Land Erosion and Coastal Home Values
Scott Below, Eli Beracha, and Hilla Skiba
Abstract: This paper studies transaction data on coastal homes in an area where continuous land
erosion has been identified and well-documented. We employ a variety of hedonic regressions to
measure the extent to which coastal land erosion is capitalized into the value of waterfront
residential properties. The empirical findings are consistent with our theoretical model that
illustrates the effect of long run discounting on property values. Our results suggest that the rate
of land erosion negatively affects the value of coastal properties. However, the negative effect of
land erosion on transaction prices is only evident when the ratio of the property’s distance from
the shore to the rate of erosion is sufficiently low. Moreover, we provide evidence that nonlocal
buyers pay a premium for properties compared with local buyers and that levels of income,
education and housing value in the area of the buyers’ permanent home address are all correlated
with the purchase price.
1. Introduction
Coastal regions in the United States are some of the most densely populated and
developed areas in the nation. According to the National Oceanic and Atmospheric
Administration (NOAA), over the last few decades the proportion of the US population that lives
near the coasts grew faster than the population in the remaining areas. In fact, over 50% of the
US population currently lives in coastal areas, that represent just 17% of the land area in the
contiguous US. Since World War II, the rapid influx of people to the coasts has served as an
engine for growth in the coastal economies and has contributed to above average increases in
home prices along both coasts and the Gulf of Mexico.
While many people find coastal areas to be attractive places to live, coastal shorelines,
which are commonly viewed as the most desirable locations, are vulnerable to a variety of
environmental hazards. High-energy storms such as hurricanes and nor'easters, combined with
rising sea levels, threaten many shoreline communities. Catastrophic meteorological events, such
as seen with hurricanes Katrina and Sandy, have been associated with monetary losses reaching
tens of billions of dollars and costing hundreds of human lives.1 In light of these recurring
meteorological catastrophes, decisions regarding whether to rebuild in some of the most severely
damaged areas, such as areas along the Gulf Coast and Jersey Shore, have been complex and
contentious.
This paper examines the extent to which shoreline erosion is being capitalized into the
price of residential coastal real estate. Few papers in the extant literature have studied the price
impact of erosion on real estate, and those that have are typically limited by small and
incomplete datasets. This study contributes to the research on the capitalization of catastrophic
1
The monetary costs of hurricane Katrina and hurricane Sandy were estimated at $81B in 2005 and $68B in 2013,
respectively. The death tolls reported to be associated with each of these hurricanes were 1,833 and 286
respectively.
1
risk into property prices by addressing several of the key shortcomings within the current
literature. First, we address the small sample size problem common in many previous papers by
utilizing a large sample of transactions spanning an extended period of time. Second, we
combine data from a number of sources to create a dataset that includes property level
transaction data, detailed property characteristics, and shoreline erosion data. In addition to
identifying the common property-specific factors that have been shown to impact transaction
prices, we also record the actual distance of each house from the water in feet and match each
property with the estimated shoreline erosion rate at that specific location. Moreover, we utilize
identifying information on the buyers of the properties, including the buyers’ permanent home
addresses, in order to study whether local buyers of the properties in our sample pay a different
price compared with nonlocal buyers. The buyer data also allow us to examine the driving
factors behind any price differential. Fifth, we present a theoretical framework for discounting
near and distant disastrous events. Finally, using the dates of specific named storms, we
investigate whether the capitalization of erosion risk immediately following severe storms varies
from the capitalization of the erosion risk during “normal” times.
All real estate is at risk from natural disasters, but in contrast to events like tornadoes,
earthquakes, and floods, which are indiscriminate and largely unpredictable, beach erosion is an
endless process whose progress can be observed and measured over time. For example, consider
a house located 150 feet from the mean high tide line of a shoreline that is estimated to be
receding at an average rate of five feet per year. Armed with this information, a potential buyer is
able to develop a rough estimate of how long the property could remain habitable (in the absence
of a catastrophic storm and barring artificial measures undertaken by the state, local, or federal
governments to slow the natural erosion process). Exposure to a progressive, continuous erosion
2
process differs significantly from exposure to risky meteorological events that occur
sporadically, with a high degree of uncertainty. Most importantly, estimates of long run shoreline
erosion rates are publicly available by location and agents’ fiduciary responsibility mandates
disclosure of such material information prior to the sale. In addition, insurance premiums may
also signal the risks to which a property is exposed. As a result, we should expect rational market
participants to at least partially capitalize erosion risk into their buying decisions and,
consequently, property prices.
Our findings from transaction level data on coastal homes suggest that land erosion risks
are at least partially capitalized into the prices of waterfront residential properties, and the
empirical findings are consistent with the predictions of our theoretical models. We find that
properties located closer to the ocean within areas exposed to the highest erosion rates are
associated with negative relative price premiums. However, we also find this negative effect to
be statistically significant only if the ratio of the property’s distance from the shoreline to the rate
of erosion (henceforth, the distance-to-erosion ratio) is sufficiently low. In other words, it
appears that erosion risk is not capitalized into property prices until the threat of the property loss
becomes imminent. Additionally, and consistent with the existing literature, we find evidence
that nonlocal homebuyers, on average, pay a price premium for properties compared with local
buyers. Also consistent with the existing literature, we show that the relative price premium
appears to be driven by a price anchoring bias and higher transaction costs. However, we provide
new evidence that buyers’ income and education level are also correlated with the price they pay.
Given that a large share of the US population lives in coastal areas, we believe the results
of this study are important for a large number of homebuyers and real estate investors, as well as
for the various governmental agencies involved in mitigating the impacts of natural disasters.
3
The rest of this paper is organized in the following way. Section 2 motivates our research
by discussing the threats associated with land erosion in more detail and reviewing some of the
relevant existing literature. Section 3 presents our theoretical model and develops the hypotheses.
Section 4 describes our dataset and the methodology used in the empirical analysis. Section 5
presents the results of the analysis and Section 6 concludes.
2. Motivation for the Study, Contribution and Literature Review
2.1. Motivation and Contribution
The barrier islands of the mid-Atlantic coast are unique in that they are made up solely of
sand and clay and are not supported by underlying bedrock. This unique composition, combined
with strong ocean currents and frequent severe storms, means these Outer Banks islands migrate
more than virtually any other coastal area in the United States.2
While it is possible to artificially protect some shoreline areas via periodic beach
nourishment (i.e. placing new sand onto eroded beaches) or by the installation of seawalls or
groins, the costs can be prohibitive. In addition, hardened structures like seawalls and groins
merely shift the erosion issues down the beach to become someone else’s problem. As a result,
installation of hardened structures along the North Carolina coast is illegal.
It is estimated that beach nourishment in North Carolina has cost more than $3.8 million
per mile of shoreline since the nourishment programs began, and North Carolina is not unique in
this regard. For example, The Program for the Study of Developed Shorelines (PSDS) estimates
that New Jersey has spent more than $11 million per mile of shoreline in beach nourishment
2
Riggs, Stanley R., et al, "The Battle for North Carolina's Coast-Evolutionary History, Present Crisis, & Vision for
the Future." The University of North Carolina Press, 2011, pg. 3.
4
programs (pre-hurricane Sandy) and Florida in excess of $4.6 million per mile of shoreline.3
Geologists argue that rising ocean levels will only increase the demand for beach nourishment
programs going forward and that artificially protecting shorelines in this fashion is an
unwinnable battle, in addition to being prohibitively expensive.
This study furthers current knowledge of the capitalization of catastrophic risk into
property prices by addressing several of the shortcomings in the extant literature. Specifically,
we address many of the data limitations common in the previous research by analyzing a large
sample of transactions that span an extended period of time. We are also able to consider each
individual property’s precise distance to the water in combination with the average annual
erosion rate of the shoreline in front of each home in the sample. This two-pronged approach
allows us to isolate the effects of proximity to the water from the effect of erosion rates on
property values.
Due to the topography of the area we explore, we are also able to compare oceanfront
properties to inland and soundfront properties. Like oceanfront properties, soundfront properties
enjoy direct access to the water and waterfront views, but are not subject to the same level of
erosion risk and may even be beneficiaries of accretion. Finally, we are also able to investigate
the extent to which the nonlocal price premium is related to the levels of income and education at
the buyers’ place of origin, in addition to housing price levels that have been considered in the
existing literature.
Source: Program for the Study of Developed Shorelines’ (PSDS) Beach Nourishment Database. All estimates are
in 2012 dollars and based on historical aggregates beginning with the first nourishment event in the dataset, which
took place in the late 1920s.
3
5
2.2. Literature Review
Many existing studies explore the effect of exogenous environmental factors on coastal
real estate valuation. With regard to location, the literature provides consistent evidence that
waterviews materially enhance property values (Kulshreshtha and Gillies 1993; Lansford and
Jones 1995; Benson, Hansen, Schwartz and Smersh 1998; Pompe and Rinehart 1999; Bond,
Seiler and Seiler 2002; Bin, Crawford, Kruse and Landry 2008; Wyman, Hutchinson and Tiwari
2014, among others). On the other hand, studies focusing on coastal flood hazards (Skantz and
Strickland 1987; Harrison, Smersh and Schwartz 2001; Hallstrom and Smith 2005; Bin, Kruse
and Landry 2008; Bin, Crawford, Kruse and Landry 2008, for example) consistently find that
higher susceptibility to temporary flooding or rising sea levels negatively impacts coastal
property values. A recent paper by Turnbull, Zahirovic-Herbert and Mothorpe (2013) also
illustrates that higher flood risk not only affects transaction prices, but also negatively impacts
liquidity.
Some papers have examined the impact of proximity to the water on the price of
waterfront properties, but the evidence so far is conflicting. Studies by Gopalakrishnan, Smith,
Slott and Murray (2010), Landry, Keeler and Kriesel (2003), and Pompe and Rinehart (1995)
find evidence for the capitalization of beach width into property prices. However, the results
from these studies vary widely, with the marginal willingness to pay for a one-foot increase in
beach width ranging from $70 to $8,000. In contrast, a recent study by Ranson (2012) finds that
beach width has little impact on housing prices in general, except in the case of some highly
eroded beaches. The highly variable and dissimilar results of these studies are likely driven, at
least in part, by the difficulty of gathering reliable data on the main factors affecting a buyer’s
willingness to pay for beach width. While some of these studies have precise information on the
6
distance of the properties from the shoreline, they fail to account for differences in coastal
dynamics across locations and properties. In particular, the distance from the shore should be
considered together with the rate at which the shoreline is eroding. The fact that previous
research has not considered shoreline loss rates may partially explain the disparity in the
conclusions of the studies.
Pompe and Rinehart (1999) as well as Landry, Keeler and Kriesel (2003) recognize that
the capitalization of land erosion rates into transaction prices depend on market participants’
knowledge of the coastal dynamics and their expectations regarding future artificial coastal
management actions. However, these studies do not distinguish between participants with
varying levels of information.
Our study also investigates price premiums paid by different buyer types. In previous
papers, Lambson, McQueen and Slade (2004) investigate the price premium paid by nonlocals,
in general, relative to the price paid by local buyers for apartments in the Phoenix area. The
authors conclude that the relative nonlocal price premium is a result of an anchoring bias and
higher search costs. In addition, Hardin and Wolverton (1999) show evidence for a price
premium related to buyer type. Specifically, they show that REITs pay acquisition premiums
over other types of buyers.
The paper also relates to the literature on capitalization of distant outcomes. Prior
literature recognizes that individuals undervalue distant events, such as environmental problems,
with severe but long delayed consequences. Ainslie (1991) and Azfar (1999) apply a hyperbolic
discount curve rather than a constant rate in order to describe rational economic behavior in the
presence of distant outcomes. Azfar (1999) also argues that the undervaluation of distant events
and overvaluation of immediate events is particularly problematic when the level of information
7
about the event is non-static over time. Similarly, other studies from psychology and economics
suggest that individuals discount highly infrequent events with potentially disastrous
consequences in a non-linear manner that marginalizes distant events (Thaler 1981, Loewenstein
1987, Ainslie 1991, Cropper et al. 1994, and Kirby and Herrenstein 1995, among others).
3. Theoretical Framework and Hypothesis Development
We develop a simple theoretical framework that illustrates how the risk of land erosion is
capitalized into the price of residential real estate under the assumption that buyers are, on
average, risk averse. The general framework is applied to buyers with identical knowledge of the
erosion risk associated with residential properties in a given area. This is a reasonable
assumption given the publicly available erosion rate maps produced and periodically updated by
the State of North Carolina. These maps display the average long run erosion rates along the
entire North Carolina coast. To this neutral state we then introduce the possibility of exogenous,
short-term shocks in the form of severe and potentially catastrophic storm events.
Consider a housing unit of quality q that is expected to provide its owner with an annual
utility u(q). If this housing unit is subject to a continuously approaching, destructive, exogenous
event that is expected to occur N years from now, the housing unit quality will be reduced to ̃ at
time N+1. While a distant, potentially catastrophic event will negatively impact the value of
properties subject to the event, stable erosion rates allow buyers to more accurately assess the
risks associated with such destructive events. Therefore, variation of the perceived quality of the
property around ̃ is expected to be negatively related to the stability of the erosion rate. That is,
during normal times, which are associated with general stability in the long run erosion rates,
buyers will infer smaller variations of the perceived quality of the property around ̃. While the
8
estimate of the long run erosion rate is public information, an exogenous shock to the system,
such as an extreme storm event, would disrupt the stability of the erosion rate (without being
destructive to the property). Post-event, the system would gradually stabilize, eventually
reverting to its long run erosion rate equilibrium. In other words, an exogenous shock to the
system will cause the variation in the expectations of the erosion rate to suddenly spike before
reverting to normal. Therefore, during the non-normal period buyers will employ larger variation
assumptions with respect to the perceived quality of property around ̃. Specifically, during
stable erosion rate periods buyers believe that housing quality will drop to
p, where
̃ with probability
̃ with probability (1-p). Therefore, on average,
, and to
during stable times buyers will expect:
̃
̃
̃
(3.1)
On the other hand, during times of unstable erosion rates buyers will believe that housing quality
could drop as far as
̃, where
with probability p, but drop to only
̃ with probability (1-p). Thus, when the erosion rate is unstable buyers will expect:
̃
̃
̃
(3.2)
If we assume buyers are risk averse, during stable erosion rate periods they should expect
to receive the following utility from the housing unit once the destructive event occurs.
̃
(
̃)
(3.3a)
Similarly, during unstable erosion periods buyers expect to receive the following utility
from the housing unit once the destructive event occurs.
̃
(
̃)
9
(3.3b)
However, because the expected utility is increasing with α (or decreasing with the variability in
the perceived ̃):
̃ ̃
̃[
(
̃ ̃
(
̃)
̃
(
̃)]
̃)
̃
(3.4a)
̃
(3.4b)
(3.4c)
Hence, the expected utility during stable erosion rate periods is greater than the expected utility
during unstable erosion rate periods (given risk averse buyers, the sign of the expression in
brackets of equation (3.4c) is positive due to the concavity of the utility function).
To simplify the notation, let u denote utility before the destructive event occurs and ̃
and ̃ denote the expected utility after the occurrence of a destructive event during stable and
̃
unstable erosion rate periods, respectively. Consequently,
̃ . Now consider the
implied value of a housing unit that is expected to be affected by a destructive event N years in
the future, but ultimately survives for T years (unless
, in which case the housing unit is
expected to be completely destroyed by the event with probability p). The value (V) of this
housing unit can then be written as:
̃
= [
]
̃
[
̃
̃
],
(3.5a)
(3.5b)
where r is the discount rate applied to future utility consumption. Depending on location, each
housing unit would have a different N such that housing units closer to (further from) the
shoreline and/or located in high (low) shoreline erosion-rate areas will be associated with a
smaller (larger) value of N. Differentiating equation 3.5b with respect to N gives:
10
̃
(
)
(3.6)
where the sign of equation 3.6 follows because the logarithmic term is negative. Intuitively, this
indicates that the further into the future a destructive event is expected to occur, the higher the
current value of the housing unit will be.
Hypothesis 1: The impact of a possible destructive event on the value of a housing unit is
negatively related to the amount of time until the destructive event is expected to occur.
Moreover, differentiating equation 3.5b a second time with respect to N gives:
̃
[
]
(3.7)
where the sign of equation 3.7 follows because the negative logarithmic term is squared.
Equation 3.7 implies that the value of a housing unit decreases more rapidly over time when N is
small and more slowly when N is large.
Hypothesis 2: The expectation of a destructive event has a negligible impact on the value of
properties for which the event is extremely distant and a material impact on the value of
properties for which the event is less distant.
In order to determine the value of a potentially damaged housing unit during stable and
unstable erosion rate periods we differentiate equation 3.5b with respect to ̃:
̃
[
]
(3.8)
Where the positive sign of equation 3.8 implies that the value of any potentially damaged
housing unit is higher during stable erosion rate periods.
Hypothesis 3: The capitalization of a distant catastrophic event into property prices will
temporarily increase following a severe storm that significantly increases the short run variation
in the shoreline erosion rate.
Following the exogenous shock of a disruptive event, a de-escalation period is required in
order for the variation in the expectations of the land erosion rate to revert to normal. Therefore,
the shorter the interval (I) between disruptive events, the smaller the difference in the variation of
11
the expected erosion rates between stable versus unstable periods, as the system may never fully
stabilize.
(3.9)
Equation 3.9 implies that in areas where disruptive storms occur with high frequency, the
difference between the valuations of properties during stable versus unstable periods will be
indistinguishable.
Hypothesis 4: The gap between the valuation of properties during stable and unstable erosion
rate periods decreases with the interval between disruptive events.
4. Data and Methodology
4.1. Data
The empirical analysis of the paper begins with two major residential real estate datasets
that provide us with both transaction level data and detailed information on individual properties.
We then compute distance to the ocean and the corresponding proximate erosion rate for each
property. Exhibit 1 shows the geographic area of the study, which is Dare County, North
Carolina. Dare County is the easternmost county in North Carolina and boarders on the Atlantic
Ocean. Shoreline erosion has long been measured and recorded in Dare Country along the
entirety of its Atlantic coast. Due to its location and the wealth of historical data on the erosion
rates of the barrier islands, Dare County is uniquely suited for the purposes of our study.
Transactions records for this study are obtained from the Dare County Tax Office and
include 33,790 residential real estate transactions that occurred in the county from 1996 through
2012. For each transaction we are able to observe the transaction date, price, parcel number, and
permanent mailing address of the purchaser, which allows us to distinguish between local and
nonlocal buyers. Since many of the properties in this area serve as vacation homes, the
12
permanent mailing address of the purchaser is, in most cases, different from the address of the
property. We categorize buyers into 4 different groups: 1) local buyers, which are defined as
buyers with a permanent address inside North Carolina, 2) buyers from North Carolina’s two
neighboring coastal states – Virginia and South Carolina, 3) buyers from the South and the
Northeast regions, and 4) buyers from all other origins.4 This classification of buyer origin
allows us to not only test for a possible local vs. nonlocal premium, but also to determine
whether the magnitude of the price premium varies with the buyer’s origin. The variability of the
price premium, in turn, sheds light on the role search costs might play in any nonlocal price
premium. Additionally, we collect zip code level data from the Census of Bureau on the median
value of homes near each buyer’s permanent address, as well as the median level of income and
level of education attainment for that zip code.5 These data enable us to determine whether any
relative local versus nonlocal price premium paid is driven by a price anchoring bias, buyers’
financial strength, or buyers’ ability to collect and process information about the market and the
specific property they purchase.
We then merge the transactions data with a dataset containing physical descriptions of
45,695 unique residential parcels within Dare County. The physical descriptions data are also
from the Dare Country Tax Office and include detailed information on the number of bedrooms
and bathrooms, number of stories, units in the structure, lot size, date of construction, and a
rating of the structure condition (ranging from below average to excellent). We restrict the
merged sample to include only transactions on single-family properties for which the structure
4
As a robustness check we also classified buyers based on whether their permanent address is located within 100
miles from the Atlantic Ocean in North Carolina, in North Carolina’s neighboring states or in other states. The
results of our analysis using these classifications are similar to the classification described in the text. For brevity,
we omit the results of this robustness classification from the text.
5
Education and income data is gathered by the American Community Survey and is based on a 5-year estimate. We
define our education variable as the percentage of population above the age of 25 within each zip code with more
than high school education. Income is defined as income per capita.
13
size, lot size, number of bedrooms and bathrooms, and structure condition are available.
Moreover, because the description of each property’s characteristics and condition are only
available for the most recent transaction, we only include the most recent transaction price for
each property in our analysis. Following the above exclusions, the number of individual
transactions in our final sample is 13,106.6
We then develop the main variables of interest for this study, the distance to the ocean
and the proximate erosion rate. First, Google Earth is used to identify whether each transacted
property is located either inland (11,071 transactions) or waterfront (2,035 transactions).
Next, we use Google Earth to measure and record the shortest physical distance (in feet)
of each waterfront property from the water. Given the physical geography of Dare County, some
of the waterfront properties in the sample face the ocean and some face the sound. Soundfront
properties are similar to oceanfront properties in that both enjoy direct water access and
waterfront views. However, soundfront properties are not exposed to anything approaching the
magnitude of the currents or wave action impacting oceanfront beaches. This means that
shoreline erosion is not a significant concern for the buyers of the soundfront properties. Since
shoreline erosion affects only oceanfront properties, the North Carolina Division of Coastal
Management only records erosion rates for shorelines and inlets adjoining the Atlantic Ocean. As
a result, we further classify our sample of waterfront properties as either oceanfront (854
transactions) or soundfront (1,181 transactions).
Finally, we employ maps from the North Carolina Division of Coastal Management that
detail the long run average annual shoreline erosion rates along the entire North Carolina coast.
6
The exclusion of all but the last transaction of each property from our analysis is particularly important with our
sample due to the possibility that previous transactions have been associated with a different structure, a structure at
a different condition or even an empty lot. The likelihood of each of these possibilities is especially high given the
abnormal rate of construction, restructuring or renovation in areas that are exposed to destructive events and to rapid
growth, in general.
14
These maps are constructed using detailed aerial photographs of the entire North Carolina coast
and then overlaid with the erosion-rate data. They are available to the public at no charge. Using
this information, we are able to identify the exact location of each of the 854 oceanfront
properties on the map and record the rate of shoreline erosion at that location for each property.
Appendix A contains two examples of these maps.
The final compiled dataset includes the property characteristics, proximity to the water,
and information about the quality of the beaches in terms of the estimated erosion rates. To our
knowledge, this dataset is unique in several respects among both the real estate and economics
literatures and enables us to examine the impacts of proximity-to-the-water on property valuation
while also controlling for the risks imposed by shoreline erosion. As a result, we are able to
eliminate some of the noise and biases inherent in previous studies that only consider these
factors in isolation.
It is important to note that even relatively stable long run average shoreline erosion rates
can deviate abruptly due to sudden shocks from meteorological events. For example, it is
possible for a shoreline that has exhibited a relatively stable long run erosion rate of 4 feet
annually to suddenly experience a loss of 50 or 100 feet of beachfront during a hurricane or
nor’easter. Following such catastrophic events, however, it is common for the affected shoreline
to return to its normal long run erosion rate, albeit from a new location further inland. Therefore,
the potential for extreme storm-driven departures from the long run average erosion rate is
important for prospective homebuyers to consider when purchasing a property located near the
water, even if the home is situated adjacent to a shoreline with a relatively low long run erosion
rate.
15
Table 1 provides a set of descriptive statistics for the final merged dataset. Panel A
includes general statistics, including the share of the transactions where the purchaser is local
(from North Carolina) versus nonlocal. Local purchasers make up approximately 45% of the full
sample, and the nonlocals comprise roughly 55%. For soundfront properties, the share of local
buyers remains similar to their representation across the full sample—at about 47%. However,
the share of local buyers falls to just 19% for the oceanfront properties in the sample. This points
to a strong preference for oceanfront properties by nonlocal buyers. Even so, 82.1% of the
nonlocal purchasers in the dataset bought a property that is neither soundfront nor oceanfront,
compared with 87.7% for local purchasers (as is shown in greater detail in panel C).
Panel B includes statistics on the characteristics of the homes included in the sample. The
average lot size is 13,591 square feet, and the average home in the sample has 2.9 baths, 3.7
bedrooms, and sold for a nominal price of just over $330,000. Oceanfront properties are located
315 feet from the water on average, while soundfront properties are located much closer to the
water (an average of just 72 feet). The average long run erosion rate for the oceanfront properties
in the sample is 3.2 feet per year.
Panel C provides descriptive statistics for the buyer characteristics in the sample. The
panel shows that buyers from North Carolina account for 45% of the transactions in the dataset
while 33% of buyers are from the two neighboring coastal states, South Carolina and Virginia.
Of the remaining nonlocal buyers, 9.3% are from the South7, 9.6% from the Northeast, and the
remaining 3% are from the Midwest (1.8%), West (1.0%)8 or outside the United States (0.3%). It
7
Buyers from Florida, a state that is included in the South region, only represent 0.9% of the transactions.
We employ the Census four region segmentation in order to define the South, Northeast, Midwest and West
regions.
8
16
also appears that, on average, nonlocal buyers purchase larger (in terms of number of bedrooms
and bathrooms) and more expensive properties compared with local buyers.9
4.2. Methodology
In order to examine the effects of shoreline erosion rates on property prices, we employ
several different hedonic model specifications, beginning with a standard hedonic regression:
∑
In this equation,
∑
(1)
is the natural logarithm of the transaction price of property i at time t;
physical property attributes; λ and μ are the
are the coefficients of the vector of
coefficients of the indicator variables Inland and Sound which take the value of 1 when a
property is located either inland or soundfront, respectively, and 0 otherwise (oceanfront is the
omitted variable);
are the coefficients of the
year indicators; and
is the random error
with a mean of 0 and variance σ2. The physical attributes of the properties included in vector X
are Bath, Bed and Story, which represent the number of bathrooms, bedrooms and stories of the
property, respectively; LnLotSize and LnAge are the natural logarithms of the lot size (in square
feet) and age of the house (in years), respectively; Condition takes a value between 1 and 4,
reflecting the overall reported condition of the property (1 – below average, 2 – average, 3 –
above average and 4 – excellent). The primary purpose of the hedonic regression presented in
equation (1) is to confirm whether oceanfront properties sell for a premium compared with
soundfront properties and whether inland properties sell for a discount compared with waterfront
properties.
9
The averages of the number of bedrooms, bathrooms and price are statistically different at the 1% level when
properties purchased by local and nonlocal buyers are compared.
17
In order to determine whether buyers are willing to pay a premium for properties located
closer to the water, we estimate the following regression equation for all waterfront properties, as
well as for oceanfront and soundfront properties separately:
∑
∑
(2)
In this equation, LnDist is the natural logarithm of the property’s distance from the water in feet.
We then introduce the shoreline erosion rate into our analysis in order to examine the
capitalization of erosion risk into property prices. For the sample of oceanfront properties we
estimate the following regression equation:
∑
In this model,
∑
(3)
is the long run erosion rate (in feet per year) at the proximate location
of property i. Alternatively, we also analyze the data when replacing
with the ratio of the distance from the ocean to the proximate erosion rate
∑
∑
and
:
(4)
A negative coefficient of Erosion in equation (3) would indicate that more rapid long run erosion
rates negatively affect property values, and therefore provide support for Hypothesis 1.
Similarly, a positive coefficient for ErosionRatio in equation (4) would suggest that homebuyers
are willing to pay a premium for a waterfront property located further from the ocean relative to
the rate of erosion experienced in that location. This would also provide evidence for the
capitalization of the long run erosion rate into property prices.
In order to test Hypothesis 2, that the capitalization of the rate of erosion and the distance
of properties from the shore is negligible for properties for which the destructive event is
expected to occur far into the future, we apply equations (3) and (4) on subsets of our data sorted
into terciles by LnDist or ErosionRatio. Coefficients that are increasing in magnitude for the
18
Erosion or ErosionRatio for properties with lower LnDist or higher ErosionRatio would support
Hypothesis 2. Additionally, we apply equation (2) to oceanfront properties segmented by
differing Erosion thresholds. A larger negative LnDist coefficient for the tercile that includes the
lowest Erosion values would also provide support to Hypothesis 2.
The existing literature provides evidence that nonlocal buyers purchase properties for a
premium, on average, compared with nonlocal buyers. Lambson, McQueen and Slade (2004)
provide evidence that a price anchoring bias and higher search costs contribute to the relative
price premium. In this paper we revisit the nonlocal premium hypothesis presented by Lambson,
McQueen and Slade.
We analyze several different specifications of the following regression equation in order
to investigate the nonlocal price premium.
∑
∑
(5)
where Local, CoastNeigh and S&NE are dummy variables with a value of 1 if the buyer’s origin
is North Carolina, a neighboring coastal state and the South or the Northeast, respectively, and 0
otherwise. The origin characteristic vector (OriginChar) includes our education measure
(OriginEdu) and the natural log of the per capita income (OriginInc) and median housing value
(OriginVal) at each buyer’s zip code of origin. The coefficients of the location origin variables
indicate whether a nonlocal price premium exists and whether it varies with buyer origin. The
coefficients of OriginEdu, OriginInc and OriginVal variables allow us to examine whether buyer
characteristics like education, income, and home values in the area of origin are correlated with
the transaction price. As a robustness check, we also run regression specification (5) on the top
19
and bottom 50% of the transactions in terms of price for inland, soundfront and oceanfront
properties. This price segmentation addresses the fact that nonlocal buyers, on average, purchase
more expensive homes compared with local buyers.
To examine the effect of short-term shocks to the erosion rate on the capitalization of
erosion risk into property values, we identify periods following hurricanes that caused severe
damage to the area. According to the National Oceanic and Atmospheric Administration
(NOAA), 70 named storms (hurricanes or tropical storms) affected the eastern part of North
Carolina during the 1995 to 2012 time period, with differing levels of severity. Fortunately, most
of these events were associated with only minor physical destruction and had minimal lasting
effects on the area. However, three storms: Hurricane Fran (September1996)10, Hurricane Floyd
(September 1999)11 and Hurricane Isabel (September 2003)12 were particularly destructive to the
area, with physical damages exceeding $500 million13 in addition to the loss of human lives.
We create three indicator variables (Yhit) that include the two-year period14 immediately
following each of these three catastrophic events. These indicator variables are then interacted
with Inland, Sound, LnDist and ErosionRatio variables as follows:
∑
∑
∑
(6a)
10
The physical damage from Hurricane Fran was reported to be $2.55B in 1996. Hurricane Fran was also
responsible for 14 deaths of which 6 were classified as direct deaths. At the time, this hurricane was considered to be
the state’s worst natural economic disaster.
11
The physical damage from Hurricane Floyd was reported to be $3.7B in 2007. Hurricane Floyd was also
responsible for 35 direct and 16 indirect deaths. Floyd compounded the effect of Hurricane Dennis for which severe
beach erosion was reported and occurred only a few weeks prior to Floyd. North Carolina Governor Jim Hunt
described the hurricane as "the worst disaster to hit North Carolina in modern times."
12
The physical damage from Hurricane Isabel was reported to be $450M in 2003. Most of the damage from this
hurricane was concentrated in Dare County where thousands of homes washed away. The storm surge produced a
wide inlet (up to 2,000 feet) that isolated some roads in the area for up to two months.
13
2013 dollars.
14
We also experimented with “post” periods that include the one-year and three-year periods that immediately
followed each of our three identified catastrophic events. Results from these analyses are similar to the two year post
windows and therefore omitted in the interest of brevity.
20
∑
∑
∑
(6b)
∑
∑
∑
(6c)
∑
∑
∑
(6d)
Consistent signs across the coefficients for τ1, τ2, τ3 and τ4 for the three post periods would
provide support for Hypothesis 3, indicating buyers temporarily adjust the risk premium during
post catastrophic-event periods for properties that are waterfront, closer to the shore, and with
lower ratios of distance-to-erosion rate.
For robustness, we also apply equations (3) and (4) to examine the capitalization of land
erosion for subsamples of the data. These robustness checks allow us to observe whether the
capitalization of erosion risk into property prices varies by specific time period or housing price
trend. Therefore, we segment our dataset two ways. The first segmentation separates the full
dataset into observations occurring during the early period of our study (1996-2003) and those
occurring in the late period of our study (2004-2012). The second segmentation divides the data
into three periods of differing housing price trends: Boom, bust, and normal. We define the
period between 1996 and 2002 as normal in terms of price appreciation, the period from 2003 to
2006 as the boom period, and 2007 through 2012 as the bust period.
21
5. Results
5.1. Capitalization of Land Erosion into Property Prices
Table 2 displays the results of the hedonic regressions from equations (1) through (4).
The adjusted R-squares of these regressions range from 60% to 67% and the signs of the
regression coefficients are generally in line with expectations. The coefficients for housing
characteristic variables such as the number of bathrooms (Bath), number of bedrooms (Bed),
numbers of stories (Story), lot size (LnLotSize), and property condition (Condition) are all
positive and statistically significant, which is consistent with the findings of numerous other
studies. However, the coefficient of the property’s age variable (LnAge), which is expected to be
negative, is instead positive and significant. While it is not easily verifiable from our data,
conversations with real estate agents from the area reveal that the most desirable locations along
the coast were developed first, and the newer homes have been generally built on less desirable
lots. Additionally, the agents indicate that the overall quality of craftsmanship and finishes in
older homes often surpasses those of newer homes.
As expected, the results from the hedonic regression defined in equation (1)
(specification 1) reveal that oceanfront properties (the omitted variable in the model) sell for a
premium to soundfront properties, which in turn sell for a premium compared with inland
properties. Specifications (2), (3) and (4) of Table 2 report the results of the hedonic regression
defined in equation (2). Specification (2) refers to all waterfront properties (oceanfront and
soundfront combined) and specifications (3) and (4) refer separately to soundfront and
oceanfront properties, respectively. In all three specifications the coefficient of the distance from
the property to the water (LnDist) is negative and statistically significant, suggesting that when
shoreline erosion rates are not explicitly considered, homebuyers are willing to pay a premium
22
for properties which are closer to the water. These results conflict with some previous studies
(Gopalakrishnan, Smith, Slott and Murray 2010; Landry, Keeler and Kriesel 2003; Pompe and
Rinehart 1995), which provide evidence for buyers’ willingness to pay extra for an additional
foot of sand. However, these studies did not control for the long run erosion rate and therefore
were unable to isolate the desire to be closer to the water from the desire to be protected from the
risk of shoreline erosion.
Specifications (5) and (6) present the results of two different hedonic regressions from
equation (3). Specification (5) shows a negative and statistically significant coefficient for the
long run erosion rate variable (Erosion). The magnitude of the negative coefficient becomes even
larger when the property’s distance from the water is considered (specification 6). It is important
to note that in this specification, the magnitude of the LnDist coefficient is roughly double that
found when erosion is not included in the regression (specification 4). This is consistent with the
notion that once the erosion rate factor is considered, buyers prefer to be closer to the water. The
results presented in specifications (5) and (6) clearly suggest that homeowners consider the land
erosion factor when purchasing a home and discount housing located in more rapidly eroding
areas. In other words, it seems clear that long run erosion risk is capitalized into residential
property prices, in support of Hypothesis 1. The final specification (7) in Table 2 reports the
results from the hedonic regression defined in equation (4). The positive and statistically
significant coefficient of the ErosionRatio variable illustrates the tradeoff between the desire to
be located closer to the ocean and the consideration of the long run land erosion rate. The fact
that home buyers are willing to pay a premium for an oceanfront property with a high distanceto-erosion ratio also confirms the results from specifications (5) and (6). Overall, Table 2
resolves some of the inconsistencies in the existing literature regarding the impact of beach width
23
on property prices. The results highlight the fact that being closer to the ocean commands a
premium, but that erosion rate must also be considered.
Table 3 provides a closer look at how long run erosion rates are capitalized into the prices
of residential real estate. Specifications (A1), (A2) and (A3) employ the hedonic regression from
equation (3) on terciles of the oceanfront properties in the dataset sorted by distance from the
water. Specification (A1) refers to the one-third of oceanfront properties closest to the water.
Specifications (A2) and (A3) include the middle and most distant thirds of the oceanfront
sample, respectively. The results in these three specifications suggest that the long run erosion
rate is capitalized to the greatest extent into the prices of properties located closest to the ocean
(A1). The capitalization of the erosion rate is slightly smaller for properties in the middle tercile
in terms of distance from the ocean (A2) and becomes statistically insignificant for properties in
the most distant tercile (A3).
Specifications (B1), (B2) and (B3) employ the hedonic regression from equation (4) on
terciles of the oceanfront data sorted by the distance-to-erosion ratio (ErosionRatio). Similar to
the segmentation methodology for physical distance from the ocean, specification (B1) includes
the tercile of properties with the lowest distance-to-erosion ratio, while specifications (B2) and
(B3) contain the middle and the highest distance-to-erosion terciles, respectively. Consistent with
the findings presented in specifications (A1) through (A3), although to an even greater extent,
the ratio of distance from the ocean to the long run erosion rate appears to only be capitalized for
properties in the lowest distance-to-erosion ratio tercile (specification B1). For the other two
terciles the coefficients of distance-to-erosion ratio are statistically insignificant. Again, this
implies that home buyers do not factor shoreline erosion rates into their reservation price unless
the erosion risk to the subject property is high.
24
Finally, specifications (C1) and (C2) examine the capitalization of land erosion into
property prices among different ranges of long run erosion. Specification (C1) includes all
properties located in areas with long run annual erosion rates below 2.5 feet, and specification
(C2) includes all properties associated with long run annual erosion rates of 2.5 feet and greater.
We find the coefficient of the LnDist variable is only negative and statistically significant for
properties located in low erosion rate areas (C1) and statistically insignificant for properties
located in high erosion rate areas (C2). This indicates that proximity to the ocean only commands
a premium for properties located in areas with low erosion rates and further supports the results
from specifications (A) and (B).
Overall, the findings presented in Table 3 support Hypothesis 2 and suggest that
homebuyers generally ignore the risk of shoreline erosion unless it appears to be a near-term
threat. In other words, oceanfront homebuyers are willing to pay a premium for closer proximity
to the water and do not factor erosion risk into the purchase price unless the property is either
very close to an eroding beach and/or located in a rapidly eroding area.
5.2. Buyers’ Origin
The results reported in Panel A of Table 4 shed light on the extent to which nonlocal
buyers pay a premium for properties compared to local buyers. The existing literature interprets a
price premium paid by nonlocal buyers as a market inefficiency and provides some evidence that
the premium is driven by a price anchoring bias and higher search costs.
Specifications (1) and (2) present the results of the hedonic regression from equation (5)
applied to the full sample with one and three location dummies, respectively. The negative
coefficient of the Local dummy variable in each of the regressions indicates that nonlocal buyers
25
pay a premium when they purchase a property compared with local buyers. The coefficients of
CoastNeigh and S&NE variables are also negative, but smaller in magnitude than the Local
coefficient and the S&NE coefficient is insignificant. This suggests that the premium paid by
nonlocal buyers is positively correlated with the distance of buyers’ origin from North Carolina.
Specifically, North Carolina residents pay the smallest premium, while buyers from the
neighboring coastal states, the South, Northeast, and all other areas (Midwest, West and foreign
countries) pay an increasingly higher premium.15 Assuming that search costs are correlated with
the buyer’s physical distance from the subject property, our results are consistent with the
conclusion of Lambson, McQueen and Slade (2004) that higher search costs contribute to the
nonlocal premium.
Specifications (3) through (5) repeat specification (2) for inland, oceanfront and
soundfront properties. Generally, the results of these regressions are similar to the results
generated from the full sample, suggesting that the nonlocal premium is not driven by a specific
property type. We noted earlier that nonlocal buyers tend to purchase, on average, larger and
more expensive properties compared with local buyers. Therefore, in specifications (6) and (7)
we repeat specification (2) for the bottom and top 50% of the transactions in terms of the sale
price in each of the inland, oceanfront and soundfront categories. Once again, the results are
similar to those from the full sample, indicating that the nonlocal premium is present across
varying price points and is therefore not driven by the tendency of nonlocal buyers to purchase
higher-priced homes.
15
We thank an anonymous referee for suggesting the examination of North Carolina homestead exemption as a
possible driver for the nonlocal premium. While the state of North Carolina offers homestead exemption, these
exceptions are only available to the elderly, disabled and as a low income families. Given that these exemptions only
capture the weakest economic segments of the population, it is reasonable to assume that due to high property prices
of our subject area, the vast majority of buyers would not qualify for these exemptions.
26
Panel B of Table 4 presents results from regressions that employ the zip code
characteristics of the buyer’s origin. The results from specifications (1) through (3) show that,
considered independently, the level of education (OriginEdu), housing valuation (OriginVal) and
income per capita (OriginInc) at the buyer’s origin are all positively related to the price a buyer
pays for a property. These results are not surprising given the positive correlation between the
three characteristics. However, when these characteristics enter the regression simultaneously in
specification (4), the coefficients of OriginVal and OriginInc remain positive and statistically
significant while the coefficient of OriginEdu becomes negative and statistically significant. This
suggests that buyers with higher income levels and from areas where housing is more expensive
tend to pay more for properties, while more educated buyers tend to pay less, on average. The
positive coefficient of the OriginVal variable is consistent with the price anchoring bias
hypothesis of Lambson, McQueen and Slade (2004). The negative coefficient of the OriginEdu
variable suggest that, on average, more sophisticated buyers are able to purchase properties at a
relative discount. When local and nonlocal buyers are examined separately in specifications (5)
and (6), the coefficient of OriginEdu is no longer statistically significant and the sign switches to
positive in the case of nonlocal buyers (specification 6). Of the three origin characteristics, only
the coefficient of OriginVal retains its sign (positive) and significance for local buyers
(specification 5), while the coefficient of OriginInc is the only coefficient that retains its sign
(positive) and significance for nonlocal buyers (specification 6). These results suggest that the
buyer’s income is the primary driver of the nonlocal price premium and therefore contradicts the
notion of a price anchoring bias. Also, the insignificant coefficients of the OriginEdu variable
weakens our previous finding on the relation between buyers’ level of sophistication and relative
price discounts.
27
5.3. Long Run Erosion Rate Shocks
Table 5 presents the results of our analysis of the effect of shocks to the long term erosion
rate on the capitalization of erosion risk into property prices. As per the hedonic regressions from
equations (6a) through (6d) we interact the post hurricane periods with four different variables.
Generally, the results presented in Table 5 provide little evidence that shoreline erosion risk is
capitalized differently during post-hurricane periods. The results from equation (6a)
(Specification 1) indicate that inland properties sold for a relative premium following hurricane
Fran (S1), but for a relative discount following hurricane Floyd (S2). Specification (2) shows no
clear relative discount or premium for soundfront properties following the three catastrophic
events. Finally, Specifications (3) and (4) indicate that oceanfront properties located a greater
distance from the ocean, or with a higher ratio of distance to erosion rate, sold for a premium
following hurricane Isabel (S3), but show no sign of a premium following hurricanes Fran and
Floyd.
It is possible that these weak and variable results are due to the high frequency of named
and unnamed storms (including nor’easters which, although unnamed, can be extremely severe)
that impact the area during the sample period. While most of these storms did not have a lasting
impact on properties located in Dare County, the high frequency of storms impacting the area
makes disentangling severe post-catastrophic events from less severe events a significant
challenge. Therefore, we believe the results presented in Table 5 are generally in line with
Hypothesis 4.
28
5.4. Robustness Checks
As a robustness check we examine whether the evidence of capitalization of the erosion
rate into property prices is triggered by a specific time period or market price trend. As we
describe in the methodology section, we first divide the dataset into two segments, which include
the observations that took place during the early period of our study (1996-2003) and the late
period of our study (2004-2012). We then repeat the regression analyses presented in Table (2)
for both time periods. The results of this analysis reveal that the capitalization of shoreline
erosion into property prices during the earlier and the later time period is similar and therefore
suggests that the general findings presented in this paper persist throughout the entire sample
period. For the sake of brevity, these results are not tabulated.
We continue by analyzing segments of the data based on different housing price trends.
The normal price trend segment includes the 1996 through 2002 time period, the boom period is
defined as occurring between 2003 and 2006, and the bust period includes all transactions that
took place between 2007 and 2012. Once again, we repeat the regression analyses presented in
Table 2 for each of these three time periods. The regression results for each of the segmented
periods are not materially different from the results presented in Table 2 and thus suggest our
general findings are not being driven by a particular housing price trend. For brevity, these
results are also not presented.
6. Conclusion
This paper explores how the risk associated with shoreline erosion is capitalized into
residential real estate prices. The results of our analysis show that while oceanfront properties
command a higher premium when located closer to ocean, the rate of erosion at the property
29
location is negatively associated with property value. These findings resolve some of the
inconsistencies in the existing literature that examines the impact of the distance of the property
from the ocean without controlling for shoreline stability. Additionally, our results indicate that
the risk of land erosion is capitalized into the price of properties only when those properties are
located either very close to the ocean and/or adjacent to rapidly eroding beaches. This is
consistent with evidence provided by the psychology literature on general human behavior
regarding distant events.
Moreover, we show that nonlocal buyers tend to pay a premium for the properties they
purchase compared with local buyers. The nonlocal premium is observed across different price
points and appears to be positively correlated with the income level of the buyer’s origin.
When examining periods immediately following severe hurricanes we do not find reliable
differences in the capitalization of shoreline erosion into property prices. Since the high
frequency of named and unnamed storms may have impaired our ability to adequately isolate
severe storms from less severe storms, we suggest this issue be further explored using data from
areas with a lower frequency of catastrophic meteorological events.
30
References:
Ainslie, G., 1991. Derivation of “Rational” Economic Behavior from Hyperbolic Discount
Curves. The American Economic Review 81: 334-340.
Azfar, O., 1999. Rationalizing Hyperbolic Discounting. Journal of Economic Behavior and
Organization 38: 245-252.
Benson, E., J. Hansen, A. Schwartz, and G. Smersh, 1998. Pricing Residential Amenities: The
Value of a View. Journal of Real Estate Finance and Economics 16: 55-73.
Bin, O., T. Crawford, J.B. Kruse, and C.E. Landry, 2008. Viewscapes and Flood Hazard: Coastal
Housing Market Response to Amenities and Risk. Land Economics 84: 434-48.
Bin, O., J.B. Kruse, and C.E. Landry, 2008. Flood Hazards, Insurance Rates, and Amenities:
Evidence from the Coastal Housing Market. Journal of Risk and Insurance 75: 63-82.
Bin, O., B. Poulter, C.F. Dumas, and J.C. Whitehead, 2011. Measuring the Impact of Sea-Level
Rise on Coastal Real Estate: A Hedonic Property Model Approach. Regional Science 51:
751-767.
Bleich, D., 2003. The Reaction of Multifamily Capitalization Rates to Natural Disasters. Journal
of Real Estate Research 25: 133-144.
Bond, M.T., V.L. Seiler, and M.J. Seiler, 2002. Residential Real Estate Prices: A Room with a
View. Journal of Real Estate Research 23: 129-137.
Cropper, M., S. Aydede, and P. Portney, 1994. Preferences for Life Saving Programs: How the
Public Discounts Time and Age. Journal of Risk and Uncertainty 8: 243-265.
Gopalakrishnan, S., M.D. Smith, J.M. Slott, and A.B. Murray, 2011. The Value of Disappearing
Beaches: A Hedonic Pricing Model with Endogenous Beach Width. Journal of
Environmental Economics and Management 61: 297-310.
Hallstrom, D.G. and V.K. Smith, 2005. Market Responses to Hurricanes. Journal of
Environmental Economics and Management 50: 541-561.
Hardin, W.G. III and M.L. Wolverton, 1999. Equity REIT Property Acquisitions: Do Apartment
REITs Pay a Premium? Journal of Real Estate Research 17: 113-126.
Harrison, D.M., G.T. Smersh, and A.L. Schwartz, 2001. Environmental Determinants of Housing
Prices: The Impact of Flood Zone Status. Journal of Real Estate Research 21: 3-20.
Heinz Center for Science, Economics and Environment, 2000. Evaluation of Erosion Hazards
Summary. Prepared for Federal Emergency Management Agency. Available at
http://www.heinzctr.org/publications/PDF/erosnrpt.pdf.
Kirby, K. and R. Herrnstein, 1995. Preference Reversals Due to Myopic Discounting of Delayed
Reward. Psychological Science. 6: 83-89.
Kulshreshtha, S. and A. Gillies, 1993. Economic Evaluation of Aesthetic Amenities: A Case
Study of River View. Water resources Bulletin 29: 257-266.
Lambson, V.E., G.R. McQueen, and B.A. Slade, 2004. Do Out-of-State Buyers Pay More for
Real Estate? An Examination of Anchoring-Induced Bias and Search Costs. Real Estate
Economics 32: 85-126.
31
Landry, C., A. Keeler, and W. Kriesel, 2003. An Economic Evaluation of Beach Erosion
Management Alternatives. Marine Resource Economics 18: 105-127.
Loewenstein, G., 1987. Anticipation and the Valuation of Delayed Consumption. The Economic
Journal, 666-684.
Landry, C.E. and P. Hindsley, 2011. Valuing Beach Quality with Hedonic Property Models.
Land Economics 87: 92-108.
Lansford, N. and L. Jones, 1995. Recreational and Aesthetic Value of Water Using Hedonic
Price Analysis. Journal of Agricultural and Resource Economics 20: 341-355.
McKenzie, R. and J. Levendix, 2010. Flood Hazards and Urban Housing Markets: The Effects of
Katrina on New Orleans. Journal of Real Estate Finance Economics 40: 62-76.
Pompe, J. and J. Rinehart, 1995. Beach Quality and the Enhancement of Recreational Property
Values. Journal of Leisure Research 27: 143-154.
Pompe, J. and J. Rinehart, 1999. Establishing Fees for Beach Protection: Paying for a Public
Good. Coastal Management 27: 57-67.
Ranson, M., 2012. What Are the Welfare Costs of Shoreline Loss? Housing Market Evidence
from a Discontinuity Matching Design. Harvard Kennedy School, Working Paper.
Riggs, S.R., D.V. Ames, S.J. Culver, and D.J. Mallinson, 2011. The Battle for North Carolina’s
Coast: Evolutionary History, Present Crises, and Vision for the Future. The University of
North Carolina Press, Chapel Hill, ISBN 978-0-8078-3486-2.
Skantz, T.R. and T.H. Strickland, 1987. House Prices and a Flood Event: An Empirical
Investigation of Market Efficiency. Journal of Real Estate Research 2: 75-83.
Thaler, R., 1981. Some Empirical Evidence on Dynamic Inconsistency. Economics Letters 8:
201-207.
Turnbull, G.K., V. Zahirovic-Herbert, and C. Mothorpe, 2013. Flooding and Liquidity on the
Bayou: The Capitalization of Flood Risk into House Value and Ease-of-Sale. Real Estate
Economics 41: 103-129.
U.S. Army Corps of Engineers, 1996. Shoreline Protection and Beach Erosion Control Study.
IWR Report 96-PS-1.
Wyman, D., N. Hutchinson, and P. Tiwari, 2014. Testing the Waters: A Spatial Econometric
Pricing Model of Different Waterfront Views. Journal of Real Estate Research,
Forthcoming.
32
Table 1: Descriptive Statistics
Table 1 shows descriptive statistics of the datasets employed in the paper. Panel A shows the distribution
of observations in the sample that includes the number of inland and waterfront properties and the share
of local and nonlocal buyers, who transacted each type of property during the sample period. Panel B
shows summary statistics of the property characteristics included in the hedonic regressions. Panel C
provides summary statistics by the origin of the buyer on the characteristics of the properties. “Coastal
Neighbors” refers to Virginia and South Carolina and the definition for states included in each of the four
US regions are from the US Census.
Panel A: General Statistics
Time period
1996 to 2012
Total number of observations
13,106
Inland properties
11,071
Waterfront properties (soundfront & oceanfront)
2,035
Ocean front properties
854
Soundfront properties
1,181
% of local / nonlocal buyers (total)
45.03/54.97
% of local / nonlocal buyers (oceanfront)
18.99/81.01
% of local / nonlocal buyers (soundfront)
47.22/52.78
Panel B: Property Characteristic Statistics
Average
Housing age (years)
Number of baths
Number of bedrooms
Number of stories
Lot size (sqft)
Transaction price ($)
Distance from water (ft)
Distance from water (ft) – Ocean
Distance from water (ft) – Soundfront
Long run land erosion rate (ft/year)
15.22
2.85
3.73
1.67
13,591
332,831
173
315
72
3.2
33
Standard
deviation
14.98
1.47
1.21
0.73
9,605
286,570
167
161
69
2.7
Number of
observations
13,106
13,106
13,106
13,106
13,106
13,106
2,035
854
1,181
854
Panel C: Property Characteristic Statistics by Buyers’ Origin
Coastal
neighbors
32.99
2.20
8.07
7.98
83.96
14.87
3.02
3.89
1.73
12,022
377,056
308.6
60.70
South
Northeast
Midwest
West
Foreign
% of all transactions
Average quality
% Soundfront
% Oceanfront
% Inland
Housing age (years)
Number of baths
Number of bedrooms
Number of stories
Lot size (sqft)
Transaction price ($)
Distance from water (ft)–Ocean
Distance from water (ft)–Soundfront
North
Carolina
45.04
2.11
9.55
2.72
87.73
16.05
2.51
3.42
1.53
14,772
258,600
316.2
70.09
9.31
2.26
9.75
10.56
79.69
14.47
3.21
4.06
1.87
13,462
416,293
326.8
77.96
9.60
2.26
9.85
11.51
78.64
13.49
3.37
4.20
1.91
13,447
411,210
318.8
86.70
1.81
2.20
10.46
16.32
73.22
13.03
3.42
4.14
1.95
14,159
457,879
294.7
68.80
1.01
2.23
6.77
19.55
73.68
17.11
3.14
4.06
1.95
13,602
430,446
339.7
41.10
0.26
2.24
14.71
5.88
79.41
15.91
3.40
4.20
1.91
14,147
453,292
170.5
54.44
Long run land erosion rate (ft/year)
3.26
3.02
3.30
3.46
3.31
3.12
2.00
34
Table 2. Capitalization of Erosion Rate
Table 2 displays the results of the hedonic regressions presented in equations (1) through (4). The
dependent variable is the logarithm of property’s purchase price. The independent variables include basic
characteristics of the homes: Number of bathrooms (Bath), number of bedrooms (Bed), numbers of stories
(Story), the logarithm of lot size (LnLotSize), condition of the property (Condition), and the logarithm of
property’s age (LnAge). The main variables of interest include: Soundfront and Inland, which take the
value of 1 when the property is a soundfront property or is located inland, respectively, and 0 otherwise
(the oceanfront indicator is the omitted variable); LnDist is the distance to water in logarithm of feet;
Erosion is the long run erosion rate (in feet per year) at the location of property; ErosionRatio is the
distance to the ocean divided by erosion rate. Specification (1) considers all transactions, specification (2)
considers soundfront and oceanfront properties, specification (3) considers soundfront properties and
specifications (4) though (7) consider oceanfront properties. T-statistics are displayed under the
coefficients (*10%, **5%, ***1% level of significance).
(1)
All
Bath
Bed
Story
LnLotSize
Condition
LnAge
Soundfront
Inland
0.1443***
[25.16]
0.0951***
[14.71]
0.0815***
[12.15]
0.0864***
[12.68]
0.1543***
[22.72]
0.1042***
[26.92]
-0.2416***
[-11.41]
-0.4564***
[-26.80]
LnDist
(2)
Sound- &
Oceanfront
0.1436***
[10.98]
0.0490***
[3.29]
0.0509***
[3.00]
0.3669***
[17.88]
0.1318***
[8.31]
0.1286***
[10.95]
-0.4382***
[-10.82]
(3)
(4)
(5)
(6)
(7)
Soundfront
Oceanfront
Oceanfront
Oceanfront
Oceanfront
0.1612***
[8.35]
0.0788***
[3.51]
0.0611**
[2.56]
0.2728***
[10.61]
0.1536***
[7.21]
0.1462***
[10.71]
0.1295***
[7.06]
0.0073
[0.36]
0.0368
[1.51]
0.5299***
[16.00]
0.0848***
[3.58]
0.1066***
[4.84]
0.1196***
[6.61]
0.0092
[0.46]
0.0307
[1.31]
0.5115***
[16.05]
0.0724***
[3.10]
0.1020***
[4.78]
-0.1544
[-0.32]
0.1136***
[6.31]
0.0075
[0.38]
0.0526**
[2.21]
0.5354***
[16.61]
0.0701***
[3.03]
0.0850***
[3.93]
-0.3587
[-0.74]
0.1289***
[7.04]
0.0107
[0.53]
0.0172
[0.72]
0.5028***
[15.31]
0.0815***
[3.44]
0.1168***
[5.40]
-0.028
[-0.06]
-0.0862***
[-4.45]
-0.0776***
[-3.39]
-0.0742*
[-1.95]
-0.0384***
[-6.07]
-0.1473***
[-3.83]
-0.0452***
[-6.94]
Erosion
ErosionRatio
Constant
# obs
Adj. R2
9.9533***
[144.06]
13,106
0.6026
9.1391***
[35.08]
2,034
0.6502
8.5244***
[33.06]
1,181
0.6016
8.2236***
[18.20]
853
0.6508
35
7.8195***
[26.05]
854
0.6638
9.1598***
[19.92]
854
0.6692
0.0613***
[2.74]
7.4333***
[24.23]
854
0.6519
Table 3. Capitalization of Erosion Rate in Partitioned Samples
Table 3 shows the results from hedonic regressions in partitioned samples. In specifications (A1), (A2)
and (A3) the data are sorted based on the property’s distance from the water into terciles. Specification
(A1) refers to the one third of the oceanfront properties that are the closest to the water. Specifications
(A2) and (A3) include the middle third and the furthest third of the observations in terms of distance from
the water, respectively. In specifications (B1), (B2) and (B3) data are sorted into terciles by
(ErosionRatio), so that specification (B1) includes the tercile of the observations with the lowest distanceto-erosion ratio and specifications (B2) and (B3) include the terciles with the middle and the highest ratio
of distance-to-erosion ratio properties, respectively. Specification (C1) includes all properties that are
located in areas with a long run erosion rates below 2.5 feet per year, and specification (C2) includes all
properties associated with a long run erosion rates equal or greater than 2.5 feet per year. The dependent
variable is the logarithm of property’s purchase price. The independent variables include basic
characteristics of the homes: Number of bathrooms (Bath), number of bedrooms (Bed), numbers of stories
(Story), lot size (LnLotSize), condition of the property (Condition), and the logarithm of property’s age
(LnAge). LnDist is the distance to water in logarithm of feet; Erosion is the long run erosion rate (in feet
per year) at the location of property; ErosionRatio is the distance to the ocean divided by erosion rate. Tstatistics are displayed under the coefficients (*10%, **5%, ***1% level of significance).
Bath
Bed
Story
LnLotSize
Condition
LnAge
LnDist
Erosion
(A1)
0.1516***
[4.46]
-0.0041
[-0.12]
0.0627
[1.49]
0.4798***
[10.72]
0.0889**
[2.26]
0.0906**
[2.37]
0.0715
[0.67]
-0.0487***
[-5.93]
(A2)
0.1135***
[3.40]
-0.0164
[-0.47]
0.0277
[0.62]
0.6474***
[8.43]
0.0679*
[1.73]
-0.0043
[-0.11]
-0.2189
[-0.64]
-0.0440***
[-3.30]
(A3)
0.0820***
[2.93]
0.0820**
[2.33]
0.1056***
[2.75]
0.2006**
[2.37]
0.0546
[1.31]
0.1542***
[4.19]
-0.0153
[-0.14]
-0.0159
[-1.02]
ErosionRatio
Constant
# obs
Adj R2
6.6828***
[7.01]
284
0.7770
9.4315***
[2.62]
286
0.6353
10.0585***
[6.71]
283
0.6269
(B1)
0.1605***
[4.39]
-0.0536
[-1.55]
0.1081**
[2.13]
0.4175***
[9.73]
0.0662
[1.56]
0.0498
[1.22]
(B2)
0.1530***
[4.40]
-0.0052
[-0.14]
0.0240
[0.58]
0.4715***
[5.80]
0.0971**
[2.55]
0.1128***
[2.93]
(B3)
0.0798***
[2.85]
0.0639*
[1.74]
0.0268
[0.68]
0.3038***
[3.44]
0.0228
[0.53]
0.1141***
[3.04]
0.1551***
[3.77]
7.7661***
[16.94]
284
0.7262
-0.2005
[-0.91]
8.6325***
[6.76]
286
0.5677
-0.1058
[-1.03]
9.8006***
[9.08]
283
0.6017
36
(C1)
0.0931***
[4.37]
0.0171
[0.70]
0.0315
[1.11]
0.5667***
[13.56]
0.0853***
[3.08]
0.0747***
[2.89]
-0.1612***
[-3.47]
(C2)
0.1810***
[4.97]
-0.0265
[-0.74]
0.1033**
[2.13]
0.3618***
[6.79]
0.0523
[1.19]
0.1171***
[2.78]
0.0610
[0.84]
8.9245***
[15.03]
590
0.6430
7.0770***
[9.52]
263
0.7112
Table 4. Properties Price Paid by Buyers’ Origin
Panels A and B of Table 4 show results from hedonic regression presented in equation (5) for different
data segments. In Panel A specifications (1) and (2) analyze the full dataset, specifications (3) through (5)
consider inland, oceanfront and soundfront properties and specifications (6) and (7) analyze low and high
price properties, respectively. In Panel B specifications (1) through (4) analyze the full dataset and
specifications (5) and (6) analyze transactions by local and nonlocal buyers, respectively. The dependent
variable is the logarithm of property’s purchase price. The independent variables include basic
characteristics of the homes: Number of bathrooms (Bath), bedrooms (Bed) and stories (Story), lot size
(LnLotSize), condition of the property (Cond), and the logarithm of property’s age (LnAge). The main
variable of interest in Panel A are Local, CoastNeigh and S&NE, which take the value of 1 if property
buyers are from North Carolina, Virginia or South Carolina and the South and the Northeast regions,
respectively, and 0 otherwise. The main variable of interest in Panel B are OriginEdu, OriginVal and
OriginInc which are measures of education, housing value and income at the buyers’ origin zip code. Tstatistics are displayed under the coefficients (*10%, **5%, ***1% level of significance).
Panel A: Local and Nonlocal Buyers
Bath
Bed
Story
LnLotSize
Condition
LnAge
Inland
Soundfront
Local
(1)
All
0.1400***
[24.76]
0.0828***
[12.94]
0.0718***
[10.84]
0.1095***
[16.07]
0.1463***
[21.83]
0.0970***
[25.34]
-0.4349***
[-25.87]
-0.2194***
[-10.50]
-0.1665***
[-19.92]
CoastNeigh
S & NE
Constant
# Obs
Adj R2
9.8994***
[145.32]
13,106
0.6143
(2)
All
0.1399***
[24.75]
0.0828***
[12.94]
0.0708***
[10.66]
0.1085***
[15.90]
0.1464***
[21.85]
0.0968***
[25.29]
-0.4323***
[-25.69]
-0.2176***
[-10.41]
-0.2145***
[-9.21]
-0.0588**
[-2.52]
-0.0360
[-1.50]
9.9568***
[138.52]
13,106
0.6145
(3)
Inland
0.1329***
[21.23]
0.0915***
[12.98]
0.0812***
[11.32]
0.0710***
[9.87]
0.1453***
[19.68]
0.0883***
[22.02]
(4)
Oceanfront
0.1284***
[7.01]
0.0117
[0.58]
0.0263
[1.11]
0.5130***
[15.80]
0.0838***
[3.54]
0.1120***
[5.17]
(5)
Soundfront
0.1600***
[8.29]
0.0673***
[3.00]
0.0537**
[2.26]
0.2400***
[9.98]
0.1625***
[7.70]
0.1465***
[10.78]
-0.2062***
[-7.89]
-0.0448*
[-1.71]
-0.0142
[-0.52]
9.8821***
[132.57]
11,059
0.5886
-0.2173***
[-3.03]
-0.0986
[-1.50]
-0.1152*
[-1.71]
7.7258***
[25.02]
853
0.6529
-0.1502*
[-1.92]
-0.1263
[-1.59]
-0.1073
[-1.32]
8.3418***
[33.18]
1,194
0.5973
37
(6)
Low Price
0.0094
[1.16]
0.0377***
[4.55]
0.0333***
[3.83]
0.0283***
[3.46]
0.1227***
[12.90]
0.0580***
[13.37]
-0.5594***
[-27.79]
-0.3471***
[-14.08]
-0.0704**
[-2.26]
-0.0099
[-0.31]
0.0159
[0.49]
11.7519***
[111.97]
6,482
0.3047
(7)
High Price
0.1148***
[24.11]
0.0527***
[9.53]
0.0335***
[5.86]
0.0920***
[14.49]
0.0588***
[10.41]
0.0463***
[11.84]
-0.5658***
[-35.92]
-0.3012***
[-15.43]
-0.1270***
[-6.47]
-0.0385**
[-1.99]
-0.0316
[-1.59]
11.5305***
[117.20]
6,624
0.5945
Panel B: Buyers’ Origin Characteristics
Bath
Bed
Story
LnLotSize
Condition
LnAge
Inland
Soundfront
(1)
All
0.1428***
[24.70]
0.0951***
[14.59]
0.0820***
[12.11]
0.0826***
[12.06]
0.1505***
[21.97]
0.1047***
[26.92]
-0.4519***
[-26.16]
-0.2341***
[-10.93]
(2)
All
0.1420***
[24.47]
0.0909***
[13.90]
0.0785***
[11.58]
0.0769***
[11.19]
0.1468***
[21.32]
0.1011***
[25.95]
-0.4488***
[-25.80]
-0.2297***
[-10.67]
(3)
All
0.1398***
[24.28]
0.0936***
[14.42]
0.0792***
[11.76]
0.0808***
[11.86]
0.1468***
[21.51]
0.1022***
[26.37]
-0.4442***
[-25.81]
-0.2274***
[-10.67]
(4)
All
0.1411***
[24.32]
0.0901***
[13.77]
0.0770***
[11.34]
0.0779***
[11.34]
0.1458***
[21.18]
0.0999***
[25.62]
-0.4451***
[-25.59]
-0.2269***
[-10.55]
(5)
Local
0.1526***
[16.21]
0.0520***
[5.20]
0.0669***
[6.31]
0.0493***
[5.20]
0.1611***
[14.30]
0.0974***
[18.24]
-0.4888***
[-12.75]
-0.1911***
[-4.54]
0.1769***
[13.52]
8.1945***
[55.36]
12,874
0.6071
-0.2838***
[-4.98]
0.1055***
[5.07]
0.1434***
[5.17]
7.5404***
[38.11]
12,668
0.6077
-0.0469
[-0.44]
0.1845***
[5.31]
-0.0372
[-0.86]
8.6701***
[22.53]
5,782
0.5374
CoastNeigh
S & NE
OriginEdu
0.2807***
[7.90]
0.1425***
[13.47]
OriginVal
OriginInc
Constant
#Obs
Adj R2
10.4752***
[120.99]
12,869
0.6033
9.0700***
[63.02]
12,683
0.6068
38
(6)
Nonlocal
0.1208***
[16.44]
0.0945***
[11.06]
0.0698***
[8.06]
0.1632***
[15.34]
0.1289***
[15.04]
0.0895***
[15.91]
-0.4076***
[-21.14]
-0.2675***
[-10.30]
-0.0587**
[-2.33]
-0.0411
[-1.58]
0.0749
[0.95]
0.0090
[0.33]
0.0865**
[2.21]
8.4122***
[30.80]
6,886
0.6265
Table 5. Capitalization of Erosion after a Shock to the Erosion Rate
Table 5 presents the results of the analysis that investigates the effect of shocks to the long term erosion
rate on the capitalization of erosion risk into property prices. As per the hedonic regressions from
equations (6a) through (6d) we interact the post hurricane period indicators (S1, S2, and S3) with four
different variables of interest from previous analyses (Inland, Soundfront, LnDist, and ErosionRatio). Tstatistics are displayed under the coefficients (*10%, **5%, ***1% level of significance).
Soundfront
Inland
(1)
-0.2459***
[-11.59]
-0.4512***
[-23.10]
(2)
-0.2363***
[-9.71]
-0.4561***
[-26.78]
(3)
-0.4194
[-0.86]
-0.2056***
[-4.06]
-0.0461***
[-7.08]
LnDist
Eros
0.0579**
[2.02]
ErosRatio
Inland*S1
Inland*S2
Inland*S3
0.0884**
[2.35]
-0.0774**
[-2.32]
-0.0332
[-1.14]
SoundfrontS1
-0.0815
[-1.63]
0.0077
[0.18]
0.0057
[0.16]
SoundfrontS2
Soundfront*S3
LnDist*S1
-0.0841
[-0.81]
0.0890
[0.98]
0.2956***
[3.19]
LnDist*S2
LnDist*S3
ErosionRatio*S1
ErosionRatio*S2
ErosionRatio*S3
Constant
# Obs
Adj R2
(4)
9.8851***
[134.81]
13,106
0.6030
9.9540***
[144.04]
13,106
0.6026
39
9.6346***
[9.33]
854
0.6731
-0.0912
[-1.26]
-0.0144
[-0.25]
0.1082*
[1.76]
6.8192***
[10.13]
854
0.6531
Exhibit 1. Dare County, North Carolina
40
Appendix A:
Exhibit A1: Example of Land Erosion Rate Map – Hatteras Island at Rodanthe
Exhibit A2: Example of Land Erosion Rate Map – Bodie Island - South
41