weather disruption: a best practice approach to assess a firm's exposure in an increasingly...

Weather Disruption & Risk Management:

A Best Practice Approach to Assess a Firm’s Exposure in an Increasingly Costly World of Weather Disruptions

Prepared by Steve Bowen of Impact Forecasting

November 2015

2Proprietary & Confidential

AgendaSection 1 OverviewSection 2 Increasing Weather LossesSection 3 What’s Driving the Losses?Section 4 Business ImpactSection 5 Solution: Catastrophe Modeling


Overview Weather catastrophe losses are increasing

Driver of the increase? All of the above

Global economy becoming more interconnected by the day

Businesses searching for best practice approach to mitigate & prepare for future natural disasters

Catastrophe modeling


Global Weather Losses Global weather-related economic losses have annually trended upward by

4.0% above inflation since 1980– Nominal loss trend: +7.1%

Weather Loss Avg. (1980-1989): $49 billion* Weather Loss Avg. (1990-1999): $105 billion* Weather Loss Avg. (2000-2009): $113 billion* Weather Loss Avg. (2010-2014): $185 billion*

Public and private insurance entities have paid out more than USD1.0 trillion in weather-related loss claims since 1980– Nominal loss trend: +10.1%– +7.0% above inflation

* Totals have been adjusted to 2015 USD


Global Weather Losses Which region of the globe has incurred the highest economic cost from

weather events since 1980?

– ANSWER: United States (41% of the world total)

$1.5 TRILLION How about insured losses?

– ANSWER: United States (67% of the world total)

$690 BILLION


Global Weather Losses (1980-Present)

United States– Economic Loss:

$1.50 trillion41%

– Insured Loss: $690 billion

67%

Americas– Economic Loss:

$321 billion9%


4%

EMEA– Economic Loss:

$626 billion17%


18%

APAC– Economic Loss:

$1.23 trillion33%


11%


Global Weather Losses: Peril Trends

Annual Increase Trend (1980-2014)– Tropical Cyclone: +6.2%– Severe Thunderstorm: +4.8%– Flooding: +4.2%– Other Perils: +2.5%

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0

20,000,000,000

40,000,000,000

60,000,000,000

80,000,000,000

100,000,000,000

120,000,000,000

140,000,000,000Flooding

Annual LossAverage (1980-2014)

USD

Bn

Source: Aon Benfield

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0

50,000,000,000

100,000,000,000

150,000,000,000

200,000,000,000

250,000,000,000Tropical Cyclone


USD

Bn


1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0

5,000,000,000

10,000,000,000

15,000,000,000

20,000,000,000

25,000,000,000

30,000,000,000

35,000,000,000

40,000,000,000

45,000,000,000Severe Thunderstorm


USD

Bn



Weather Loss to GDP Aon Benfield: Analysis of losses relative to GDP

– Economic Loss to GDP trend (1980-2014): +1.1%– Insured Loss to GDP trend (1980-2014): +4.6%– Flatter growth trends since 1990 given better global data records

Economic growth, increased value of insured assets, and population migration account for ~85% of increased loss trend– 6.1% GDP growth vs. 7.1% nominal loss growth

Factors such as weather & climate account for the other ~15%


Weather losses dating to 1980…

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0

50,000,000,000

100,000,000,000

150,000,000,000

200,000,000,000

250,000,000,000

300,000,000,000Global Weather Economic & Insured Losses

Economic Loss Insured Loss Source: Aon Benfield


…are manageable as a proportion of GDP

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

Economic & Insured Loss as % of GDP

Economic % of GDP Insured % of GDP

% o

f GD

P

Source: Aon Benfield & World Bank GDP (Current US$)


Weather vs. Climate

“Climate is what we expect; weather is what we get.” – Mark Twain, 1887

Weather: atmospheric conditions over a short period of time (hour, day, week) over a particular area. Weather exhibits short-term fluctuations, sometimes extreme, in temperature and precipitation types and amounts.

Climate: atmospheric trends over a longer period of time (season, year, decade, century) over a particular area. Climate shows trends in temperature and precipitation data that is compared to multi-year averages.

…in a time of climate change


Climate Change Global land and sea surface temperatures are rising

Global CO2 is at its highest level in 3 million years

Proven correlation between CO2 and temperatures

Sea level and upper ocean heat content are rising

Natural variability and cycles occur in the atmosphere and oceans

Weather-related catastrophe losses are increasing above the rate of inflation– Not all increases can be attributed to climate change– Population and housing shifts playing a significant role


Fact: Global Temperatures are Rising

October 2015 was 368th consecutive warmer-than-average month

Recent pause?– NOAA Study: Rate of warming in the

last 15 years has been as fast or faster than what was witnessed in the latter half of the 20th century

Consistency among major data collection agencies– NOAA, UK MetOffice, NASA, JMA– Trend continues to show increase


Fact: Carbon Dioxide Levels are Rising

1950 Level

2015 Level


Fact: Correlation Between CO2 & Temperatures

Present Day CO2 Value


Fact: Sea Levels Rising & Oceans Getting Warmer Global mean sea level has

risen at an average rate of 1.7 mm/year over the past 100 years

Since 1993, sea level has risen at an accelerated rate of 3.5 mm/year

Melting land ice (glaciers) will play a more significant role in future sea level rise

93% of global warming ends up being stored in and heating the oceans

Warmer oceans lead to more unstable atmosphere


Critical Non-Climate Factors Global population is growing (now 7+ billion)

– Annual growth rate of +1.6%

Population migration shifts– 44% of current world population (3.2 billion) lives within 150 km of an ocean

coastline

Increased commercial and residential exposure– People moving to areas most at-risk and vulnerable to the costliest perils

Global wealth accelerating– Nominal GDP annual growth: +6.1% since 1980


Global Urbanization Trends

1970– World Population: 3.7 billion– Urban Population: 1.4 billion– 38% of world population urban



1970

2014Source: United Nations


U.S. Annual Population Growth Rates (1980-2010)

Source: U.S. Census

Urban sprawl occurring more frequently

30% of the U.S. population and 28% of U.S. housing counts are located in an area threatened by tropical storms and hurricanes

West: 3.0%Pop: +11.4M

Midwest: 0.5%Pop: +8.1M

Atlantic: 0.9%Pop: +19.9M

Gulf Coast: 2.1%Pop: +21.6M

72% of total population increase has occurred in ocean-bordering states

since 1980

Non-Coastal South: 0.9%Pop: +3.8M

Coastal West: 1.9%Pop: +17.4M


Coastal migration and build-up: Miami Beach, 1926-2006

Source: Wendler Collection Source: Joel Gratz © 2006

Miami Beach 1926

Miami Beach 2006


U.S. population and housing counts have steadily grown over time

1980 1985 1990 1995 2000 2005 2010210,000,000

230,000,000

250,000,000

270,000,000

290,000,000

310,000,000

330,000,000

80,000,000

90,000,000

100,000,000

110,000,000

120,000,000

130,000,000

140,000,000

Population Housing Units

U.S. Population Growth1980-1990: 9.8%1990-2000: 13.1%2000-2010: 9.7%

Annual Growth Rate1980-2010: 1.2%

U.S. Housing Count Growth1980-1990: 15.7%1990-2000: 13.3%2000-2010: 13.6%

Annual Growth Rate1980-2010: 1.2%

U.S. Population and Housing Growth

Source: U.S. Census Bureau


1975 average U.S. house size: 1,645 square feet 2007 average U.S. house size: 2,521 square feet 2014 average U.S. house size: 2,657 square feet

– Average annual rate of change 1975-2010: approximately +1.1%

Increasing Average Housing Size

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

1,500

1,700

1,900

2,100

2,300

2,500

2,700

2,900 U.S. Single-Family Home Size

U.S. Average U.S. Median

Squa

re F

eet

Source: U.S. Census Bureau

Annual Growth Rate (1973-2014)U.S. Average: 1.43%U.S. Median: 1.45%


Annual GDP growth trend (1980-2014): +6.1%

Steady Growth in Global GDP

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0

10,000,000,000,000

20,000,000,000,000

30,000,000,000,000

40,000,000,000,000

50,000,000,000,000

60,000,000,000,000

70,000,000,000,000

80,000,000,000,000

90,000,000,000,000Global GDP

GD

P (T

rillio

ns U

SD)

Source: Aon Benfield & World Bank GDP (Current US$)

World GDP has grown from $11 trillion in 1980 to nearly

$80 trillion in 2014


Business Impacts World economy is interconnected like never before

Extreme weather can lead to significant disruptions to the global supply chain and business interests

Damage to business facilities and/or infrastructure cause major direct and secondary impacts to how companies are able to meet their clients’ needs

Business interruption, in some instances, may be as costly as actual physical damage incurred

Nearly every major line of business at risk by weather or natural disaster peril


Global Examples (…2011 was a bad year…) 2011 Japan Earthquake & Tsunami

– Worldwide impact to nearly every line of business– Extensive facility & infrastructure damage had major global delivery implications

• $222 billion economic loss (costliest natural disaster in world history)• $38 billion insured loss

2011 Thailand Floods– Devastation to manufacturing facilities for automobile & electronics industries

• $47 billion economic loss (secondary losses were even higher)

2011 Global Floods & Drought– Severe impacts from both perils in Australia, Russia and Pakistan caused global

food prices to skyrocket• Societal impact? May have led to escalation of civil unrest in the Middle East.


Global Example: Super Typhoon Haiyan (2013)

Source: Nature Journal


Global Example: Super Typhoon Haiyan (2013)

Source: Nature Journal

Haiyan was the costliest natural disaster in the Philippines’ history– Economic Loss: $13 billion; Insured Loss: $1.5 billion


U.S. Examples 2014/15 Polar Vortex & Heavy Snow

– Business closings; interstate closures; airport closures; frozen/burst pipes– Physical damage

• $11 billion economic loss in two years due to winter weather (highest two-year stretch since 2010/11)

2011 Severe Weather– Epic year for tornado, hail & straight-line wind damage– One of the rare years where tornado costs equalled or surpassed hail/wind totals

• $39 billion economic loss; $28 billion insured loss


Weather Impacts Remember 2005? Conga line of Atlantic hurricanes led to spike in U.S.

gasoline prices

1/3/05

1/14/05

1/25/05

2/5/05

2/16/05

2/27/05

3/10/05

3/21/05

4/1/05

4/12/05

4/23/05

5/4/05

5/15/05

5/26/05

6/6/05

6/17/05

6/28/05

7/9/05

7/20/05

7/31/05

8/11/05

8/22/05

9/2/05

9/13/05

9/24/05

10/5/05

10/16/0

5

10/27/0

5

11/7/05

11/18/0

5

11/29/0

5

12/10/0

5

12/21/0

5$1.70

$1.90

$2.10

$2.30

$2.50

$2.70

$2.90

$3.10

$3.30

U.S. Gasoline PricesIncreased 45% from $2.11 at start of hurricane season to a peak of $3.07 in the aftermath of Hurricane Katrina

$3.07

$2.11Historical Peak of Atlantic Hurricane

Season


U.S. Business Interruption Extreme weather does not have to cause damage to lead to disruption

Violence/Bombing/Terrorism

Earthquakes

Hurricanes

Fires/Explosions

Floods

Lightning Storms

Telecommunication Failures

Computer Hardware Problem

Power Outages

0% 10% 20% 30% 40% 50% 60% 70% 80%

6%

8%

14%

15%

18%

34%

46%

52%

72%

Percentage of U.S. Businesses Disrupted Due To....

Source: Aon Benfield & Journal of Accountancy


Catastrophe Modeling Very Broad Definition: Computer simulations used to analyze exposure

risks, determine loss frequency and calculate financial losses from different disaster perils

Each model takes into account a number of different parameters– Physical Characteristics of Exposures

• Type of construction, occupancy, year build, number of stories, etc.– Property Location– Financial terms of insurance coverage

Company portfolio exposures can be modeled to determine financial risks for varying peril scenarios

Historical events and stochastic event sets used for model calibration


Catastrophe Modeling

How do they work?

Data Preparation

Exposure Import &

ConversionHazard Vulnerability Loss Financial

Terms Results

- Validate event catalog and track information

- Understand event frequency and chance of loss

- View and adjust intensity

- Loss derivation via Monte Carlo simulation

- Understand the relationship between hazard and vulnerability

- Improve on location accuracy (geocoder)

- Map risk to hazard grid- Map risk to vulnerability

curve

- Probable Maximum Loss

- Event Loss Table- Uncertainty Reports

- Geographical and physical characteristics

- Policy Terms


Catastrophe Modeling Why catastrophe modeling?

– Historical loss information may not be credible enough for long-range projections• Remember: +7.1% nominal economic loss growth vs. +1.1% as percentage of GDP

– Insurance companies increasingly need the ability to quantify the loss potential AND the frequency of the loss

– Businesses able to assess their portfolio exposure risks for several types of natural and man-made perils (including terrorism)

– Idea of “exposure management” gaining traction in the business space

– Commercial “cat models” now available in countries equalling 90% of global GDP


Catastrophe Modeling


Catastrophe Modeling: The Reality Nearly every major type of natural disaster peril can now be modeled

– Tropical Cyclone (including Storm Surge), Severe Thunderstorm, Earthquake, Flooding (Riverine & Flash Flood), EU Windstorm, Wildfire

Catastrophe models are only as good as the data made available– Limitations to quality data beyond the United States, United Kingdom, Australia and

Japan– Incomplete datasets

A “perfect” catastrophe model does not exist


Catastrophe Modeling: The Reality Uncertainty!

– Primary Uncertainty• What type of event may occur? Will an event occur? What event scenario might it be?

– Secondary Uncertainty• How much loss will an event cause? Do the losses make sense?

– Types of Uncertainty• Hazard Uncertainty

Assumptions are made during model development (i.e. defining probability of impact)• Location Uncertainty

Input data (i.e. crude information about insured risks and their location)• Damage Uncertainty

Vulnerability (i.e. how do the insured risks behave when subjected to hazard))


Catastrophe Modeling: The Reality Cat modelers have their own unique methodologies (IF, RMS, AIR, etc.)

– Engineering testing– Different data sets or portfolios– Outside influence or opinion from academia


Recap Weather catastrophe losses are increasing

Driver of the increase? All of the above

Global economy becoming more interconnected by the day

Businesses searching for best practice approach to mitigate & prepare for future natural disasters

Catastrophe modeling


Contacts Steve BowenAssociate Director & MeteorologistImpact [email protected]

mailto:[email protected]

This presentation was a part of The Risk Institute’s Executive Education Series on November 12, 2015. For more information visit fisher.osu.edu/risk.