federal land use controls and the planning anticommons
TRANSCRIPT
Federal Land Use Controls and the Planning Anticommons
David Sunding UC Berkeley
Aaron Swoboda
University of Pittsburgh
Jonathan Terhorst UC Berkeley
July 15, 2007
Abstract
The paper concerns the economics of federal land use controls in a legal system that gives primary authority over land use to state and local governments. Such overlapping sets of land use regulations can result in a “planning anticommons” and reduce the value of property. A conceptual model captures the essential features of federal regulation, a combination of mitigation and avoidance requirements, and demonstrates how the welfare cost of these interventions is influenced by pre-existing local regulations. The framework is applied to the case of wetland conservation in California. It is shown that the overall cost of federal efforts to conserve these wetlands is large, but can be reduced by about two-thirds if federal land use controls are coordinated with those of local governments. Further, the case study shows a large variation in the per-acre cost of federal land conservation efforts, with 80% of costs attributable to conservation of 10% of habitat. This research was funded by the Giannini Foundation, the U.S. Department of Housing & Urban Development and the U.S. Department of the Interior. We would like to thank seminar participants at UCLA, UCSB, UC Berkeley, Ohio State University, University of Wisconsin, VPI, University of Maryland and University of Arizona. We would also like to acknowledge helpful conversations with John Quigley, Jeff Zabel, JunJie Wu, Andrew Plantinga, Jennifer Baxter, Robert Unsworth, Chip Patterson, Ted Maillett, Richard Adams, David Zilberman, Tom Davidoff and Steve Raphael.
Federal Land Use Controls and the Planning Anticommons
I. Introduction
Land use control has emerged as a major environmental policy issue in this
country. Environmental laws enacted in the 1970s have resulted in significant reductions
in point source pollution discharges. A desire to continue this trend of environmental
improvement has led to a focus on land use controls. In the area of water quality, for
example, land use is now the leading cause of impairment of the nation’s water bodies.
According to the EPA, at least half of the foreign matter in water does not come from
sewage treatment plants or factories, but instead results from the alteration of land in the
course of its economic development (USEPA, 2000).
Habitat conservation is another area in which control of land use is instrumental
to achieving desired outcomes. Most endangered species rely on private lands for most of
their current and potentially restorable habitat, and wetlands that provide a range of
environmental services are likewise affected by urban development (Environmental
Defense, 2000). Public lands alone are insufficient to recover most endangered species,
partly because the distribution of public lands is so skewed, and successful protection of
endangered species will necessitate habitat protection and enhancement on private lands.
The past several decades have witnessed a tremendous expansion of federal
regulation of land use changes under both the Clean Water Act and the Endangered
Species Act. With respect to wetlands regulation carried out under the Clean Water Act,
the Army Corps of Engineers and the US Environmental Protection Agency assert
jurisdiction over roughly 300 million acres of wetlands and other areas, including half of
Alaska and over 100 million acres in the lower 48 states. The Corps and the EP A claim
jurisdiction over all areas that qualify as defined by the Corps' 1987 Wetlands
Delineation Manual. They also claim jurisdiction over areas they deem to be "other
waters," as long as the wetlands or other waters have the potential to affect interstate
commerce. There are no minimum size requirements for an area to be deemed a water of
the United States, and an area may qualify as a jurisdictional wetland even if it never has
water on it. Moreover, the Corps and the EP A claim the authority to regulate ditches,
miniscule depressional areas, and other ephemeral landscape features resulting from
human activity.
The Endangered Species Act prohibits private property owners from “taking” any
species listed as endangered. The term “take” includes alteration of habitat and thus
affects a wide range of land use decisions (see the Supreme Court’s opinion in Babbit v.
Sweet Home Chapter of Communities for a Greater Oregon, 515 U.S. 687 (1995)).
Property owners may take an endangered species if they obtain a permit from the
Secretary of the Interior; such permits require an enumeration of the extent of the take,
and the adoption of a species conservation plan for the property in question. Spurred by
citizen suits, the Fish & Wildlife Service is now designating extensive areas of the nation
as “critical habitat” for various endangered species. Under Section 7 of the Act, all
federal agencies must ensure that their actions (including permitting activities under the
Clean Water Act, for example) are not likely to jeopardize the continued existence of a
listed species, or destroy or adversely modify its designated critical habitat. Recent
proposed designations include 6.9 million acres for spotted owl, 1.2 million acres for
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Canada lynx, and 20,630 stream miles for salmon and steelhead. In California alone, the
U.S. Fish & Wildlife Service has proposed that over 40 million acres of the state are
“critical habitat” for one or more species, which is roughly 40% of the state’s total land
area. Private land ownership in California totals roughly 50 million acres.
Despite the importance of land use control in achieving national policy objectives,
current federal environmental laws address private land use only in general and
unfocused terms (Pederson, 2004). The Clean Water Act and the Endangered Species Act
are crafted to avoid taking private property (in fact, the ESA has an absolute prohibition
against taking private property; see, for example, Thompson, 1997), and to avoid direct
conflicts with the planning efforts of state and local governments. Rather than select
specific lands for actual preservation, federal agencies impose procedural burdens on
private land development that require project proponents to consult with the overseeing
agency, minimize direct impacts where possible, and compensate for impacts that do
occur. These permitting requirements often allow development to proceed on some
portion of the property (to avoid triggering a takings claim by the landowner), may
prohibit it on part, and spell out a suite of required conservation measures.
The problem studied in this paper is an example of the “planning anticommons,” a
term coined by Fischel (1985). The basic idea of the anticommons is by now well known,
developed most notably by Heller (1998). The term refers to a situation in which multiple
claims of ownership coupled with the ability to exclude results in the underutilization of
property; it is analytically the opposite of the “tragedy of the commons” in which
multiple claims of ownership lead to overaccess and overexploitation. While the concept
of the anticommons has been applied extensively to topics such as patents (e.g., Heller
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and Eisenberg, 1998), it is also relevant to the subject of land use regulation. Fischel
discussed how the existence of overlapping permitting processes could erode the value of
land. This observation is consistent with the analysis in this paper, and we develop an
approach to measure the consequences of uncoordinated action among federal, state and
local governments. The ability of third parties to compel agency action can exacerbate
this problem, as it sometimes gives these groups veto power over agency decisions, and
creates an avenue for interest groups to increase the transaction costs of land
development.
This paper begins by developing a model of the costs of federal land use controls
in an urbanizing area. We are interested in the total cost of federal regulations (which can
then be compared to the benefits of water quality improvements or habitat protection; see
Freeman, 2003), and also in the variation in cost per acre as a way to gauge the potential
inefficiency of federal land use regulation. A major feature of the analysis is that we
examine the effect of federal land use controls in a legal system that reserves primary
authority over land use decisions for local governments. That is, we consider the welfare
cost of federal regulations when imposed on a backdrop of land and housing markets that
are subject to pre-existing local regulations.
We apply the conceptual framework to the case of California vernal pools, a type
of seasonal wetland that provides habitat to over a dozen endangered plants and animals.
Using detailed land use projections at the Census tract level combined with data on the
cost of mitigation and a set of local housing market parameters, we measure the costs of
land conservation and other mitigation measures across the vast acreage regulated by the
federal government due to the presence of vernal pools. The range of measured costs of
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habitat conservation is very large, with 80% of total cost in the baseline scenario
occurring on roughly 10% of the regulated acreage. This variation reflects the fact that
efficiency is not an objective of the federal agencies charged with conserving vernal
pools, and suggests that agencies could re-prioritize by seeking higher levels of
conservation on certain lands, thereby achieving a more efficient outcome.
The paper shows that policy coordination among federal and local agencies can
reduce the cost of achieving national policy objectives. For example, by allowing denser
development in areas where land conservation is sought, among other measures, cities
and counties may help reduce the economic losses from federal land use controls. In the
case study, densification of development is shown to reduce the costs of federal
regulation by about two-thirds.
II. Model
Environmental policy analysis is traditionally built around the paradigm of prices
vs. quantities, following the seminal analysis of Weitzman (1974). Federal land use
controls are more akin to a licensing program, and can impact development projects by
altering both costs and output levels (Sunding and Zilberman, 2002). In their attempts to
conserve land, federal agencies typically specify conservation requirements in terms of
both avoidance and mitigation. Avoidance requirements entail leaving some portion of an
area proposed for development in an undisturbed condition. Unless other land is made
available for development, avoidance requirements result in a net loss of developable
land. Mitigation requirements oblige the developer to undertake actions that improve or
protect habitat in some other location. Usually, the federal agency specifies a mitigation 5
ratio of acres protected off-site to acres disturbed by development; this ratio is frequently
in excess of 1:1 and is affected by factors such as the rarity of the disturbed habitat,
uncertainties associated with the ability to produce comparable habitat and other factors
(National Research Council, 2001)
Analytically, a mitigation requirement is akin to a tax on greenfield development.
The number of mitigation credits needed equals the area disturbed multiplied by the
mitigation ratio. The cost of mitigation is then the number of credits needed multiplied by
the price per credit. The credit price is, in turn, determined by market conditions and is
the result of negotiations between the project developer and the owner of the mitigation
site.1
The avoidance requirement reduces the stock of developable land. A great deal of
research in urban economics has focused on the question of zoning regulation by local
governments and the ways in which zoning and other limitations on the stock of
developable land influence the housing market. The vast majority of this literature is in
the neoclassical tradition of the Alonso-Muth-Mills model of location choice and urban
growth, and considers land as an input into the production of new housing. This approach
typically assumes that density is variable and can be adjusted by developers in response
to land use controls and other market conditions. Accordingly, the neoclassical approach
1 One interesting result of the requirement to mitigate project impacts offsite is that it can transfer a significant share of the rents from urban development to owners of mitigation sites. This outcome is especially likely when mitigation concerns a scarce habitat type, or one that is hard to replicate artificially. Further, economists have advocated for the efficiency of offsite mitigation since areas to be preserved in this way can be well out of the path of urban development, and thus of less value than the areas disturbed. Thus, offsite mitigation can also transfer a share of the rents from urban development to landowners in more remote areas, thereby altering the usual result that land rents accrue as a function of distance to the city center or other central points.
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predicts that land prices and density will increase in response to a reduction in the stock
of developable land, such as an avoidance requirement.
The profit earned from housing development in a given location is
Π = p(λ)H − k(H ) + γ (N − Hλ)
where λ is the amount of land per house (i.e., the inverse density), H is the number of
housing units, is the cost of construction and k N is the stock of developable land. If
developers can subdivide optimally, then in equilibrium the price of land (γ ) is equal to
the profit from homebuilding per unit of land and homebuyers’ marginal valuation of
land ( ). That is, in equilibrium there is an equality between the extensive and intensive
margin values of land, or
pλ
p − kH
λ= pλ = γ .
Land Development with Local Regulation
Recently, Glaeser and Gyourko have shown that while this neoclassical model fits
the data well in some of the nation’s housing markets, it badly misses the mark in other
markets, especially those on both coasts (Glaeser and Gyourko, 2003; Glaeser, Gyourko
and Saks, 2006). In these markets, they argue that local regulation creates scarcity in the
housing market by limiting the number of housing units constructed. By creating a
shadow value of housing, such regulation drives a wedge between the intensive and
extensive margin values of land, in contradiction to the neoclassical model (Glaeser and
Gyourko, 2003).
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To see how local regulation can affect land development decisions, consider a
case in which a city regulates both the stock of developable land and the density of
development (thus implying a cap on the number of housing units built). In this case, the
developer’s profit is equal to
Π = p(λ)H − k(H ) + γ (N − Hλ) +θ(λ − λ ) .
The equilibrium conditions in this fixed-density model are as follows:
p(λ) − kH
λ= γ
θH
= γ − pλ
The first condition implies that the price of land is equal to its extensive margin value, or
the value of land with a house on it. The second expression implies that when the density
constraint is binding and the housing supply is limited, the price of land exceeds its
intensive margin value, defined as consumers’ marginal willingness to pay for land. If the
density constraint is not binding, then 0θ = and the system reduces to the simplified
model shown above. With binding density controls and a limited stock of developable
land, the housing stock is limited to H = Nλ
, and there is a shadow value of housing
that is incorporated into the market price of developable land.
Welfare Cost of Federal Land Use Controls
This discussion suggests an important way in which the costs of federal land use
controls are affected by local regulation. Conservation requirements imposed by federal
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agencies reduce the stock of developable land. Since N = Hλ , a reduction in the stock of
developable land with a fixed density constraint causes a proportional reduction in the
amount of new housing. Alternatively, the status quo housing stock can be maintained by
increasing the allowable density. We term these alternatives the rationing and
densification scenarios, respectively. They bookend the ways in which a local
government can respond to federal agency reductions in the stock of developable land.
We now turn to the marginal welfare costs of federal land conservation. Since we
assume that the price of housing is fixed (which is tantamount to assuming that only a
small fraction of developable land is subject to a particular federal regulation), the social
welfare from housing development is the sum of developer profit and landowner rent.
Then social welfare is
, W = p(λ)− kH[ ]0
H
∫ dH
and the marginal welfare cost of a reduction in the stock of developable land is as
follows:
Rationing: dW λ=λ =p(λ)− kH
λdN
Densification: dW H =Nλ= pλdN
Note that in the neoclassical model where density is freely variable, marginal welfare
losses under these two scenarios will be equal. However, when the density constraint is
binding, then the marginal welfare loss under rationing will exceed the loss under
densification.
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Recall that federal agency action is typically specified in terms of both avoidance
and mitigation. That is development is prohibited on some fraction of the proposed
project area, and is allowed to proceed on the remainder. In this sense, the federal action
has elements of both quantity and price regulation since the mitigation requirement is
akin to a tax on land conversion. Allowing for the possibility that regulation entails a
combination of mitigation and avoidance, the per-acre cost of federal regulation is as
follows:
Rationing: MCR = αp(λ) − kH
λ+ (1−α )ωρ
Densification: MCD = α pλ + (1−α )ωρ
where the price of a mitigation credit is ω , α is the percentage development that must be
avoided, and ρ is mitigation ratio specified by the agency. Assuming the mix of
mitigation and avoidance is primarily a biological choice unaffected by local zoning
rules, the marginal cost of conservation through rationing will exceed the cost of
conservation when density is variable.
Recently, Quigley and Swoboda (2007) developed a theoretical treatment of the
effects of habitat conservation efforts on the housing market, particularly locational
choices. Their findings confirm that habitat conservation can have a significant effect on
welfare in urban areas, and that habitat conservation can transfer large amounts of wealth
among landowners, housing consumers and the rest of society. Their paper, however, is
predicated on the standard neoclassical model as described above, and does not consider
the effects of local regulation. In fact, their analysis assumes that density and other
housing characteristics can vary in response to land conservation efforts. Further, they
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assess only the welfare effects of avoidance, and not the mitigation requirements
described above.
One way in which Quigley and Swoboda’s approach is more general than this one
is that they consider both open and closed cities, whereas this paper uses an open city
model. The results are consistent in that Quigley and Swoboda note that in the open city
version of their model, the marginal welfare cost of the avoidance requirement (or a
reduction in the amount of developable land at a particular location) is equal to the price
of land at that location. Where the approaches differ is that our analysis recognizes that
local regulation can drive that price of land well above consumers’ marginal valuation of
lot size, implying that local regulation can drive the costs of federal land use controls well
above levels envisioned by the standard neoclassical model.
III. Case Study: California Vernal Pools
This section of the paper presents a case study of the costs of federal land use
controls: protection of vernal pool habitat in California. Vernal pools are small seasonal
wetlands that provide specialized habitat for over a dozen endangered species. Owing to
the presence of these species, and to the fact that even seasonal wetlands are classified as
“waters of the United States,” development in areas containing vernal pools is regulated
by the federal government under both the Endangered Species Act and the Clean Water
Act. From an economic point of view, conservation of vernal pools is an ideal case in
which to explore the impacts of federal land use controls since they are scattered across
numerous local government jurisdictions and many vernal pool complexes lie squarely in
the path of projected development. 11
Federal Agency Conservation Requirements
Project developers must obtain federal agency permission before commencing
construction on sites that contain vernal pools. Because vernal pools are a type of
wetland, they fall under the jurisdiction of the Army Corps of Engineers, which is tasked
under Section 404 of the Clean Water Act with the issuance of permits to discharge fill
material into wetlands and other waters of the United States. Because vernal pools
contain a number of endangered plant and animal species, prior to issuing a permit the
Corps must consult with the U.S. Fish & Wildlife Service under Section 7 of the
Endangered Species Act dealing with interagency cooperation.
California vernal pools are classified into two broad types: Group A and Group B.
Each type of vernal pool habitat is subject to different federal conservation requirements.
Group A habitat has a higher frequency of occurrence and is relatively easy to provide
via conservation banks. Group B habitat, by contrast, supports a greater number of
endangered species and is unlikely to be successfully created in conservation banks. The
Service reports that Group A habitat is not subject to any avoidance requirement, but
developers must provide compensatory mitigation at a ratio of 2:1 (USFWS, 2005). That
is, for every acre of vernal pools eliminated by development, the permit applicant must
provide 2 acres of created or restored vernal pool habitat. Projects may fulfill the
mitigation requirement by purchasing credits from a conservation bank, purchasing
suitable habitat and managing that habitat in perpetuity, or dedicating land already owned
by the project applicant and having suitable vernal pool habitat.
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Conservation requirements for Group B habitat are much higher. The Service
indicates that avoidance should occur on 85.7 percent of the project site within vernal
pool critical habitat (a 6:1 avoidance requirement), thus allowing development to occur
on only 14.3 percent of the project site. In addition, the developer is required to mitigate
disturbance of vernal pool habitat at the rate of 3:1 for each acre of vernal pools filled.
These conservation requirements reflect the universalist and inflexible approach
frequently taken by federal agencies with respect to environmental regulation (Sunstein,
1997; Ackerman and Stewart, 1985). Vernal pool conservation requirements are not
affected by the conditions of the underlying markets for land and housing. Rather, what
conditioning that does occur is due to biological differences in the types of vernal pool at
issue, with the rarity of Group B habitat leading to more severe land use restrictions and
greater on-site avoidance requirements.
Conservation bank prices are used to estimate the mitigation costs associated with
federal agency action. Prices were obtained from Service personnel who routinely survey
conservation banks. The largest prevalence of existing banks is in the Sacramento region,
where each vernal pool conservation credit costs roughly $200,000 per acre.2 In addition,
the Service estimates that the average cost of a mitigation credit is $135,000 in Placer
County and $105,000 elsewhere in the study region.
The geographic extent of vernal pools is also well-defined. On August 6, 2003,
the Fish & Wildlife Service proposed critical habitat for 15 vernal pool species on the
2 Recently, seasonal wetland credits in the Sonoma area, which is outside our study region, have reached $600,000 per acre, indicating both the importance of mitigation requirements and the scarcity value of housing in northern California.
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threatened or endangered lists. Critical habitat is defined as areas that are in need of
special management to ensure toe conservation (including recovery) of the species. The
vernal pool designation is notable in several respects, first for its size. Geographically,
biologically, and economically, the designation cuts across a broad swath of the
California landscape. The Service designated a total of 1.2 million acres—over one
percent of the entire state—as far south as Ventura County and as far north as Modoc
County on the Oregon border. The analysis that follows calculates the cost of the federal
conservation requirements for vernal pools within the area of critical habitat.
Extensive Margin Values
As defined in the previous section, the extensive margin value of land is the
difference between its selling price and the cost of building it, per unit of land. Building
costs include including labor and materials, and the cost of development including
architecture, grading, utilities, provision of common space, and local fees (including
utility hookup charges). This section describes how the extensive margin value of land is
calculated for each acre in the study area.
Data on the market prices of new homes were obtained from DataQuick
Information Systems, which maintains a database of new single-family home sales in the
study area. Based on information gathered from county recorders and assessors, the
database provides a rich set of house descriptors, including assessor’s parcel number,
home size, lot size, number of stories, number of bedrooms, number of bathrooms, build
year, sale price, and sale date for all transactions dating back to 1997. It is worth
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emphasizing that our dataset is for newly constructed homes, thus avoiding problems of
over-aggregation that frequently plague hedonic analyses of this type.
Each observation is also spatially referenced, so the data can be aggregated to any
level using a geographic information system (GIS).3 Table 1 presents descriptive
statistics for the salient variables. The database exists for 120 of the 158 census tracts
comprising the federally-designated critical habitat. Because these data are essential to
the model, the remaining tracts were excluded from the analysis.
Because California home prices have roughly tripled in the past decade, the
nominal sale prices reported by DataQuick are not directly comparable across time. The
prices were inflated to real dollars using the Office of Federal Housing Enterprise
Oversight’s home price index. This index provides quarterly data on price inflation for
detached, single-family dwellings by metropolitan statistical area (MSA). For
observations that did not lie in any MSA, they were matched to the closest.
Data on the cost of residential construction was obtained from Marshall & Swift,
which publishes a quarterly guide to building cost per square foot indexed by region,
construction quality (average, good, very good, or excellent), home size. New homes
were assumed to be one story, stud-framed with stucco siding and of average construction
quality, which is typical for newly constructed tract homes.
3 Data censoring was performed to correct for apparent data entry errors, outliers and missing data. Out of an initial 355,509 observations, 144,222 were dropped due to missing observations on key variables needed for the analysis. Of the remainder, an additional 52,263 were dropped due to censoring. Histograms of the censored variables are shown in through . Censoring cutoffs were chosen to suppress extreme outliers while maintaining smooth tails.
Figure 1 Figure 3
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Descriptive statistics for the extensive margin value are presented in Table 2. The
first two columns present the means and standard errors of the mean. Means range from
$25 to $165 per square foot. The remaining four columns display observations per county
as well as the 25th, 50th and 75th percentiles.
Intensive Margin Values
Intensive margin values of land are calculated using a hedonic regression
technique whereby home value, characterized by observed selling price, is decomposed
into an additive bundle of housing, geographic and demographic attributes:
1 2 3 4 5i i i i i ip lotsize sqft beds baths stories iβ β β β β= + + + + + βL ,
where L is a vector of neighborhood and geographic control variables, plus an intercept.4
1β is then the intensive margin value of land as defined in the previous section. The
specification was estimated on observations grouped by metropolitan statistical area so as
to model the composition of markets for new housing and developable land, which
frequently span county lines. Since geospatial datasets often exhibit autocorrelated error
terms, a robust regression was utilized to relax the classical OLS assumptions.
Table 3 displays point estimates, standard errors, and regression diagnostics. The
least expensive land values in the data set are located in the Modesto MSA, at $3.03 per
4 The spatial variables used in L were: distance to the nearest incorporated city; distance to the nearest metropolitan statistical area; county and city fixed effects; and elevation and the square of elevation above sea level. The demographic controls were block group data in the 2000 census and consisted of: population density (persons per square mile), median age, percent population growth over the period 2000-2004, percent white, percent black, percent Hispanic, percent Asian, and the percent of housing that was renter occupied
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square foot. At more than five times that amount, the Oakland MSA is the most
expensive in the study area.
Development Activity in Areas Containing Vernal Pools
The total cost of conservation is the product of the per-unit conservation cost,
above, and the number of new homes that will be built in the conserved area.
Determining the increase in new homes within critical habitat presents several challenges.
First, a suitable time frame must be selected, one which is short enough to give the model
strong explaining power, yet long enough to fully capture the effects of the regulation.
Second, a suitable and precise spatial frame must be selected, due to the highly localized
effects of habitat conservation. Third, predicting the location of development requires
modeling the urban growth process, a notoriously difficult problem.
This paper examines urban growth forecasted to occur over a 20-year timeframe.
This time frame coincides with the planning horizon of the California state-mandated
jurisdictional General Plans and population and employment projections by regional
associations of governments. It is the longest time span for which the demographic and
economic forecasts needed for this study are reliable and available.
This study reaches below the regional level in order to illustrate the location-
specific, heterogeneous nature of the effects of federal land use regulation. Census tracts
are used as the unit of analysis. This choice is motivated by both the nature of the
problem and data availability. The census tract is a standard level of aggregation in
socioeconomic research. It is finest level of distinction at which the above data are
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published. This is important in light of our finding that local, even neighborhood-level
characteristics are responsible for a high degree of heterogeneity in the effects of habitat
conservation. For example, a county-level analysis may not be sensitive enough to
discern any noticeable effect even though the effects are large on a smaller scale.
The primary sources for estimates of future housing and population were the
study area’s federally designated Metropolitan Planning Organizations (MPO). Typically
created by county governments, these forecasts are the preferred source for growth
estimates because they are created using detailed knowledge about local growth trends
and characteristics, potentially resulting in more accurate estimates than those obtained
with mathematical forecasting techniques. The organizations which created the estimates
used in this analysis are ABAG, the Association of Bay Area Governments; SACOG, the
Sacramento Area Council of Governments; SCAG, the Southern California Association
of Governments; and AMBAG, the Association of Monterey Bay Area Governments.
If these forecasts were not available for a given area, for example because the
MPO does not publish these data out to 2025 or at the tract level, mathematical forecasts
created for a CalTrans planning survey by UCLA’s Institute of Transportation Studies
were used instead (Crane et. al. 2002.).
Identifying the housing and population deltas also requires estimates of present-
day conditions. These were obtained from Applied Geographic Solutions, a company
which offers yearly updates to the latest available decennial census housing estimates
based on the Census’s American Community Survey, change-of-address records, FEMA
registrations, USPS delivery statistics, IRS statistics, and credit-reporting databases.
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Finally, growth estimates must be modified to account for infill development.
This is a common feature in California, where many cities are already built-out. It entails
redeveloping low density, typically single-family, dwellings into high-density apartment
blocks, condominiums, or shared dwellings. Landis and Reilly (2003) completed a study
of projected infill development by county over the next 50 years. Growth estimates were
adjusted according to their numbers to avoid overstating greenfield development.
Since the federal critical habitat proposal does not delineate the precise location of
vernal pools, but rather areas that are likely to contain them, the model was adjusted
probabilistically to account for the likelihood of a federal permitting requirement. The
expected loss due to consultation, E(L) , was set equal to the expectation over the
potential states C (consultation) and NC (no consultation), with . Thus,
. Determining
E(L | NC) ≡ 0
E(L) = p(C)E(L | C) p(C) , the probability of a consultation, requires
knowledge on the distribution of pools within critical habitat. Vernal pool density was
calculated using a study by Holland (1996), who performed a complete visual survey in
twenty Central Valley counties for vernal pools using aerial photography. His results
were used to determine average vernal pool density by county. In counties he did not
survey, density was assumed to equal the mean across surveyed counties, six percent.
It is now necessary to allocate this growth within the census tract. This is an
important leap over assuming growth will occur uniformly across the landscape. The
hydrographic and edaphic features of vernal pools may cause land to be unsuitable for
development, preventing conserved habitat from interfering with planned development
and resulting in no added cost. Conversely, conserved habitat may occupy the last
portions of undeveloped land within a tract, meaning future development will be
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shoehorned towards vernal pools. These scenarios illustrate the need for more precise
growth allocation.
This allocation was performed using the California Urban and Biodiversity
Analysis (CURBA) model developed by Landis and Reilly (2003). CURBA is a
statistical model that incorporates both spatial and nonspatial data to project urban growth
in California. Its explanatory variables include demand variables, pertaining to job
accessibility and income level; location-specific variables, such as freeway proximity,
whether the land is classified as farmland, and whether it lies in a flood-plane;
neighborhood variables, modeling the geography of a location’s neighbors; and
regulatory variables, such as whether a location is in an incorporated city.
CURBA analyses the state by dividing it into a matrix of one-hectare grid cells. It
outputs a probabilistic score that a given cell will be converted from undeveloped to
developed in the next twenty years. Let G be the total amount of projected greenfield
development, defined above, and define the CURBA prediction function
C :{1,K ,n}→ 0,1⎡⎣ ) mapping each cell to its respective probability of development. The
analysis assumes the following identity holds:
G = λ C(i)i=1
n∑
Thus, the sum of probability scores within each census tract, scaled by a fixed multiplier,
is identically equal to the total projected greenfield development for that tract. Now solve
for λ and let the sets and be those cells that fall in group A and B habitat. Then
the expected development in group A habitat is given by
H A HB
20
GA = λ C( j)j∈HA
∑ ,
with G defined similarly. B
Results
Results of the analysis are shown Table 4. Federal agency efforts to conserve
vernal pools result in approximately $890 million in lost welfare within the study area
under the rationing scenario, and $240 million if developers are permitted to increase
density in response to federal action. A total of $119 million in costs are due to mitigation
requirements, and the remainder is due to the requirement that project developers avoid
portions of the project site.
The county-level summaries mask the considerable heterogeneity in impacts
across the study area. When costs are aggregated by census tract, the finest level available
at this stage in the analysis, the results are striking. Figure 4 shows overlaid Lorenz
curves for the two scenarios. The x-axis contains the cumulative percentage of critical
habitat contained in each census tract, and the y-axis contains the cumulative percentage
of cost. In the densification scenario, nearly 70% of the cost of federal agency action
stems from roughly 20% of the affected land area. Results are even more varied in the
rationing scenario, where 80% of the costs relate to protection of 10% of the habitat.
Carrying this logic a step further, we examine individual examples of census
tracts in the study area to demonstrate that the most expensive protected habitat may also
be the least desirable, at it usually surrounds urban fringes. Table 5 displays results for
the top five most expensive Census tracts in the study. The single most expensive tract, in
21
Sacramento County, costs $423 million under rationing ($62 million if densification),
which is almost half the total cost of the proposed regulation. Figure 5 shows a map of
this tract, along with satellite imagery taken in 2005. Development and grading
preparations are readily visible, and urban expansion appears primed to target the area set
aside as critical habitat, which is marked with slash lines. This is an excellent example of
how a microdata approach can be used to identify areas with high economic costs.
One of the least expensive census tracts in the study is 06013304000, in Contra
Costa County. Satellite images of the area, from 2005, are shown in Figure 6, with
critical habitat overlaid with slash lines. This example illustrates how terrain informs the
results of the model. Undeveloped land in the western portion of the tract costs little to
designation since it borders farmland and is hilly, both of which make development more
difficult.
An additional metric that can be used to analyze the effects of federal land
conservation efforts is cost per acre of habitat. Table 6 displays the same results as Table
5, except that tracts have been sorted on this basis. The census tract with the highest cost
per acre, in Solano County, results in $3.9 million in losses ($757,000 in the densification
scenario) for 28.5 acres of designated, group B habitat. The cost of the designation in this
tract approaches $135,276 per acre of vernal pools. A map of this tract is shown in Figure
7. The only undeveloped parcel in the census tract is that which was conserved; all
remaining land has been densely urbanized. Not only does this result in large producer
quasi-rents in the case of development, by it may also be of only marginal ecological
value for preserving the listed species.
22
V. Conclusions
Federal land use control is extensive and increasing, particularly since federal
agencies have been so successful in controlling point source pollution discharges.
Dealing realistically with the remaining sources of pollution, and with other
environmental problems such as protection of biodiversity, requires addressing land use.
The paper considers the economic costs of federal land use regulation in a legal system
that gives primary regulatory authority over land use to state and local governments. The
conceptual model shows how local regulations can influence the impacts of federal
interventions, and develops expressions for welfare losses. The model also incorporates
basic elements of federal permits, distinguishing between avoidance and mitigation
requirements, which can have quite different welfare implications.
The paper applies this framework to conservation of vernal pools in California.
Considering both avoidance and offsite mitigation requirements, we show that the
potential welfare costs of protecting vernal pools can run to $890 million in present value
terms. This cost is unevenly distributed across the landscape, with over 80% of the total
welfare cost associated with protecting roughly 10% of the existing vernal pool habitat in
the baseline rationing scenario. This finding suggests that alternative interventions may
better balance economic welfare with environmental protection.
One way in which federal regulators could tailor their habitat protection efforts to
local land market conditions is by altering the mix of avoidance and mitigation
requirements according to local market conditions. With respect to vernal pools, the
analysis has shown how the presence of group B habitat, and the consequent requirement 23
to avoid areas that would have otherwise been developed, causes costs to increase by a
factor of roughly three over the mitigation expenditures that would have been required
otherwise. There are other reasons why mitigation is desirable as well. It streamlines the
permitting process by affording developers a ready-made stock of habitat, obviating the
costly and time-consuming process of creating it on a project-by-project basis. This
would also seem to generate efficiency gains—although the effects of time delay have
not been considered in this paper, they are judged to be considerable since surplus gains
from housing are large, and development requires an outlay of fixed assets which cannot
be shifted as permitting proceeds.
Another major funding of the paper is that the welfare cost of federal agency
action depends heavily on the nature of local regulation, and how local governments
respond to federal intervention. When the housing supply is limited by pre-existing local
regulation, the welfare cost of additional federal regulation can be large. Conversely,
local governments can reduce the cost of federal action by relaxing or altering zoning and
other regulations. We consider the case in which local governments accommodate federal
protection efforts by allowing increases in the density of development. If federal
regulation reduces the stock of developable land by imposing avoidance requirements,
then losses per-acre are equal to the extensive margin value of land, which incorporates
the associated shadow price of new housing. If density is allowed to adjust to keep the
number of housing units constructed at its status quo level, then the per-acre loss from the
avoidance requirement is equal to the intensive margin value of land. This result follows
from the fact that when the density constraint binds from below, homebuyers are forced
to consume too much land in equilibrium. Relaxing this constraint is one way to
24
accommodate the federal government’s desire to protect habitat and other landscape
amenities.
25
0
5.0e-07
1.0e-06
1.5e-06
2.0e-06D
ensi
ty
0 500000 1000000 1500000 2000000Sale price
Figure 1: Histogram of prices
26
0
2.0e-04
4.0e-04
6.0e-04
8.0e-04
Den
sity
1000 2000 3000 4000House sqft.
Figure 2: Histogram of house sizes
27
0
5.0e-05
1.0e-04
1.5e-04
2.0e-04
2.5e-04
Den
sity
0 5000 10000 15000 20000Lot size
Figure 3: Histogram of lot size
28
Table 1: DataQuick Database
Variable x σ x N p25 p50 p75
Price 615,063 280,800 227,752 427,554 550,174 728,608 Bedrooms 3 1 167,878 3 4 4
Bathrooms 2.5 0.5 165,277 2.0 2.5 3.0
Lot Size 7,252 2,824 175,708 5,460 6,969 8,276
Square Feet 2,150 611 222,516 1,685 2,077 2,555
29
Table 2: Extensive Margin Values
County x σ x N p25 p50 p75 Alameda 165.1 72.0 6,192 110.4 165.5 212.7Amador 44.3 16.4 110 35.3 42.2 51.5
Butte 42.5 16.0 322 34.3 44.3 51.6
Calaveras 53.8 31.0 141 31.3 56.7 71.4
Contra Costa 91.5 46.5 4,172 63.1 81.0 109.0
Fresno 39.3 14.9 4,763 30.3 38.4 46.8
Lake 27.4 17.2 5 14.9 29.2 31.4
Lassen 23.0 5.0 54 19.5 22.3 25.8
Madera 30.5 12.5 256 24.8 32.2 37.6
Merced 41.1 10.8 155 33.6 40.5 47.2
Monterey 52.5 19.9 4 39.8 45.5 65.2
Napa 132.1 49.1 210 105.4 127.7 155.8
Placer 72.8 33.8 1,049 57.2 68.3 83.3
Sacramento 60.3 23.9 21,099 48.7 58.7 68.7
San Joaquin 67.2 25.3 14,780 51.6 64.0 81.4
Santa Barbara 60.5 41.2 7 34.8 44.6 87.0
Siskiyou 24.7 11.0 25 19.2 22.3 25.6
Solano 83.6 44.9 4,521 60.9 76.5 96.3
Stanislaus 48.3 29.3 282 34.1 43.6 60.3
Ventura 112.8 43.9 10,906 87.4 108.4 133.4
Total 81.48 50.25 69,053 51.54 67.64 97.44
Note: All figures in 2005$/square feet except N.
30
31
1
Table 3: Intensive Margin Values
MSA β 1
ˆβσ N 2 R Bakersfield 7.65 0.30 6,816 0.85
Fresno 3.60 0.33 4,950 0.87
Modesto 3.03 1.79 452 0.83
Oakland 19.57 0.49 10,374 0.87
Riverside-San Bernardino 6.99 0.19 37,167 0.75
Sacramento 9.06 0.16 24,477 0.84
San Jose 16.63 0.67 5,509 0.83
Santa Rosa 17.67 0.49 4,660 0.86
Stockton-Lodi 6.45 0.24 14,747 0.82
Vallejo-Fairfield-Napa 9.15 0.42 4,719 0.88
Ventura 13.95 0.54 6,438 0.83
Note: 2005$/ft2
Table 4: Model Results - Overall
Acres of Development in Welfare Decrease
County New Houses Group A Group B Mitigation Cost Rationing Densification
Alameda 4,904 42 0 536 536 536
Amador 679 0 2 4 143 38
Butte 18,281 239 2,102 8,055 163,387 47,579
Calaveras 28 0 0 1 3 1
Colusa 1,923 0 0 0 0 0
Contra Costa 12,559 20 23 31 460 94
Fresno 24,807 795 0 1,222 1,222 1,222
Glenn 1,213 3 0 7 7 7
Kings 140 1 0 2 2 2
Lake 1,487 41 0 515 515 515
Lassen 460 0 0 0 0 0
Madera 12,751 1,417 0 19,034 19,034 19,034
Mariposa 586 1 0 8 8 8
Merced 4,316 68 236 5,763 21,574 13,048
Modoc 269 0 0 4 4 4
Monterey 3,651 107 0 1,356 1,356 1,356
Napa 758 0 0 2 138 12
Placer 29,801 2,338 0 32,809 32,809 32,809
32
Acres of Development in Welfare Decrease
County New Houses Group A Group B Mitigation Cost Rationing Densification
Plumas 448 1 0 15 15 15
Sacramento 60,901 319 1,885 25,193 490,318 80,722
San Benito 905 35 0 441 441 441
San Joaquin 1,684 9 0 79 79 79
San Luis Obispo 8,525 344 0 4,373 4,373 4,373
Santa Barbara 1,933 19 0 247 247 247
Shasta 13,562 591 0 1,225 1,225 1,225 Solano 14,257 485 691 9,133 131,075 25,291
Stanislaus 6,460 2 180 756 13,910 4,020
Tehama 9,142 597 0 7,584 7,584 7,584
Tulare 5,777 102 0 267 267 267
Yuba 888 9 0 111 111 111
Total 243,094 7,583 5,120 118,771 890,841 240,639
Note: Cost figures in thousands of 2005$.
33
Table 5: Top 5 Census Tracts (Overall)
Acres of Development In Welfare Decrease
County New Houses Group A Group B Mitigation Cost Rationing Densification
Sacramento 37,498 149 1,527 16,604 422,551 61,583
Butte 7,032 89 1,399 4,540 140,941 30,841
Solano 2,110 27 305 1,326 57,545 8,474
Solano 5,186 268 200 4,478 38,291 9,117
Sacramento 5,477 0 186 1,387 34,364 6,855
Note: Cost figures in thousands of 2005$.
34
35
Table 6: Top 5 Census Tracts (Per Acre)
Acres of Development In Cost Per Designated Acre
County New Houses Group A Group B Mitigation Cost Rationing Densification
Solano 451 0 29 88 135,276 26,550
Butte 463 0 20 49 88,444 21,303
Butte 3,927 0 234 586 66,528 21,303
Sacramento 37,498 149 1,527 16,604 60,152 8,767
Butte 7,032 89 1,399 4,540 52,410 11,468
Note: Costs in 2005$.
0
.2
.4
.6
.8
1C
umul
ativ
e %
of I
mpa
cts
0 .2 .4 .6 .8 1Cumulative % of Critical Habitat
RationingDensification
Lorenz Curve
Figure 4: Lorenz Curve
36
Figure 5: Census Tract 06067008701
37
Figure 6: Census Tract 06013304000
38
Figure 7: Census Tract 06095252502
39
References
Ackerman, B. and R. Stewart. “Reforming Environmental Law.” Stanford Law Review
(1985): 1333-1365.
Crane, R. et al. California Travel Trends and Demographics Study. Institute of
Transportation Studies, UCLA, 2002.
Dwyer, J. “The Pathology of Symbolic Legislation.” Ecology Law Quarterly (1990): 233-
280.
Environmental Defense. Progress on the Back Forty: An Analysis of Three Incentive-
Based Approaches to Endangered Species Conservation on Private Land, 2000.
Fischel, W. The Economics of Zoning Laws: A Property Rights Approach to American
Land Use Controls. Baltimore and London: Johns Hopkins University Press,
1985.
Freeman, A. M. The Measurement of Environmental and Resource Values. Washington,
DC: Resources for the Future Press, 2003.
Glaeser, E. and J. Gyourko. “The Impacts of Building Restrictions on Housing
Affordability.” Federal Reserve Board of New York Economic Policy Review,
2003.
Glaeser, E., J. Gyourko and R. Saks. “Why Have Housing Prices Gone Up?” American
Economic Review (2005): 329-333.
Heller, M. “The Tragedy of the Anticommons: Property in the Transition from Marx to
Markets.” Harvard Law Review (1998): 621-688.
40
Heller, M. and R. Eisenberg. “Can Patents Deter Innovation? The Anticommons in
Biomedical Research.” Science (1998): 698-701.
Holland, R.F. “Great Valley Vernal Pool Distribution, Photorevised 1996.” In C.W.
Witham, E.T. Bauder, D. Belk, W.R. Ferren, Jr., and R. Ornduff, eds. Ecology,
Conservation, and Management of Vernal Pool Ecosystem: Proceedings from the
1996 California Native Plant Society Conference. Sacramento, CA, 1998.
Landis, J. “CUF, CUF II, and CURBA: A Family of Spatially-Explicit Urban Growth
and Land Use Policy Simulation Models.” In R. Brail and R. Klosterman, eds.,
Planning Support Systems. Redlands, CA: ESRI Press, 2001.
Landis, J. and M. Reilly. "How We Will Grow: Baseline Projections of the Growth of
California's Urban Footprint through the Year 2100.” UC Berkeley Institute of
Urban & Regional Development Working Paper Series. Paper WP-2003-04.
http://repositories.cdlib.org/iurd/wps/WP-2003-04
National Research Council. Compensating for Wetland Losses under the Clean Water
Act. Washington, DC: National Academies Press, 2001.
Pederson, W. “Using Federal Environmental Regulation s to Bargain for Private Land
Use Control.” Yale Journal on Regulation (2004): 3-64.
Quigley, J. and A. Swoboda. “The Urban Impacts of the Endangered Species Act: A
General Equilibrium Analysis” Journal of Urban Economics (2007): 299-318.
Sunding, D. and D. Zilberman. “The Economics of Environmental Regulation by
Licensing: Observations on Recent Changes to the Federal Wetland Permitting
Program.” Natural Resources Journal (2002): 59-90.
41
Sunstein, C. Free Markets and Social Justice New York: Oxford University Press, 1997.
Thompson, B., “The Endangered Species Act: A Case Study in Takings & Incentives.”
Stanford Law Review (1997): 305-380.
US Environmental Protection Agency. National Water Quality Inventory. Washington,
DC, 2000.
U.S. Fish & Wildlife Service. Recovery Plan for Vernal Pool Ecosystems of California
and Southern Oregon. Sacramento, CA, December 15, 2005.
42