Download Download Second Paper

Optimal Real Estate Capital Durability and Localized Climate Change Disaster Risk
Devin Bunten
UCLA
Matthew E. Kahn
UCLA and USC and NBER
November 8th 2015
Abstract
The durability of the real estate capital stock could hinder climate change adaptation because
past construction in beautiful but increasingly risky coastal areas anchors the population. But,
coastal developers anticipate that their asset also faces more risk and this creates an incentive to
seek adaptation strategies ranging from; self protection, to reducing capital durability to seeking
an exit option. The option value offered by short lived capital is greater if the volatility of local
shocks increases over time. Climate change is likely to increase such volatility. Building on
past work that has studied the consequences of persistent local labor demand declines, this paper
studies how persistent local new climate risks impact the real estate investor’s joint decision of
locational choice, capital durability and maintenance.
This paper incorporates ideas from our 2014 NBER working paper titled; “The Impact of
Expected Fat Tail Climate Risk on Urban Quality of Life and Real Estate Price Dynamics”.
Kahn thanks NBER Dinner Participants at the March 2015 Energy and Environmental
Economics Dinner for their comments and concerns. We thank Jiwei Zhang for excellent
research assistance.
Introduction
A majority of the United States population and an even larger share of the nation’s
income lives within 80 kilometers of coast (Rappaport and Sachs 2003). Many of the nation’s
most beautiful and productive cities are located along the ocean and river coasts. New York
City, Miami, Seattle, Washington DC, San Francisco and Los Angeles are prime examples. As
sea level rise takes place, could the value of real estate capital be severely impacted?
Miami provides a salient example. This metro area is home to six million people. The
city is located six feet above sea level. In summer 2013, Rolling Stone magazine published a
long front page article claiming that Miami is doomed because of imminent sea level rise.1
Hedonic real estate pricing studies have measured the price discount for housing facing greater
flood risk (Bin, Crawford, Kruse and Landry 2008 and Bin and Landry 2013). In coastal cities,
there are thousands of durable real estate structures built at different times in the past. These
could be stranded assets if climate change decimates specific geographic areas.
The potential for durable housing to slow down spatial reallocations of labor and capital
away from declining areas is highlighted by Glaeser and Gyourko (2005). They study the
implications of urban decline in a world featuring durable housing. Consider the case of Detroit.
This city experienced a large buildup of durable housing in the 1950s and 1960s as the demand
for cars produced in Detroit boomed. Starting in the 1970s, Detroit lost market share in the car
industry. As local housing demand declined, the inelastic housing stock meant that home prices
fell sharply. The large quantity of cheap housing anchored a large number of people with little
labor market attachment to remain in the area. The very existence of the durable capital slowed
down the out-migration to more productive areas. As Detroit’s housing stock depreciated in
value, the city became a poverty magnet as the poor sought out low rent areas.
Does Detroit’s decline caused in part by durable capital foreshadow Miami’s future in the
face of climate change? Substitute the words “declining local labor demand in Rust Belt
manufacturing cities” for “increased risk and declining quality of life in coastal cities” and the
“By century's end, rising sea levels will turn the nation's urban fantasyland into an American Atlantis. But long
before the city is completely underwater, chaos will begin” (see http://www.rollingstone.com/politics/news/whythe-city-of-miami-is-doomed-to-drown-20130620).
1
exact same mechanism could play out again. Coastal areas have had the advantage of enjoying
high exogenous levels of natural amenities (i.e temperate climate, beautiful views). They, or at
least a subset of their neighborhoods, now face the emerging challenge of sea level rise.
This paper studies how localized climate change risk affects local real estate investment.
Forward looking real estate investors face choices over constructing new real estate and
investing in maintaining existing real estate. We seek to understand how these choices are
affected by expectations of evolving local climate change risk. If less housing is built in risky
areas, then fewer people will live there. If existing housing is depreciating faster in risky areas,
because investors are not spending money on maintenance, then poor people will live there.
Thus, real estate capital investment in climate change affected areas determines the economic
incidence of climate change and has key implications of how we collectively adapt to the
emerging climate change challenge.
We study the investment and valuation of durable real estate capital for capital built in
different cities that face differential climate risks. We contrast three case. The first is the
business as usual case in which real estate capital lasts for fixed number of years in the future. In
the second case, we study the optimal durability of the capital stock when it faces new
destructive and localized risk. The third capital stock is “Lego” which for a cost can be
disassembled and dragged to another location on “higher ground” and re-assembled there.
To
appreciate the possible adaptation benefits of such a capital stock imagine an extreme case in
which home owners could costlessly carry their home to another location when a short term
threat (such as a flood) emerges. In such a “turtle” economy, neither life nor capital (i.e the
turtle and its shell) would be destroyed in natural disasters. The capital would move to higher
ground for a short time (and rents for this land would be paid) and then the capital would move
back to its original location. In this age of smart phones and mandatory evacuations, more
people can move to higher ground during disasters and thus the death toll from disasters is likely
to decline but durable capital is stuck in place and thus is at risk to be destroyed by the natural
disasters (Kahn 2005, Changon et. al. 2000).
For each of these capital stocks, we study how rising climate change risks impacts the
capital stock’s optimal life span, valuation, and investor maintenance. Aggregating from such
micro investment decisions yields an aggregate quantity and quality of real estate supply
function for coastal cities. This in turn plays a key role in determining the count of people who
are exposed to climate risk and their quality of housing. Intuitively, if capitalists drag all of their
capital away from coastal cities and there is no new construction, then nobody is exposed to the
flood risk.
In studying how real estate construction and maintenance responds to local
fundamentals, our paper builds on the housing supply literature including the within city filtering
literature (Sweeney 1974, Bond and Coulson 2008, Brueckner and Rosenthal 2009) and the
cross-city durability literature (Glaeser and Gyourko 2005).
Durable Coastal Real Estate Capital
In this paper, we abstract away from two potential major challenges caused by coastal sea
level rise. First, we do not study deaths from natural disasters. As documented by Kahn (2005),
richer nations suffer fewer deaths than poorer nations. The combination of rising per-capita
income and increased access to smart phones and disaster alerts are predicted to sharply lower
future deaths from coastal climate change incidents. Second, we do not discuss how the
productivity of the macro economy is affected by migration away from the coasts. Desmet,
Nagy and Rossi-Hansberg (2015) have studied this issue and conclude that coastal flooding
could severely impact local productivity. It is relevant to note that their model does not include
human capital as a productive local input. Instead, productivity is higher in places where
population density is higher and since the coastal areas are more densely populated, productivity
is higher there and thus in their model sea level rise causes productivity risk.2
Taking these two points as given, we will argue that the main likely future loss imposed
by coastal shocks is lost coastal real estate capital. At a point in time in a given city, real estate
developers have made irreversible investments in long lived durable real estate capital.
2
In a human capital based model of urban growth, firms can move away from coastal areas to
“higher ground” and enjoy human capital spillovers at their new location (Glaeser et. al. 1995).
Essentially, Wall Street could reconstitute elsewhere and replicate the productivity gains that
Wall Street currently enjoys. Urban economics research tends to emphasize the role that human
capital plays in urban growth. Such a person based, rather than a place based, explanation for
urban growth suggests a certain optimism that coastal productive agglomerations (such as Wall
Street) can move to “higher ground”. For example Wall Street can reconstitute in the
Connecticut suburbs as leading firms such as Goldman Sachs lead and other firms follow.
To simplify the discussion let each city be defined by a single attribute namely whether it
is located on a coast or not. To further simplify our analysis, we will assume that inland cities
are unaffected by climate change.3 Coastal cities are more beautiful and feature a more
temperate climate than inland cities. Assuming that all households face zero migration costs and
have the same preferences over consumption, housing quality and spatial attributes, there will be
a rent premium for the coastal cities that traces out the representative agent’s indifference curve.
Climate change represents “new news” that will differentially affect coastal and inland cities.
Optimal Real Estate Capital Durability in Coastal and Inland Cities
Greater durability costs more to build but the average cost per year of a building’s life is
falling in some range. The benefit of building a durable building is that the developer collects a
rental flow for the structure for more years but this rental flow declines as the building ages
because the building depreciates in quality. Define r as the constant economy wide interest rate
and δ as the annual probability that the capital is intact and can be rented out. Define cost(T) as
the cost of constructing a building that can last for at most T periods. Cost is assumed to be a
convex function of the life of the building and acquiring land. The timing of this investment
game is the following; the developer invests in a building of lifespan T years. Each year there is
an independent random variable that is drawn. Each year there is a probability 1-δ that the
building is destroyed and the developer receives no revenue from the building from that period
forward. If this event does not occur then the developer receives the annual rent and a new draw
from this distribution (the climate shock) is taken the next year.
Given this notation, the risk neutral developer choosing to build one house of a fixed size
chooses an optimal durability of capital to maximize the present discounted value of profit:
Albouy et. al. (2015) present a hedonic approach for relaxing this assumption. They model
climate change as shifting a city such as Denver concerning where it is located in amenity space.
For example, if Denver’s winter temperature rises from 30 degrees to 36 degrees and if the real
estate hedonic gradient is stable over time, then one can calculate how much Denver’s climate
amenity bundle will change (measured in $) due to climate change.
3
𝑇
∑ 𝛿𝑗 ∗
𝑗=0
𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑎𝑔𝑒)
− 𝑐𝑜𝑠𝑡(𝑇)
(1 + 𝑟)𝑗
(1)
At the, optimum equation (2) holds:
∆𝑃𝑟𝑜𝑓𝑖𝑡(𝑇) = 𝑃𝑟𝑜𝑓𝑖𝑡(𝑇 + 1) − 𝑃𝑟𝑜𝑓𝑖𝑡(𝑇)
𝛿 𝑇+1
= 𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑇 + 1) ∙ (
)
+ 𝑐𝑜𝑠𝑡(𝑇) − 𝑐𝑜𝑠𝑡(𝑇 + 1)
1+𝑟
(2)
This analysis assumes that real estate durability is a choice variable. Investors choose what
durables to install and the quality of equipment and materials built into a home. A building
sciences literature investigates these issues (see Chapman and Izzo 2002, Noguchi 2003).
Introducing Climate Change Risk
All coastal real estate developers realize that climate change risk is greater in coastal
cities. To simply model this process, we assume that climate change reduces the annual
probability that the property is not destroyed from δ to π where π<δ. After climate change; the
optimal investment problem for a developer can now be written as:
𝑇
∑ 𝜋𝑗 ∗
𝑗=0
𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑎𝑔𝑒)
− 𝑐𝑜𝑠𝑡(𝑇)
(1 + 𝑟)𝑗
(3)
The new optimality condition for capital durability is presented in equation (4):
∆𝑃𝑟𝑜𝑓𝑖𝑡(𝑇) = 𝑃𝑟𝑜𝑓𝑖𝑡(𝑇 + 1) − 𝑃𝑟𝑜𝑓𝑖𝑡(𝑇) = 𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑇 + 1) ∙ (
𝜋 𝑇+1
)
+ 𝑐𝑜𝑠𝑡(𝑇) − 𝑐𝑜𝑠𝑡(𝑇 + 1)
1+𝑟
(4)
Comparing equation (4) to equation (2), we immediately see that the developer will build
less durable capital in the coastal area after climate change risk emerges. By building less
durable capital, the land owner holds an option to re-optimize in the future. The sunk cost is
lower when the structure is less durable. It is important to note that we have modeled climate
change as having no impact on the coastal amenity and thus no negative effect on the rental
payments conditional that the building is not destroyed. This means that climate change has no
impact on the equilibrium rental differential between coastal and inland real estate. By building
capital more often, the average cost of coastal housing will rise and this lowers developer profit.
In this first climate change model, we assumed that every developer has rational
expectations and perfect foresight. An alternative way of modeling uncertainty is to follow
Hansen and Sargent (2011) and introduce robust decision rules. If the developers in the coastal
areas know that they do not know the future probabilities of property destruction, then those
engaging in robust mini-max decision rules (seeking to reduce their losses in the worst states of
the world while facing ambiguous risk) would be likely to build even more short lived capital in
the coastal areas.
Self Protection
In the previous sections, we have assumed that there are no self protection investments
that a coastal real estate owner can engage in to reduce exposure to capital destruction risk. Yet,
along coastal areas we observe home owners place their homes on stilts. In Bangladesh, and
Holland, there are floating structures (Ritzema 2008). We now follow Ehrlich and Becker
(1972) and allow coastal real estate owners to invest in costly self protection. For a one time
expenditure equal to c, they can reduce their risk exposure each period.
In the coastal area, the investor’s decision problem is now to choose;
𝑇
∑ 𝜋(𝑐)𝑗 ∗
𝑗=0
𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑎𝑔𝑒)
− 𝑐𝑜𝑠𝑡(𝑇) − 𝑐
(1 + 𝑟)𝑗
This ability to offset coastal risk in the limit will mean that climate change’s impact will have no
effect on destruction probabilities, i.e π converges to δ. In this case, the optimal durability will
converge back to the “no climate change case”. In intermediate cases where it is too costly to
completely offset climate change, the real estate owner will still choose a more durable capital
stock than in the case with no self protection. In this sense, ex-ante investment in self protection
substitutes for ex-post insurance (i.e having less durable capital).
The Quantity and Durability of Housing
We now relax the assumption that the developer builds one unit of housing. In this case,
the developer is building an apartment building and must choose how many units to build and the
durability of the building. Define N as the number of units in the building. The risk neutral
developer chooses the durability and the size of the building to maximize the expected present
discounted value of profit:
𝑇
∑ 𝛿𝑗 ∗
𝑗=0
𝑁 ∗ 𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑎𝑔𝑒)
− 𝑐𝑜𝑠𝑡(𝑇, 𝑁)
(1 + 𝑟)𝑗
(5)
Under the assumption that the cost function is convex in N, there is an interior solution
satisfying;
𝑇
∑ 𝛿𝑗 ∗
𝑗=0
𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑎𝑔𝑒) 𝜕𝑐𝑜𝑠𝑡(𝑇, 𝑁)
=
(1 + 𝑟)𝑗
𝜕𝑁
If climate change shrinks the probability that the property survives each year, then the developer
will build fewer units in the coastal areas and continue to build less durable capital.4 By
reducing the amount of sunk capital at risk to be taxed by “Mother Nature”, the investor frees up
resources to invest in safer assets (such as housing on higher ground inland areas).
In this section, we have abstracted away from general equilibrium impacts on rents in the
coastal area from such supply contractions because we have assumed that the coasts face no
amenity risk. Residents of such areas face no death risk or amenity risk. Only the owner of the
real estate capital is exposed to disaster risk. We have also assumed that the developer cannot
raise her profit by substituting and only building housing in the inland cities.
4
We are assuming that the cross-derivative of the cost function either equals zero or is positive.
Endogenous Depreciation
Now assume that the developer can pay a fee of M (age) to offset building depreciation
from aging. This maintenance fee is an increasing function of the building’s age. This
investment does not lengthen the life of the building. In the absence of climate risk, an investor
who owns a building of age j will make this maintenance investment if equation (7) holds.
𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑗 + 1, 𝑚𝑎𝑖𝑛𝑡𝑎𝑖𝑛𝑒𝑑) − 𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑗 + 1, 𝑛𝑜𝑡 𝑚𝑎𝑖𝑛𝑡𝑎𝑖𝑛𝑒𝑑)
∙ 𝛿 ≥ 𝑀(𝑗)
1+𝑟
(7)
The developer will be less likely to pay this maintenance fee in the face of climate change
because the expected revenue from this costly investment declines. This means that the
anticipation of climate change risk will accelerate the depreciation of the quality of the existing
capital stock. Past research has modeled the filtering process such that neighborhoods make a
comeback after older housing is scrapped and replaced with new housing (Brueckner and
Rosenthal 2011, Sweeny 1974, Rosenthal 2000). In the coastal city case, real estate investors
would anticipate the climate change “tax” on new investments and would be less likely to invest
to upgrade the depreciating durable housing.
The Option Value of “Lego” Real Estate
Consider a new type of real estate capital stock that features an explicit option. For
paying a cost of $c dollars, a real estate owner retains the option to disassemble an existing
property and to transfer it to another location. In this section, we analyze how this option affects
a real estate investor’s optimal durability investment and maintenance investment relative to the
case presented in the previous section in which the capital was “stuck” in the coastal city. To fix
ideas, we refer to this section’s capital as “Lego” resembling the children’s building blocks that
can be assembled and disassembled. Engineering work on modular building highlights that this
is a feasible possibility.5 Directed technological change will only improve this technology
(Acemoglu and Linn 2004).
For an existing piece of capital whose maximum lifespan is T and the building is already
of age v located in a coastal area that now faces increased risk of climate change, the owner will
move it to higher ground this period if:
𝑇−𝑣
∑ 𝜋𝑗 ∗
𝑗=0
𝑇−𝑣
𝑟𝑒𝑛𝑡(𝑐𝑜𝑎𝑠𝑡, 𝑎𝑔𝑒)
<∑
(1 + 𝑟) 𝑗
𝑗=0
𝑟𝑒𝑛𝑡(𝑛𝑜𝑡 𝑐𝑜𝑎𝑠𝑡, 𝑎𝑔𝑒)
(1 + 𝑟) 𝑗
− 𝑐 − 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑆𝑎𝑓𝑒 𝐿𝑎𝑛𝑑
(8)
In this equation, the left term is the expected present discounted value of not moving the
property and earning a rental flow for the next T-v years weighted by the probability that the
capital is not destroyed. The right term is the present discounted value of moving the property to
“higher ground” to the safe inland city. The asset owner must pay for new land to “park” the
structure and he must pay the moving cost but he collects the PDV of the inland rental stream.
For an investor who owns a coastal property and chooses to drag it to higher ground, this
investor now holds a vacant plot of coastal land. An augmented version of equation (5) above
should dictate his optimal choice of property size and durability.
For an investor considering building a new structure in the coastal area, the optimal
structure durability and size will be a function of whether the property can be moved in the
future. This option is more valuable if the future fat tail coastal risk is known to be unknown or if
the distribution is known then in the case where there is “fat tail” risk.. The standard logic from
the Dixit and Pindyck (1994) option value model is that there is a value to delaying a decision
until the uncertainty is resolved. In this real estate economy, the uncertainty is never resolved
5
Some examples of relevant websites include:
http://your.kingcounty.gov/solidwaste/greenbuilding/documents/Design_for_Disassemblyguide.pdf, http://www.willscot.com/specialty/retail-commercial
http://nreionline.com/technology/high-rise-debut-modular-construction-poised-take
http://en.wikipedia.org/wiki/Commercial_modular_construction, http://www.modular.org/
https://palomarmodular.wordpress.com/2012/05/08/relocating-a-modular-building/
during the life of the asset but the owner recognizes that he is implicitly insured against
worsening climate risk by being able to exercise his migration option.
Facing the same coastal climate risk 1-π each year, the owner of “Lego” capital will be
more likely to build more durable capital, and a larger structure and to invest more in its
maintenance. The proof of this claim is to note that the expected PDV of marginal revenue from
these investments will be higher than in the case where there is no option to leave. The owner of
the option can always choose not to exercise the option. As the economic returns to durability
and maintenance increase, the coastal Lego capital stock will not filter down the quality
spectrum over time and thus will not become a Detroit poverty trap.
The ability of real estate owners to carry their capital away from the risky area means that
the supply of housing to the risky area is more elastic. Returning to the setup in the Glaeser and
Gyourko (2005) durable housing study, a negative local demand shock translates into sharply
lower home prices when housing is inelasticly supply to an area. But, in this “Lego economy”
featuring a more elastic housing supply, declining housing demand in risky areas leads to less of
an equilibrium decrease in local housing prices as quantity responses help clear the market. In a
model featuring homogeneous beliefs about increasingly risky coastal climate risk, new
construction in the risky areas will drop sharply and the net housing supply will decline leading
to fewer people to live there. Thus, coastal risk increases the population living on “higher
ground”.
Model Extensions
In this section, we present several model extensions that offer promising pathways for
future research.
Risk Perception Heterogeneity
Suppose that the population differs with respect to beliefs about the severity of climate
change damage for the coasts. The existence of “climate deniers” (people who under-estimate
the true probability of devastating coastal events) could increase new housing construction in
risky places. If there are sufficient “climate deniers” and there is a resale capital market then
this group may purchase the capital and choose not to move it. In this case, cities such as Miami
could remain heavily populated even in the Lego economy case.
The insurance industry provides one counter-veiling influence in this case. Capital
owners in coastal areas who seek out insurance would be quoted extremely high insurance
premium prices (assuming the insurance industry is pricing insurance reflecting actual evolving
risk probabilities). In this case, the capital owners may update their subjective probabilities or
respond by building less durable capital. Alternatively, they may choose to purchase less
insurance.
Coastal Housing Demand Heterogeneity
There are at least two different reasons for why people may continue to value living near
the coast even if there were homogenous beliefs about the challenge posed by climate change.
These two explanations include; income variation such that the rich are able to effectively
engage in self protection and location specific social capital.
Income heterogeneity will produce ex ante sorting whereby the rich bid up the price of
Miami real estate due to its high-quality amenities. Richer households will be better able to
insulate themselves from climate shocks. This has been shown at the macro level (Kahn 2005).
Self-protection against the risk of climate change means that richer people face less risk than the
average person (Ehrlich and Becker 1972).
Localized social capital provides a second explanation for why incumbents in coastal
areas may continue to be willing to pay to live there (Glaeser, Sacerdote and Laibson 2001). The
presence of an endogenous moving cost (i.e having made friends in one’s initial area) induces a
wedge between the willingness to pay of the incumbent residents of Miami and those who settle
in an alternative locale. Before settling in cities, two individuals could have identical willingness
to pay for coastal areas but once one settles in Miami and builds a network this individual will
now be willing to pay a premium to live there despite its rising risk. For long term residents of
Miami, they have built up a social network such that if they moved away from the area, they
would be likely to lose this location specific attribute unless the group could co-ordinate their
migration to “higher ground”.
Place Based Disaster National Government Insurance
The federal government explicitly and implicitly subsidizes disaster insurance in coastal
areas. The expectation that government will subsidize coastal defense and pay for ex-post
insurance encourages more people to live in disaster prone areas (Kousky et. al. 2005). In this
sense publicly subsidized coastal protection crowds out private real estate developer self
protection.6 Such federally subsidized public goods and government subsidized insurance
provides an incentive for land owners in coastal areas to build more durable housing, to invest
more in its maintenance and to be less likely to exercise their “Lego” option to leave the areas.
At first glance such spatial subsidies create a spatial moral hazard effect as the federal
government is implicitly subsidizing risk taking by those who choose to live in coastal locations.
But, it is well known in the migration literature that older people and less educated people are
less geographically mobile. This means that this group is at increased risk from place based
shocks. How to protect this group from such shocks without exacerbating moral hazard effects
remains an open policy design question. An open political economy question focuses on the
incentives of place based politicians who govern coastal at risk areas. Their political clout is
likely to be an increasing function of the count of people who live in these areas. This raises the
issue of “human shields”. Are coastal place based politicians rewarded by luring more people to
live in increasingly risky areas because this allows them to attract more federal protection dollars
(see Kousky et. al. 2005)?7
Zoning in Safe Inland Cities
Throughout this paper, we have assumed that the rent in the inland cities is a constant and
this implicitly assumes that all of these areas feature identical amenities and constant returns to
6
See Greg Ip’s Wall Street Journal piece http://www.wsj.com/articles/cities-built-to-endure-
disaster-1444401240.
7
For evidence from North Carolina see http://blog.ucsusa.org/north-carolina-governor-purdue-balks-on-sea-levelrise-science.
scale with respect to the marginal cost of supplying more units. But imagine a case where the
inland cities engage in stringent zoning or feature a topography such that it is difficult to build
there (Saiz 2011). In this case, capitalists who seek to move their capital away from Miami will
face a higher equilibrium land cost for moving their property to the inland cities. If zoning
precludes stacking the Lego piece on top of an existing building, this land cost could be high and
this will slow down the arbitrage process of the capitalist leaving Miami.
Bunten (2015) analyzes the aggregate implications of zoning by productive cities.
Neighborhoods within large metropolitan areas have an incentive to discourage new housing
construction to avoid the localized disamenities of congestion. An unintended consequence of
this NIMBY-ism is that residents and economic activity are deflected to the edge of productive
areas or to less productive areas altogether. A similar dynamic could playout in “safe cities” and
“safer” geographic areas within coastal metropolitan areas. In this case, home prices will be
higher than the marginal cost of constructing new housing because of such a regulation tax
(Glaeser, Gyourko and Saks 2005a, 2005b). Such a spatial price premium in safe places would
discourage adaptation because owners of “Lego Capital” would be less likely to exercise their
migration option.
Expected Immigrant Flows to Coastal Cities and Local Housing Supply
Coastal cities tend to be immigrant cities. Cities such as Los Angeles, Miami and New
York City are well known for their large immigrant shares. This potential for immigration has
implications for housing demand. For example, the Mariel Boatlift brought thousands of Cubans
to Miami and in the short run this raised local rents (Saiz 2013). As documented by Borjas,
Freeman and Katz (1997), immigration to coastal cities is associated with equilibrium flows of
natives of similar skills to other local labor markets. Taking this logic to the climate change risk
case, if new immigrants are moving to coastal cities then even if natives move to “higher
ground”, if enough immigrants are expected then coastal real estate capital may be constructed to
be durable and to be maintained for this group. In this case, such immigrants would find housing
and it would be affordable but risky.
Conclusion
Recent research has examined how specific geographic areas cope with climate shocks.
Examples include; the study of the Dustbowl in the 1930s (Hornbeck 2013), the Ogallala aquifer
(Hornbeck and Keskin 2014), typhoon impacts (Hsiang and Jina 2015). Other studies have
examined population migration responses in the aftermath of natural disasters (Deryugina,
Kawano, and Levitt 2014, Paxson and Rouse 2011).
How a geographic area is affected by natural disasters partially depends on the quantity
and quality of the capital stock placed in such areas. Previous research has not studied how the
durability and quality of capital tied to an area determines how many people are affected by a
disaster.
This paper has argued that this supply of local real estate capital is determined by forward
looking investors. Forward looking investors face the joint decision of investment in durability,
upkeep of an existing piece of capital and the decision of whether to keep the real estate capital
in its current location or to move it to “higher ground”. This exit option becomes increasingly
attractive for owners of coastal property as climate change unfolds. These decisions are
interrelated. If capital owners are aware that they cannot move their capital and that it is at risk,
then they will build less durable capital and maintain it less. Such a depreciating capital stock
will become a poverty magnet as predicted by the filtering hypothesis (Sweeney 1974, Glaeser
and Gyourko 2005).
The anticipation by land owners that climate change poses a coastal challenge leads them
to respond by adjusting durability, maintenance and considering the exit option. This option
becomes more valuable as the uncertainty about the risk increases.
This paper’s framework offers several empirical predictions that merit future research.
First, for geographic areas whose topography and location is such that they face significant risk
of sea level rise, what investments are owners taking to both maintain the property and to reduce
its risk exposure? In such areas, are developers building new properties that could be wiped out
in a significant flood? Or, are moral hazard theorists correct that well intentioned place based
public investments in self protection are crowding out private self protection? An intermediate
strategy would be to purchase coastal homes and convert them into natural wetlands to reduce
coastal flood risk.8 Such a strategy would reduce the count of potential victims, and provide
some coastal protection.
The controversial issue here will be eminent domain and whether the home sellers receive the
current value of their homes or a base year pre-climate change price assessment.
8
References
Acemoglu, Daron and Joshua Linn. Market Size in Innovation: Theory and Evidence from the
Pharmaceutical Industry. Quarterly Journal of Economics, 2004 119(3) 1049-1090.
Albouy, David, Walter F. Graf, Ryan Kellogg, and Hendrik Wolff. Climate amenities, climate
change, and American quality of life. Journal of the Association of Environmental and Resource
Economists, Forthcoming.
Bin, Okmyung, and Craig E. Landry. "Changes in implicit flood risk premiums: Empirical
evidence from the housing market." Journal of Environmental Economics and management 65,
no. 3 (2013): 361-376.
Bin, Okmyung, Thomas W. Crawford, Jamie B. Kruse, and Craig E. Landry. "Viewscapes and
flood hazard: Coastal housing market response to amenities and risk." Land Economics 84, no. 3
(2008): 434-448.
Bond, Eric W., and N. Edward Coulson. "Externalities, filtering, and neighborhood
change." Journal of Urban Economics 26, no. 2 (1989): 231-249.
Borjas, George J., Richard B. Freeman, Lawrence F. Katz, "How much do immigration and trade
affect labor market outcomes?." Brookings papers on economic activity (1997): 1-90.
Brueckner, Jan K., and Stuart S. Rosenthal. "Gentrification and neighborhood housing cycles:
will America's future downtowns be rich?." The Review of Economics and Statistics 91, no. 4
(2009): 725-743.
Bulow, Jeremy. "An economic theory of planned obsolescence." The Quarterly Journal of
Economics (1986): 729-750.
Bunten, Devin. “Is the Rent Too High? Aggregate Implications of Local Land Use Regulation”
UCLA Working Paper 2015.
Changnon, Stanley A., Roger A. Pielke Jr, David Changnon, Richard T. Sylves, and Roger
Pulwarty. "Human Factors Explain the Increased Losses from Weather and Climate
Extremes." Bulletin of the American Meteorological Society 81, no. 3 (2000): 437-442.
Chapman, Robert E., and Christine A. Izzo. Baseline measures for improving housing durability.
US Department of Commerce, Technology Administration, National Institute of Standards and
Technology, 2002.
Cooley, Thomas F., Jeremy Greenwood, and Mehmet Yorukoglu. "The replacement
problem." Journal of Monetary Economics 40, no. 3 (1997): 457-499.
Deryugina, Tatyana, Laura Kawano, and Steven Levitt. The economic impact of hurricane
katrina on its victims: evidence from individual tax returns. No. w20713. National Bureau of
Economic Research, 2014.
Desmet, Klaus, Dávid Krisztián Nagy, and Esteban Rossi-Hansberg. The geography of
development: evaluating migration restrictions and coastal flooding. No. w21087. National
Bureau of Economic Research, 2015.
Dixit, Avinash K., and Robert S. Pindyck. Investment under uncertainty. Princeton university
press, 1994.
Ehrlich, Isaac, and Gary S. Becker. "Market insurance, self-insurance, and self-protection." The
Journal of Political Economy 80, no. 4 (1972): 623-648.
Ellen, Ingrid Gould, and Katherine M. O'Regan. "How low income neighborhoods change:
Entry, exit, and enhancement." Regional Science and Urban Economics 41, no. 2 (2011): 89-97.
Enserink, Bert. "Thinking the unthinkable–the end of the Dutch river dike system? Exploring a
new safety concept for the river management." Journal of Risk Research 7, no. 7-8 (2004): 745757.
Gallagher, Justin, Learning about an Infrequent Event: Evidence from Flood Insurance Take-up
in the US. American Economic Journal: Economic Policy, forthcoming.
Glaeser, Edward L, Joe Gyourko and Raven Saks. 2005. Why is Manhattan So Expensive?
Regulation and the Rise in Housing Prices. Journal of Law and Economics, 48(2) 331-369.
Glaeser, Edward L, Joe Gyourko and Raven Saks. 2005. Why Have Housing Prices Gone Up?
American Economic Review, 95(2) 329-333.
Glaeser, Edward L., JoséA Scheinkman, and Andrei Shleifer. "Economic growth in a crosssection of cities." Journal of monetary economics 36, no. 1 (1995): 117-143.
Glaeser, Edward L., David Laibson, and Bruce Sacerdote. "An economic approach to social
capital." The Economic Journal 112, no. 483 (2002): F437-F458.
Glaeser, Edward L. & Joseph Gyourko, 2005. "Urban Decline and Durable Housing," Journal of
Political Economy, University of Chicago Press, vol. 113(2), pages 345-375, April.
Goolsbee, Austan. "The business cycle, financial performance, and the retirement of capital
goods." Review of Economic Dynamics 1, no. 2 (1998): 474-496.
Guerrieri, Veronica, Daniel Hartley, and Erik Hurst. "Endogenous gentrification and housing
price dynamics." Journal of Public Economics 100 (2013): 45-60.
Hallegatte, Stephane, Colin Green, Robert J. Nicholls, and Jan Corfee-Morlot. "Future flood
losses in major coastal cities." Nature climate change 3, no. 9 (2013): 802-806.
Hansen, Lars Peter, and Thomas J. Sargent. Robustness. Princeton university press, 2008.
Hornbeck, Richard. “The Enduring Impact of the American Dust Bowl: Short and Long-Run
Adjustments to Environmental Catastrophe.”."American Economic Review.
Hornbeck, Richard, and Pinar Keskin. "The historically evolving impact of the ogallala aquifer:
Agricultural adaptation to groundwater and drought." American Economic Journal: Applied
Economics 6, no. 1 (2014): 190-219.
Hsiang, Solomon M., and Amir S. Jina. "Geography, Depreciation, and Growth." American
Economic Review 105, no. 5 (2015): 252-56.
Kahn, Matthew E. "The death toll from natural disasters: the role of income, geography, and
institutions." Review of economics and statistics 87, no. 2 (2005): 271-284.
Kahn, Matthew E. "Urban growth and climate change." Annu. Rev. Resour. Econ. 1, no. 1
(2009): 333-350.
Kousky, Carolyn. "Learning from extreme events: risk perceptions after the flood." Land
Economics 86, no. 3 (2010): 395-422.
Kousky, Carolyn, Erzo FP Luttmer, and Richard J. Zeckhauser. "Private investment and
government protection." Journal of Risk and Uncertainty 33, no. 1-2 (2006): 73-100.
Noguchi, Masa. "The effect of the quality-oriented production approach on the delivery of
prefabricated homes in Japan." Journal of Housing and the Built Environment 18, no. 4 (2003):
353-364.
Paxson, Christina, and Cecilia Elena Rouse. "Returning to new orleans after hurricane
katrina." The American economic review 98, no. 2 (2008): 38.
Rappaport, Jordan, and Jeffrey D. Sachs. "The United States as a coastal nation." Journal of
Economic growth 8, no. 1 (2003): 5-46.
Ritzema, H. P. "Coastal Lowland development: coping with climate change: examples from the
Netherlands." In International workshop on sustainable paddy farming and healthy environment,
Changwon city, Republic of Korea, 30-31 October, 2008, p. 12. 2008.
Roback, Jennifer. "Wages, rents, and the quality of life." The Journal of Political
Economy (1982): 1257-1278.
Rosen, Sherwin. "Wage-based indexes of urban quality of life." Current issues in urban
economics 3 (1979).
Rosen, Sherwin. "Markets and diversity." American Economic Review 92, no. 1 (2002): 1-15.
Rosenthal, Stuart S. "Old homes, externalities, and poor neighborhoods. A model of urban
decline and renewal." Journal of Urban Economics 63, no. 3 (2008): 816-840.
Rosenthal, Stuart S. "Are private markets and filtering a viable source of low-income housing?
Estimates from a “repeat income” model." The American Economic Review 104, no. 2 (2014):
687-706.
Saiz, Albert. "The geographic determinants of housing supply." The Quarterly Journal of
Economics 125, no. 3 (2010): 1253-1296.
Saiz, Albert. "Immigration and housing rents in American cities." Journal of Urban
Economics 61, no. 2 (2007): 345-371.
Saiz, Albert. "Room in the kitchen for the melting pot: Immigration and rental prices." Review of
Economics and Statistics 85, no. 3 (2003): 502-521.
Shogren, Jason, and Tommy Stamland. "Skill and the Value of Life." Journal of Political
Economy 110, no. 5 (2002): 1168-1173.
Shogren, Jason F., and Tommy Stamland. "Self-protection and value of statistical life
estimation." Land Economics 81, no. 1 (2005): 100-113.
Swan, Peter L. "Optimum durability, second-hand markets, and planned obsolescence." The
Journal of Political Economy (1972): 575-585.
Sweeney, James L. "A commodity hierarchy model of the rental housing market." Journal of
Urban Economics 1, no. 3 (1974): 288-323.