurer Citizens
Max Wind Maximum wind speed
Wind Duration Number of times the wind speed exceeds the mean speed plus one standard deviation for 12 hours
Post FBC Equals 1 if the observation was for homes built in the 2000s
Age Year minus the beginning of the decade of construction
Age Sq Age Squared
Design CAT4 Equals 1 if the Design Wind Speed is for a Cat 4 hurricane
Design CAT5 Equals 1 if the Design Wind Speed is for a Cat 5 hurricane
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
44
Regression Table 4 ndash Base Models
Zip Code Fixed Effects Dummies Have Been Suppressed
Full Model Hurdle Model
Parameter Estimate Clustered
Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262515 003503 First
Max Wind 0156383 000266 Stage
Wind Duration 0042673 0007991
Population Density
-000498 0002246
Post FBC -018365 0016166
Obs 69442
AIC 149243 Intercept -8628364484 05503 -22117 0362308 Second
EHY 0035960515 00039 0002734 0000531 Stage
Premiums 0731719781 00269 0399849 0014034
BrickMasonry 0312210902 01413 0900063 0081676
Income -0214963136 0069 007255 0046157
Unit Density -0000084471 00002 -000052 0000159
CCCL 0092215125 00799 0187263 0051162
Distance 0168890828 0017 0079778 0010192
Citizens -1474742952 01257 -078342 0089688
Max Wind 0256278789 00179 0248594 0013452
Wind Duration 016693209 0042 0089406 0014077
Post FBC -126098448 00707 -063402 005657
Age 0015411882 00027 0011001 0002548
Age Sq -0002087834 00002 -000135 0000277
Obs 69442 19107
Adj R Squared 04643
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
45
Table 5 ndash Robustness Tests
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Prgt|t| Estimate Prgt|t|
Intercept -44716798 -8628364 -8662343632 -195375973
EHY 00397957 0035961 003592987 000414663
Premiums 06911625 073172 0732205042 155455262
BrickMasonry 0312211 0378693342 494600107
Income -0214963 -0206314713 -069789714
Unit Density -8447E-05 -0000056364 -0001762
CCCL 0092215 0089157134 -024189863
Distance 0168891 0166864719 021309203
Citizens -1474743 -1447225912 -304176511
Max Wind 0256279 0255880813 053083086
Wind Duration 0166932 016814728 000796048
Post FBC -12504886 -1260984 -1261543925 -127291057
Age 00162403 0015412 0015359444 004154843
Age Sq -00021287 -0002088 -0002084084 -000492109
Design CAT4 -0034039886
Design CAT5 -0076779624
Obs 69442 69442 69442 14149
Adj R Squared 04436 04643 04643 05255
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
46
Table 6 ndash Estimate of Incremental Cost per Square Foot for the FBC
Construction Costs Estimates (2002) Percent Low High Average of Total AvgPct
N-WBDR Cost 023 128 076 01745 0131748
WBDR-(lt140 mph) Cost 106 167 137 04206 0574119
WBDR-(gt140 mph) Cost 135 249 192 03445 066144
SFBC 000 000 000 00604 0
Weighted Avg $137
2010 CPI Adj $166
Table 7 ndash Per Unit Cost and Estimate of Future Reduced Losses from the FBC
Increased Unit ISO Cat Model Res Units Average Cost per SF Total AAL AAL 2005 for ISO Per PV Unit Size 2010 $ Cost 2010 $ 2010 $ 2010 Statewide Unit 50 Year
ISO Sample 1960 166 3254 479732808 1029461 466 21474
With Deductibles 1960 166 3254 801153789 1029461 778 35852
Statewide 1960 166 3254 3155658466 8863057 356 16405
With Deductibles 1960 166 3254 5269949638 8863057 594 27373
Table 8 ndash BenefitCost Ratios
Per Unit Cost
FBC Damage
Reduction 47
FBC Damage
Reduction 60
FBC Damage
Reduction 72
BCA 47 Reduction
BCA 60 Reduction
BCA 72 Reduction
ISO Sample 3254 10093 12884 15461 310 396 475
With Deductibles 3254 16850 21511 25813 518 661 793
All Florida 3254 7710 9843 11812 237 302 363
With Deductibles 3254 12865 16424 19709 395 505 606
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
47
Table 1-Appendix
1990s Omitted 1980s Omitted 1970s Omitted Full Model w Age
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept 0427553
-7942695 -7940978 -8628364
EHY 0036632 0036632 0036632 0035961
Premiums 0718244 0718244 0718244 073172
BrickMasonry 0310039 0310039 0310039 0312211
Income -0229136 -0229136 -0229136 -0214963
Unit Density -0000035524
-0000035524
-0000035524
-0000084471
CCCL 0099362 0099362 0099362 0092215
Distance 0168051 0168051 0168051 0168891
Citizens -1493938 -1493938 -1493938 -1474743
Max Wind 0256727 0256727 0256727 0256279
Wind Duration 0166741 0166741 0166741 0166932
Post FBC -1072119 -1682877 -1684593 -1260984
d_1990
-0610758 -0612475
d_1980 0610758
-0001716
d_1970 0612475 0001716
d_1960 0361577 -0249181 -0250897 d_1950 0247133 -0363625 -0365341
pre_1950 -0112074 -0722832 -0724548 Age
0015412
Age Sq
-0002088
AIC 231513 231513 231513 231659
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
48
Table 2 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4941993 -4031831 -4014147 -447168 -4469442 -48782 -4948988
EHY 0038023 0038803 0038696 0039796 0039764 0040197 0040506
Premiums 0753608 0694807 0694628 0691163 0691343 0676205 0675454
Post FBC -1361849 -1626774 -1753713 -1250489 -1258512 -088918 -1842633
Age
-0007237 -0007307 001624 0016124 0063445 0070627
Age Sq
-0002129 -0002119 -001207 -0013457
Age Cu
587E-05 6652E-05
Age_PostFBC 0022066
-0006069
1042536
Agesq_PostFBC
0009971
-2328348
Agecu_PostFBC
0140286
AIC 234499 234390 234389 234279 234283 234223 234177
Model1 uses Post FBC and no age variable
Model2 uses Post FBC and age
Model3 uses Post FBC age and age interacted with Post FBC
Model4 uses Post FBC age and age squared
Model5 uses Post FBC age age squared age interacted with Post FBC and age squared interacted with Post FBC
Model6 uses Post FBC age age squared and age cubed
Model7 uses Post FBC age age squared age cubed age interacted with Post FBC age squared interacted with Post FBC and age cubed interacted with Post FBC
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
49
Table 3 ndash Appendix
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -9070937 -8210025 -8193408 -8628364 -8619397 -901818 -9049127
EHY 003424 0034993 003485 0035961 0035889 0036392 0036671
Premiums 079838 0735049 0734789 073172 0731823 0715873 0715426
BrickMasonry 0270444 0314964 0315876 0312211 031246 0314632 0312482
Income -0246229 -0214092 -0212574 -0214963 -0214518 -021978 -022407
Unit Density -7935E-05
-586E-05
-5982E-05
-845E-05
-8468E-05
-58E-05
-551E-05
CCCL 012243
0096304 0095483 0092215 0091992 0089874 0091186
Distance 017593 0169206 0169129 0168891 0168879 0167171 016703
Citizens -1413834 -1484502 -148684 -1474743 -1475597 -149748 -149534
Max Wind 0254314 0256828 0256939 0256279 0256331 0256718 0256214
Wind Duration 0166736 016734 0167273 0166932 0166897 0166843 0167622
Post FBC -1351969 -1630266 -1793143 -1260984 -1314156 -088546 -1854842
Age
-0007626 -000772 0015412 0015125 0064521 007053
Age Sq
-0002088 -0002065 -001244 -0013596
AgeCu
612E-05 6766E-05
Age_PostFBC 0028294 0002751
1022203
Agesq_PostFBC 0008043
-2269315
Agecu_PostFBC
0136632
AIC 231894 231770 231766 231659 231662 231595 231552
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
50
Table 4-Appendix
1990-2010 Full Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -874176019 05339245 -9625571842 02663149 EHY 002607054 00010441 0036631987 00007388
Premiums 060710151 00219903 0718244372 00100833 BrickMasonry 034513022 01583532 0310039307 00775013
Income 020743174 00977143 -0229136499 00436795 Unit Density 75296E-05 00002243 -0000035524 00001131
CCCL -00250154 01001231 0099361591 00484053 Distance 014798526 00202934 0168051018 00096458 Citizens -19196541 01659296 -1493938439 00804653
Max Wind 025437545 00185181 0256726978 00087826 Wind Duration 021249002 00424434 016674127 00196152
pre_1950 0960045042 00475102
d_1950 1319252383 00508302
d_1960 1433696307 00489112
d_1970 1684593481 00482689
d_1980 1682877105 0048269
d_1990 1072118969 00483608
Post FBC -122639772 00520538
Obs 17906 69442
Adj R Squared 04675 04655
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
51
Table 5-Appendix
Balance Test
1990-2010
1980-2010
1970-2010
1960-2010
1950-2010
1900-2010
BrickMasonry
Mobile
Income
Home Value
Unit Density
CCCL
Distance
Citizens
Rejection of the Hypothesis that the means are equal α=1 α=05 α=01
Table 6-Appendix
Balance Test ndash Decade Dummies
1900-2010 1970-2010 1980-2010
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -8553452873 02672674 -9901426258 039651459 -9655445284 045117655
EHY 0036631987 00007388 0030035825 000090878 0029151754 000095153
Premiums 0718244372 00100833 0785129024 001657839 0716131662 001906194
BrickMasonry 0310039307 00775013 0058970119 011738471 019968841 013429122
Income -0229136499 00436795 -009497743 006848399 0045469518 008095252
Unit Density -0000035524 00001131 0000267179 000016345 0000142418 000019015
CCCL 0099361591 00484053 0131227752 007334551 005827059 008422606
Distance 0168051018 00096458 0201958747 001484979 0185190624 001705488
Citizens -1493938439 00804653 -1790777115 012116739 -1838920836 013911691
Max Wind 0256726978 00087826 0290175435 00136124 0281650907 001558713
Wind Duration 016674127 00196152 0142158091 003105491 0161508264 003564001
pre_1950 -0112073927 00506211
d_1950 0247133413 00526112
d_1960 0361577338 00500973
d_1970 0612474511 00488438 0575621494 005331684
d_1980 0610758136 00484157 0594048494 005276209 0584038738 005243286
d_1990
Post FBC -1072118969 00483608 -1063081791 0053084 -1121360186 005304279
Obs 69442 35507
26729
Adj R Squared 04654 04778 048
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
52
Table 7-Appendix
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -262563 0035028 First
Max_Wind 0156425 000266 Stage
Wind Duration 0042652 0007989
Population Density -000501 0002246
Post FBC -018364 0016165
Obs 69442
AIC 149188 Intercept -8553452873 02672674 -21211 0357286 Second
EHY 0036631987 00007388 0003094 0000536 Stage
Premiums 0718244372 00100833 0386122 0014285
BrickMasonry 0310039307 00775013 0910896 0081548
Income -0229136499 00436795 0082266 0046119
Unit Density -0000035524 00001131 -000047 0000159
CCCL 0099361591 00484053 018571 005109
Distance 0168051018 00096458 0078816 0010177
Citizens -1493938439 00804653 -080097 0089598
Max Wind 0256726978 00087826 0250276 0013364
Wind Duration 016674127 00196152 0089513 001408
Pre 1950 -0112073927 00506211 -003 0062288
d_1950 0247133413 00526112 0079901 005082
d_1960 0361577338 00500973 016725 0043534
d_1970 0612474511 00488438 0247777 0039334
d_1980 0610758136 00484157 0289707 0037518
d_1990
Post FBC -1072118969 00483608 -06069 0048224
Obs 69442 19107
Adj R Squared 04654
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
53
Table 8-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -635495138 -8405687667 -3539129011 -171104269 -223230383
EHY 0046815496 0049755016 0046129696 -000549296 001407418
Premiums 0902225848 0520713952 0541260712 177809555 137222708
BrickMasonry 0091248138 0330072379 0133757934 426634553 52989961
Income -0556333367 007673894 -0333566059 -139739466 -001458013
Unit Density -000273761 0000017044 -0000399979 -000624441 000229285
CCCL 0733445464 -010658834 -0002675423 -04079496 -003840961
Distance -0006196654 0146642336 0074095408 001597389 027645125
Citizens -1951200931 -1203063948 -0854092944 -69386833 118687859
Max Wind 0189251023 02218324 -0028507713 062796853 033670019
Wind Duration -1427984579 0146260971 -0051868908 -018543928 019449226
Post FBC -1330444966 -1047162316 -104366346 -075349788 -174200475
Age 0024430912 0007695322 0018112422 005900484 002379329
Age Sq -0002920973 -0000688191 -0001569489 -000686962 -000254583
Obs 7404 7315 7172 7138 7011
Adj R Squared 04659 03911 03599 06072 04469
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -3487562139 -551566428 -1144328556 -817527228 -283760759
EHY 0029382684 0038563888 004035125 0040966039 0038016928
Premiums 0410656129 0355927665 087161791 0526462478 02894645
BrickMasonry -1041361641 -1072412978 -226387428 -174495142 -090883283
Income -0098853673 -0243879192 029287347 0444050006 022370289
Unit Density 0000300877 0000720442 000118661 0001595448 0000576067
CCCL -027184702 0306014726 06309048 0038276012 -002689181
Distance 0081513275 0195383664 035499478 021933669 0163507604
Citizens -0752046762 -0675216291 -311490274 -029843341 -059537008
Max Wind 0055728801 0201000209 014525329 017495008 -001688058
Wind Duration -0136373896 -0262823057 -016752273 -048495762 0078483648
Post FBC -117688708 -1210585276 -09278322 -195314227 -135931859
Age 0006187693 001016534 001631759 -001596812 -002182466
Age Sq -0000687056 -000118586 -000152884 0000907348 000112213
Obs 6719 6643 6575 6570 6895
Adj R Squared 0197 02293 03667 02803 02262
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
54
Table 9-Appendix
2001 2002 2003 2004 2005
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -7539730029 -9359349643 -4540304325 -178548982 -2410304
EHY 0047913193 0050579977 0046738464 -000511538 001472664
Premiums 0933434158 0540773786 0554312075 178778901 139820098
BrickMasonry 0062679165 0311824421 0126642884 425909152 528054317
Income -0592068944 0052289191 -0345142584 -140252136 -002535229
Unit Density -0002758902 -0000013791 -0000418639 -000625241 000228385
CCCL 0743282837 -009598118 0007668609 -040039481 -002349054
Distance -0004011689 014827054 0076206078 001718914 028091933
Citizens -1907070056 -1172082185 -0822482861 -691994759 123979783
Max Wind 0186392922 0219937725 -0029594557 062788723 03369511
Wind Duration -1432201277 0147988806 -0051393555 -018652806 019290395
d_1980 0882524305 0733952915 0820252047 048813821 072461562
Age 005656422 0033984684 0045371148 007917294 007253832
Age Sq -0005096688 -0002462907 -0003413898 -000824514 -000600145
Obs 7404 7315 7172 7138 7011
Adj R Squared 04663 03917 03615 06072 04438
2006 2007 2008 2009 2010
Parameter Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t| Estimate Pr gt |t|
Intercept -4721292268 -6849651969 -1251120414 -104832245 -471317249
EHY 0029291524 0038176189 003980162 003937994 0036637581
Premiums 0430840225 0373067836 089118372 05725051 0338993543
BrickMasonry -1072673943 -1094867564 -228314292 -177512943 -095600629
Income -011246799 -0246875105 028804568 043007102 0198547424
Unit Density 0000308373 0000729796 000115155 000148608 0000544974
CCCL -0270035498 0310339493 06391466 00571657 -001174278
Distance 0084719416 0198463554 035735414 022474189 0168702153
Citizens -07322541 -0654042742 -309502993 -025940821 -05347339
Max Wind 0055528386 0201585869 014451638 017175987 -002013935
Wind Duration -0140314171 -0267021688 -016690919 -048869353 0079948523
d_1980 0483661036 0599607 028523853 045083377 0431080232
Age 0041843078 0047004839 004563723 004699644 0024861386
Age Sq -0003326568 -0003855416 -000367356 -000368329 -000213913
Obs 6719 6643 6575 6570 6895
Adj R Squared 01923 02258 03648 02663 02178
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
55
Table 10-Appendix
Claims Regression
Parameter Estimate Std Err Pr gt ChiSq
Intercept -125027 0189
EHY 00031 00004
Premiums 09238 00091
BrickMasonry 04034 00589
Income -04719 00319
Unit Density -00007 00001
CCCL 0049 00343
Distance 01448 00068
Citizens -10523 00567
Max Wind 01721 00056
Wind Duration -00017 00101
Post FBC -04247 00366
Age 00375 00018
Age Sq -00043 00002
Obs 69442
Pseudo R Squared 029
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
56
Table 11-Appendix
SFBC Hurdle Regression
Full Model Hurdle Model
Parameter Estimate Std Err Prgt|t| Estimate Std Err Prgt|t|
Intercept -38419 0110688 First
Max Wind 0249099 0008523 Stage
Wind Duration -017305 0034269
Pop Density -00021 0004094
Post FBC -026859 0045551
Obs 2201
AIC 17362
Intercept -642410358 07694634 -1735 1612104 Second
EHY 003777857 0001882 -000198 0001279 Stage
Premiums 058526953 00206452 0651733 0031265
BrickMasonry 051703099 03228598 -08406 0521577
Income -024791541 00885833 -024543 0119458
Unit Density -000024465 00001635 -000108 0000324
CCCL 004187952 01058532 0083285 0147401
Distance 013910546 00256963 007182 0035699
Citizens -105795182 01382522 -087333 0192635
Max Wind 009254137 00352015 0207402 0075261
Wind Duration -005698597 00853374 -020077 0083082
Post FBC -033082693 01292625 -02288 016678
Age 002653867 00055593 0044771 0007671
Age Sq -000286273 00005216 -000527 0000887
Obs 10001
10001
Adj R Squared 05309
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
57
Figure Titles
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned
house years
Figure 2 Regressions of predicted loss versus actual loss for model validation
Figure 1-App LN of Avg Loss by Structure Age
Figure 1 Pre and Post 2000 Year of Construction annual percentage of the ISO earned house years
0
10
20
30
40
50
60
70
80
90
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Pre2000 YOC Post2000 YOC Unclassified YOC
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
100
200
300
400
500
600
700
800
900
1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
58
Figure 2 Regressions of predicted loss versus actual loss for model validation
59
Figure 1-App
000
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1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
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LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
59
Figure 1-App
000
100
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300
400
500
600
700
800
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1000
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769717375777981
LN o
f A
vg L
oss
Structure Age
LN of Avg Loss by Structure Age
2000 to 2009 1990 to 1999 1980 to 1989 1970 to 1979 1960 to 1969
1950 to 1959 1940 to 1949 1930 to 1939 1920 to 1929
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-
60
i States and local jurisdictions do have national model codes that they can adopt as their own These model codes are developed by independent standards organizations such as the International Residential Code by the International Code Council ii Other studies have empirically demonstrated the value of effective building codes through a probabilistically-based catastrophe model framework that has been validated andor calibrated with historical claim data (See Kunreuther and Michel-Kerjan 2009 and AIR Worldwide 2010) iii ISO personal communication places this around 40 percent market share iv This percent is based on the 2005 EHY in our sample and the number of residential structures in FL in 2005 based on Census v It is also possible that many of the older homes were placed into the FL residual market wind pool unable to be underwritten by the private market vi This includes policyholders that did not incur a claim ($0 loss) therefore the average loss numbers here are significantly lower than those presented in Table 1 which were the average loss values for only those with a positive loss amount vii We also ran a Heckman hurdle model as well as a Tobit Hurdle model The coefficients for all variables are very close including our treatment variable Post FBC We chose to report the Simple hurdle model as it provides a value for Post FBC that is more conservative Heckman and Tobit results are available from the authors viii We further test the effect on claims by the FBC in the Appendix ix Personal communication with ATC
x A state provided map of the region can be found here
httpwwwfloridabuildingorgfbcWind_2010figurea_colors8png
xi We test this assertion in the Appendix by running our models on SFBC observations alone to see how the FBC treatment variable performs xii We use Census aged estimates for home size Census estimates are regional and we are using the South region However we compared those estimates to Zillow for Florida over the last 5 years and found them to be almost identical xiii httpswwwwhitehousegovthe-press-office20160510fact-sheet-obama-administration-announces-public-and-private-sector xiv We tested this at the beginning midpoint and end of the decade All methods had similar results in the impact of the FBC but using the beginning of the decade gave the lowest magnitude for that variable so we chose to use it as a conservative estimate of the FBC effect xv Risk averse individuals who buy a pre FBC home can at great expense retrofit the home to approach the FBC standard
- WP cover Simmons-Czaj-Donepdf
- BCA - Full Manuscript 2017maypdf
-