1. No category

Housing Market Fluctuations and the Implicit Price of Water Quality:
Empirical Evidence from a South Florida Housing Market
Okmyung Bin
Department of Economics,
East Carolina University
Jeffrey Czajkowski
Wharton Risk Management Center,
University of Pennsylvania
Jingyuan Li
Department of Applied Economics and
Statistics, University of Delaware
Gabriele Villarini
Department of Civil & Environmental
Engineering, University of Iowa
March 2015
Working Paper # 2015-05
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.
Housing Market Fluctuations and the Implicit Price of Water Quality:
Empirical Evidence from a South Florida Housing Market
March 2015
Okmyung Bin
Department of Economics, East Carolina University
Brewster Building A-435, Greenville, NC 27858
bino@ecu.edu, (252) 328-6820
Jeffrey Czajkowski
Wharton Risk Management and Decision Processes Center, University of Pennsylvania
Room 526.7 Huntsman Hall, Philadelphia, PA 19104
jczaj@wharton.upenn.edu, (215) 898-8047
Jingyuan Li
Department of Applied Economics and Statistics, University of Delaware
101 Penny Hall, Newark, DE 19716
jingyuan@udel.edu, (302)757-4676
Gabriele Villarini
Department of Civil & Environmental Engineering, University of Iowa
306 C. Maxwell Stanley Hydraulics Laboratory, Iowa City, IA 52242
gabriele-villarini@uiowa.edu, (319) 384-0596

The authors are respectively, Associate Professor of Economics, East Carolina University; Willis Re Research
Fellow, Wharton Risk Management and Decision Processes Center, University of Pennsylvania; Postdoctoral
Researcher, Department of Applied Economics and Statistics, University of Delaware; and Assistant Professor of
Civil & Environmental Engineering, University of Iowa.
Housing Market Fluctuations and the Implicit Price of Water Quality:
Empirical Evidence from a South Florida Housing Market
Abstract. In this study we utilize a hedonic property price analysis to examine changes in the
implicit price of water quality. We analyze Martin County, Florida waterfront home sales from
2001 to 2010 accounting for the associated significant real estate fluctuations in this area through
flexible econometric controls in space and time. We apply a statistical methodology that
identifies housing market price instability over time, interact water quality with these identified
market segmentations, and embed these interactions within a spatial fixed effect model to further
account for any spatial heterogeneity in the waterfront market. Results indicate that water
quality improvement is associated with higher property values. We find no evidence that the
economic downturn crowded out concern for the water quality in this area. We further impute an
implicit prices of $1,754, evaluated at the sample mean, for one percentage point increase in the
water quality grade.
Keywords: hedonic price, housing market fluctuations, segmented regression, spatial fixed
effects, water quality
JEL Classifications: D12, Q25, Q26, R21
1 I. Introduction
Between 2000 and 2006, many parts of the U.S. experienced a housing boom with some
metropolitan areas having nearly 40 percent of their existing housing stock being built during
this time period (Gabe and Florida, 2011), and average U.S. housing prices rising by more than
54 percent (Cho et al., 2011). However, in 2007 the real estate boom turned to bust driven by the
sub-prime mortgage crisis and ultimately an economic recession beginning in December 2007,
with housing values falling between 25 and 50 percent in 25 of the top 100 largest U.S.
metropolitan areas (Gabe and Florida, 2011). The real estate boom and bust was especially
pointed in the state of Florida having 7 of the top 25 metropolitan areas with the highest shares of
housing units constructed from 2000 to 2006 (Gabe and Florida, 2011) along with average
housing price increases of more than 90 percent (Labonte, 2007), but also some of the most
severe declines in housing prices from 2007 to 2010 (Gabe and Florida, 2011).
Hedonic property price models provide an intuitive analytical tool for examining whether
a relationship exists between an environmental amenity and housing prices, and if so, to often
impute implicit prices for the environmental amenity that can then potentially be used for an
associated economic analysis. Rosen (1974) formalized the relationship between the equilibrium
price schedule, supplier technology, and household preferences in a competitive market.
Marginal implicit prices can be interpreted as marginal willingness to pay for housing attributes
from the perspective of the household. As people frequently choose to live near a waterbody to
enjoy the amenity values provided by it, earlier studies considered water quality levels in the
context of the hedonic model, and related these to the willingness to pay for water quality
attributes (Bin and Czajkowski, 2013; Boyle et al., 1999; Egan et al., 2009; Epp and Al-Ani,
2 1979; Gibbs et al., 2002; Krysel et al., 2003; Leggett and Bockstael, 2000; Michael et al., 2000;
Phaneuf et al., 2007; Poor et al., 2001; Poor et al., 2007; Steinnes, 1992; Walsh et al., 2011).
Though, recent hedonic property value studies suggest that the implicit prices for
environmental quality can vary over time (Carruthers et al., 2010; Kuminoff and Pope, 2013;
Parmeter and Pope, 2012; Riddel, 2001), especially given substantial changes to the underlying
(assumed) housing market equilibrium brought about by economy-wide periods of expansion
and contraction (Carruthers et al., 2010; Colson and Zabel, 2013; Kuminoff and Pope, 2013).
Thus, understanding how the derived implicit prices for an environmental amenity such as water
quality vary, or do not vary, over time due to housing market fluctuations is essential to any
analysis that utilizes such information (Boyle et al., 2012; Cho et al., 2011). Moreover, there is
evidence that recessions crowd out concern for the environment (Kahn and Kotchen, 2010), and
therefore potentially result in lower environmental amenity implicit prices in absolute or
percentage terms during a period of contraction (Boyle et al., 2012). This is a non-trivial result
for any associated hedonic property valuation during the recent period of U.S. housing market
malaise. Cho et al. (2011) find evidence of this environmental crowding-out effect in their
analysis of a water view, as well as developed and forest-land open space in the Nashville,
Tennessee metropolitan area.
In this study, we utilize a hedonic property price analysis to examine how the recent
housing market expansion and contraction influenced the relationship between water quality and
housing prices in a waterfront housing market in South Florida. We analyze Martin County,
Florida waterfront home sales from 2001 to 2010 accounting for the associated real estate
fluctuations in the area through flexible econometric controls in space and time. We apply a
statistical methodology that identifies housing market price instability over time in order to
3 interact water quality with our identified market segmentations in the hedonic estimations.
These water quality and time interactions are then embedded within a spatial fixed effect model
to further account for any spatial heterogeneity in the waterfront market. Our results indicate
that water quality improvement is associated with higher property values. The amenity value of
water quality in this area was not influenced during the worst of the housing market contraction
and its associated recovery. Our finding negates the notion that the corresponding recession
crowded out concern for the water quality amenity in Martin County, Florida. We further impute
an implicit price of $1,754 for one percentage point increase in the water quality grade, evaluated
at the sample mean price. Evidence that water quality improvement is associated with higher
property values even in tough economic times may help resource managers and policy makers
make informed decisions during these periods of constrained financial resources.
This paper is organized as follows: Section II provides an overview of the study area and
associated data used; Section III outlines our empirical strategies; Section IV presents the results;
and Section V has the concluding remarks.
II. Study Area and Associated Data
Martin County is located on the southeastern Atlantic coast of Florida. The northeastern
portion of the county and its accompanying waterfront housing market located on the St. Lucie
River, St. Lucie Estuary, and Indian River Lagoon, are analyzed for this study. Analogous to
Leggett and Bockstael (2000), this area is well-suited for a hedonic analysis of water quality due
to the substantial and sufficiently varied number of waterfront properties, the lively housing
market in the area, as well as the variation in water quality across the St. Lucie River, St. Lucie
Estuary, and the Indian River Lagoon. Further, similar to Poor et al. (2007), we postulate that
4 water quality matters to homebuyers in this area given that the health of these waterbodies are an
integral part of the local history as well as the environmental and economic well-being of the
community (Alvarez, 2013; FLDEP, 2010; Hazen and Sawyer, 2008; SFWMDa, 2010;
SFWMDb, 2010). A poll conducted in 2006 for the Community Foundations for Palm Beach
and Martin Counties (Princeton Survey Research Associates International, 2006) found that eight
out of ten Martin County residents indicate protecting the natural environment as the number one
priority of Martin County.
Waterfront Home Sales
The specific home sale data come from the waterfront properties located in the
Northeastern portion of Martin County sold between January 2001 and December 2010 as
constructed from the Martin County property appraiser’s office website
(http://www.mcpropertyappraiser.com). The waterfront homes are defined as the residential
properties within 125 feet from the waterbodies in the entire county. Our initial data set
contained a total of 2,243 waterfront properties with at least one sale occurring between 2001
and 2010 after accounting for missing sales prices or sales dates, as well as homes with sales
prices less than $40,000. In order to match with the associated water quality data described
below, we further limited our dataset to the homes located on the waterbodies in the northeastern
portion of the county (1,593 waterfront homes remaining)1. Finally, after deleting the
observations with incomplete structural characteristics (characteristics to be described below)
and/or poor water quality data, a total of 1,526 single-family residential home sales are used for
1
Some of the 2,243 waterfront single family homes are located in southeast and northwest corner are far away from
the water quality testing stations and thus excluded from the analysis. 5 the analysis. The sales prices are adjusted to 2010 dollars through the Consumer Price Index
South urban size class B/C.2
In our effort to test housing market instability during the 2001 to 2010 timeframe, we
utilized all available waterfront property transactions in the county to construct a rudimentary
median waterfront sales price index in 2010 dollars for the 40 quarters from 2001 to 2010 as
illustrated in Figure 1.3 We see a direct indication of a boom and bust cycle in our Martin
County waterfront housing market with the peak of the market appearing to occur somewhere
around the first quarter of 2006 to third quarter of 2007.4 Boyle et al. (2012) however also
discuss that it is not a drop in price that identifies movement into a housing cycle downturn, but
rather a drop in the number of sales. Figure 2 illustrates the number of sales of our waterfront
homes by quarter from 2001 to 2010. As would be expected given sticky housing prices, the
drop in the number of sales occurs earlier in the first quarter of 2005, but clearly by 2006 there is
a shift in the number of sales hovering around 60 per quarter as opposed to within the range of
100 to 120 per quarter in the time period prior to this.
2
South - Size Class B/C means population size between 50,000 and 1,500,000 which is appropriate for Martin
County (Martin County population is 126,731 in 2000 and 146,318 in 2010).
3
We have housing sales price information on 2,243 identified waterfront properties in the whole county that sold on
average approximately 1.4 times each during the 2001 to 2010 timeframe. We therefore utilize the sales prices
across the 3,086 separate waterfront property sales transactions occurring between 2001 and 2010 to construct our
median waterfront sales price index. A Federal Housing Finance Agency (FHFA) repeat sale housing price index
(HPI) for the Port St. Lucie metropolitan statistical area (MSA) exists. However, the FHFA HPI for Port St. Lucie
MSA is a repeat sales index for home sales in both Martin and St. Lucie Counties, with St. Lucie County being
outside the scope of our analysis. Further, it includes all home sales, not just waterfront properties. As Boyle et al.
(2012) point out more significant appreciation and depreciation occurred on the urban fringe in areas such as
Florida, which would be primarily non-waterfront properties in the western portion of Martin County. We did not
want the appreciation and depreciation associated with non-waterfront properties driving our time market
segmentation partition. Our constructed median index would be most similar to the National Association of Realtors
(NAR) median index focused on existing home sale (http://www.stlouisfed.org/publications/re/articles/?id=2126).
Although we do have repeat sales information, we do not have enough to justify the construction of a repeat sales
index for waterfront homes only.
4
The peak of the FHFA HPI for Port St. Lucie MSA also occurred during this time frame.
6 As we are interested in explicitly accounting for these apparent housing market cycle
phases in our hedonic analysis, we formally test for market instability in our data. Specifically,
we apply a segmented regression methodology to identify the unknown structural breakpoints in
our data (both in the median price and number of sales) in order to account for them in our
hedonic estimation. Segmented or piecewise regression allows the detection of single or
multiple change points at unknown points in time given initial guess values from the user
(Muggeo, 2003). The relation between predictand and predictor is piecewise linear, which
means that two or more segments are connected at the location of the abrupt change. The
estimation procedure allows inference for all of the model’s parameters, including the location
(date in this case) of the change point(s) and corresponding confidence interval(s) (here we will
show the 90% confidence intervals). Model residuals are assumed to be Gaussian, but they are
allowed to have time-varying variance (i.e., exhibit heteroskedasticity). Segmented regression is
performed in R (R Development Core Team, 2013) using the freely available segmented package
(Muggeo, 2008).
As shown in Figure 3, we apply segmented regression to sales price data. We detect
three change points in the median sales price data, one in the third quarter of 2005, one in the
second quarter of 2007, and one in the fourth quarter of 2008. The temporal evolution of the
number of sales data is different from the median sales price data, with only two break points
detected, one in the first quarter of 2004 and one in the third quarter of 2006. Given these
identified housing market breakpoints in our data we account for their potential time effect in our
estimations by creating time interval dummy variables to represent each of them. Specifically
from Table 1: upturn is 2001 to the first quarter of 2007 and downturn from the second quarter of
2007 to 2010; from the number of sales time segmentations period A is 2001 to the first quarter
7 of 2004, period B is the second quarter of 2004 to the third quarter of 2006, and period C is the
fourth quarter of 2006 to 2010; and from the housing price time segmentations period 1 is 2001
to the third quarter of 2005; period 2 is the fourth quarter of 2005 to the second quarter of 2007,
period 3 is the third quarter of 2007 to the fourth quarter of 2008, and period 4 is the first quarter
of 2009 to 2010.
Water Quality
Water quality data for Martin County is tracked on a weekly basis by the Florida
Oceanographic Society (FOS, 2011) for nine separate locations as depicted in the upper-right
hand corner of Figure 4.5 Beside the public availability of data on the FOS website, this data is
also published weekly in the local newspapers and therefore it is reasonable to consider it a
known, important, and readily available source of water quality data. The specific water quality
locations in Martin County tracked and used for this study include North Fork, South Fork,
Winding South Fork, Wide Middle River, Narrow Middle River, Manatee Pocket, Inlet Area,
and Indian River Lagoon.6 Figure 4 additionally presents an example of the weekly water
quality data available by location. A total of five water quality variables are collected and
published weekly – temperature, pH, water visibility, salinity, and dissolved oxygen (DO). Also
reported is one non-technical compiled measure of water quality – location grade in percentage
score and corresponding letter grade.
All of the water quality measures, excluding temperature, are briefly explained in the
weekly published data and given corresponding labels of poor, fair, and good over specified
5
As of November 11, 2010 a tenth location was added, 10. Intracoastal Waterway South, which we did not use for
this analysis given it only being present for less than 2 months of our ten years timeframe.
6
Location 1. Winding North Fork is a part of St. Lucie County for which home sales were not collected.
8 ranges of values. The labels of poor, fair, and good for the water visibility, salinity, and DO
measures only are then used to calculate a weighted location grade percentage score ranging
from 0 to 100 percent with an associated A to F letter grade.7 In this way, location grade
(percentage and letter) captures the variations in the otherwise less observable yet more
ecologically meaningful technical measures of water quality such as DO or salinity, and
packages them in less technical, more familiar, and hence more understandable format for
residents and homebuyers to use. Simply, better water quality is reflected in higher location
grade (percentage and letter) scores and we assume Martin County homebuyers readily
understand and use this information in their home-buying decision. Bin and Czajkowski (2013)
found that from 2000 to 2004 water quality does in fact matter to Martin County waterfront
homebuyers via the location grade and consequently we collect and use it in our analysis.
Each home sale is assigned to a water quality location by taking the minimum Euclidean
distance to the nearest water quality monitoring station through the use of our constructed GIS
database.8 Figure 5 presents the home sales as per their assigned water quality locations from
our GIS database. The associated water quality location percentage score data for each location
is then assigned to each home by the date of the sale. Following a significant portion of the
hedonic water quality literature that utilizes the mean or median value during the year of the sale
regarding the water quality measure under investigation (Egan et al., 2009; Krysel et al., 2003;
Leggett and Bockstael, 2000; Poor, Pessagno, and Paul, 2007; Walsh, Milon, and Scrogin, 2010),
7
For a detailed description of the calculation, see Voisinet (2006). Further, up to May 2006 the corresponding letter
grade range associated with the percentage score was 96-100=A, 86-95=B, 76-85=C, 70-75=D, and 0-69=F. Post
May 2006 the corresponding letter grade range associated with the percentage score was 90-100=A, 80-89=B, 7079=C, 60-69=D, and 0-59=F. Also, up to May 2006, the labels of poor and good for pH were included in the location
grade percentage calculation. 8
We understand attributing the water quality based on the shortest straight line distance from a property to the
nearest monitoring site may lead to some errors (e.g., the properties that face to the St Lucie Estuary may be
attributed the water quality in the Indian River Lagoon). Thus, we visually checked all the waterfront properties and
used editing tools to manually reassign the homes to the appropriate water testing site if necessary.
9 we use the annual mean percentage score value during the year of the sale in the empirical
analysis.9 Water quality location percentage scores vary across locations as illustrated in Figure
6, although in general, water quality scores are higher in those locations nearer to the coast (e.g.,
Indian River Lagoon). However, there are still a significant number of lower price housing areas
having good water quality (e.g. Manatee Pocket).
Other Relevant Housing Price Attributes
Although we are accounting for market instability via our segmented regression time
dummies, there may be endogeneity concerns related to the unobservable patterns of
development. It could be that single-family homes on waterbodies with poor water quality were
developed to be either rented out or quickly resold by otherwise absentee owners. In this way,
the value of water quality would likely be inherently less significant to absentee homeowners
and/or renters occupying single-family homes in these areas.10 High vacancy rates, another
indication of housing market disequilibrium (Boyle et al., 2012; Colson and Zabel, 2013), could
also plagues these areas. A difference-in-difference (quasi-experimental) approach has been
used to control for this endogeneity in the literature (Meyer, 1995; Boyle et al., 2012; Parameter
and Pope, 2012). In lieu of this approach, we control for owner occupied housing and house
density within our spatial fixed effect hedonic models explained in the next section. We identify
whether a home is an owner occupied property via the mailing address in the Martin County
housing dataset. Specifically, we create an owner occupied dummy = 1 if the owner mailing
9
Over the ten year time period, not all locations report WQ data every week. Gaps occurred in 2004 and 2005 for all
locations stemming from the hurricanes that impacted this geographic area, and water quality location 8. Inlet Area
was particularly problematic from 2000 to 2005. Where water quality data was especially poor during these time
periods, corresponding location waterfront home sales were excluded from the analysis.
10
Based upon a census block analysis we conducted we do not believe this endogeneity issue to be present in the study
area. From this analysis, in general census block vacancy rates for single-family homes are higher where water quality
is higher. 10 address matches to location address of the property. A total of 69.6 percent of homes in the data
were owner occupied by this definition with these homes distributed throughout the various
areas. In general, the owner occupied rate are lower near the coastal area and higher near the
inland with the lowest rate (51%) in “Narrow Middle River” and the highest rate (77%) in both
“North Fork” and “Winding South Fork”. Housing density is obtained from the 2000 census
data from the Florida geographic data library website (FGDL, 2010), and using the constructed
GIS database, each home is spatially joined to its corresponding census block level.
In addition, we control for the structural characteristics of housing such as home square
footage, acreage of parcel land, the number of bathrooms, and dummies for whether the home
has pool or boatlift. Home square footage and lot size are all logged to allow for non-linearity in
them. Table 1 provides the summary statistics for housing sales price adjusted to 2010 values
and other variables included in our empirical analysis. We also calculate the travel distance from
the each waterfront house sold to the Central Business District in the city of Stuart. III. Model Estimation
Hedonic property value models are based on the intuitive notion that the component
values of various attributes of housing are reflected in price differentials (Rosen, 1974). The
basic idea underlying the model is that differential property prices reflect the way households
value different bundles of property characteristics. Residential properties are composite goods
with a variety of attributes, and observing how property values change as the level of various
attributes change, such as water quality, ceteris paribus, provides a way of estimating the
incremental value of these attributes to property owners. The hedonic price function is typically
represented as:
11 P  P ( s , n, w) ,
[1]
where P is the property price, which is a function of structural characteristics, s, neighborhood
characteristics, n, and water quality, w. The hedonic price function emerges from competitive
bidding among housing buyers when housing supply is taken as given. The housing market is in
equilibrium when buyers have maximized their utility, u  U ( s , n, w, y , α ) , subject to a budget
constraint, m  P ( s , n , w )  y , where U is a strictly concave utility function with the usual
properties, y is the non-housing numeraire good, m is consumer income, and α is the vector of
variables representing demographic factors, knowledge of water quality, and water quality
management expectations. Assuming that P() is continuously differentiable, the first derivative
of [1] with respect to any continuous attributes produces an estimate of the representative
households’ marginal willingness to pay for an additional unit of that attribute:
U ( s , n , w, y , α )
P ( s , n , w )

, z  s, n, w
z
z
[2]
where  is the marginal utility of income.
Considerable attention has been given to examining spatial dependence in estimated
hedonic price functions (Bin et al., 2008; Carruthers and Clark, 2010; Kim, Phipps, and Anselin,
2003). Spatial dependence arises because residential properties in a neighborhood share similar
location amenities or because they have similar structural characteristics due to similar timing of
construction (Anselin and Bera, 1998). The existence of spatial dependence implies that a
sample contains less information than an uncorrelated one, and that the loss of information
should be acknowledged in estimation to properly carry out statistical inference. If the relevant
spatial dependence is ignored in estimation of the hedonic price function, then the resulting
estimates could be inefficient or even inconsistent, and any inference based on the estimates may
12 result in misleading conclusions (Anselin and Bera, 1998). A general spatial model that includes
both the spatial lag and the spatial error components is specified by
ln P   ln P    i si    j n j    k wk  
i
j
k
[3]
     u ,
where ln P is the log of sales price, , , and  are the unknown parameters to be estimated,  is
an independent random error term,  is the spatial autoregressive coefficient, Π is the spatial
weighting matrix, and u is a vector of independent and identically distributed random error
terms. A spatial error model (=0) assumes that one or more omitted variables in the hedonic
equation vary spatially, and thus the error terms are spatially autocorrelated. In this
specification, the OLS estimator remains unbiased but is no longer efficient due to the
nonspherical error covariance. Efficient estimators are obtained by utilizing the particular
structure of the error covariance implied by the spatial process. A spatially lagged dependent
variable model (=0) assumes that the spatially weighted sum of neighborhood housing prices
enters as an explanatory variable in the hedonic price function. Failing to account for spatial lag
dependence leads to biased and inconsistent parameter estimates, whereas failing to account for
spatial error dependence leads to inefficiency. Regression diagnostics based on Ordinary Least
Squares (OLS) estimation and the Lagrange Multiplier (LM) test statistics guide our estimation
of the spatial hedonic model given in equation [3]. The spatial models are estimated via
maximum likelihood (ML). The estimation is implemented within the GeoDa v.0.9.5-i (2004)
environment in conjunction with ArcView GIS 3.3 extensions.
The elements of the weighting matrix are binary indicators that identify observations
within a neighborhood: ij = 1 when observations i and j are neighbors and ij = 0 otherwise. By
convention, the diagonal elements of the weighting matrix are set to zero and row elements are
13 standardized such that they sum to one, facilitating the interpretation of results derived from the
weighting matrix as an average of neighborhood values. The neighborhood is usually
determined by distance contiguity—the binary indicator is set to ‘1’ if two corresponding houses
are within some specified distance of one another. Using methods suggested by Anselin and
Bera (1998), we experimented with different weights matrices, and in this analysis use a spatial
weighting matrix that identifies properties within a 0.1 mile as nonzero elements. This spatial
weighting matrix was based on a comparison of the fits for several alternative specifications
using a range of distances. The distance cut-off of 0.1 mile resulted in overall the best fit and the
regression results reported hereafter are based on this weighting matrix.11
Since unobserved factors might be correlated with cross-city variations in the housing
market, a fixed-effect model is used in the hedonic price function estimation to control for
heterogeneity between the cities. Fixed-effect hedonic price models have the advantage of
overcoming the problem often caused by violating the standard assumptions about the error
process (i.e., homoscedasticity and independence). Recent studies found that including the
spatial fixed effects such as census tracks helps alleviating omitted variable bias and improves
the estimation of hedonic price functions (Kuminoff, Parmeter, and Pope, 2010; Kuminoff and
Pope, 2013). The cities considered for the fixed effects are shown at the bottom of Table 1.
IV. Results
The estimation results from the first-order spatial error hedonic models without the
temporal breakpoints are shown in Table 2. The significant spatial autoregressive coefficients
11
Sensitivity results for the spatial hedonic models based on varying distance cut-offs are available upon request.
The magnitudes of the coefficients change slightly as the distance cut-off changes, but signs of the coefficients and
their significance remain the same for most variables.
14 suggest that spatial dependence in our primary sample of housing prices indeed exists.
According to Model 1 with no spatial fixed effects, water quality is significant at the 1%
significance level and suggests that better water quality is associated with higher property values
between 2001 and 2010. Model 2 with the spatial fixed effects shows the results from the same
specification of Model 1, where the sign and magnitude of the coefficients remain the same
except for the pool variable. Again, our results indicate that water quality does in fact matter to
Martin County waterfront homebuyers. Model 2 provides a better description of the true model
than Model 1 in terms of information criteria, and thus we use the spatial fixed effects in the
subsequent models.
Table 3 presents the estimation results to examine the impact of the temporal breakpoints
on the implicit price of water quality. Model 1 is based on the model using the upturn and the
downturn with a break point in the first quarter of 2007, with the upturn periods utilized as the
omitted dummy variable category. Model 2 is based on the temporal evolution of the number of
sales data, with two break points detected, one in the first quarter of 2004 and one in the third
quarter of 2006. Model 3 is based on the three change points in the median sales price data, one
in the third quarter of 2005, one in the second quarter of 2007, and one in the fourth quarter of
2008. Overall, the estimated coefficients for the time period variables indicate that the housing
market expansion and contraction between 2001 and 2010 did not influence the coastal
waterfront housing prices in Martin County, FL. This seems to indicate the waterfront housing
properties used in our analysis were immune from the initial market contraction, different from
the mean waterfront home sales price data shown in Figure 1.
The interactions of the water quality grade and the time period indicators show that the
effects of water quality on housing prices do not change significantly throughout the housing
15 market fluctuations with an exception of the last recovery period in Model 3. Our finding
indicates that the improvement in water quality is valued by waterfront property owners and the
amenity effect does not vanish even during the worst periods of the housing market. Unlike the
earlier findings that recessions crowd out concern for the environmental quality (Cho et al.,
2011; Kahn and Kotchen, 2010), our results appear to suggest that water quality remains as a
high priority even in the worst of a housing market downturn.
Table 4 reports the estimated marginal willingness to pay (WTP) for the water quality
percentage grade, based on the results in Table 2. A bootstrapping procedure is used to generate
confidence intervals for the marginal willingness to pay (Krinsky and Robb, 1986). The
procedure generates 1,000 random variables from the distribution of the estimated parameters
and computes 1,000 marginal WTP estimates. The 90% confidence bounds for the marginal
WTP estimates are found by dropping the top and bottom 5% of the estimates. Increasing the
water quality score by one percentage point, evaluated at the sample mean price, resulted in a
premium of $1,754. The 90% lower and upper bounds of the marginal WTP are $86 and $3,276,
respectively.
While we are primarily interested in the results of our water quality and housing market
cycles, we briefly discuss results from other variables used. From the Model 2 in Table 2, we
find that all the estimated coefficients of the structural variables have the expected signs. While
the number of bathrooms, total structure square footage, and lot size are significant at the 1%, the
dummy variable for basement is significant at the 5% level. The square footage of the house and
the number of bathrooms appear to be important structural characteristics. Adding one
additional bathroom is estimated to raise the mean sales price by approximately 12.7 percent
holding other factors constant.
16 Lastly, we consider the impact of neighborhood characteristics on housing price. As
expected, the influence of the distance from central business district is negative and significant.
The marginal effect for housing density is not statistically significant. The statistically
significant and negative coefficient on the owner occupied variable indicates that higher pricing
houses are in the area with a high rental rate. This could indicate that high pricing houses in the
study area are secondary houses.
V. Discussion and Conclusions
We use weekly water quality location grade scores to conduct a hedonic property analysis
of Martin County, FL waterfront home sales during the 2001 to 2010 South Florida real estate
market upturn and downturn to investigate the relationship between water quality and housing
prices in this geographic area. We account for the significant real estate market fluctuations
during this time period by partitioning the real estate market into its distinct time segments based
on the median sales price and the number of housing sales. Our results show that water quality is
perceived to be valuable by waterfront homebuyers in Martin County, FL throughout the real
estate expansion and contraction periods. The homebuyers perceive that one percentage point
increase in water quality is worth $1,754 on average during the period of 2001 to 2010.
These results first suggest that more effort certainly needs to be aimed at understanding
the impact of water quality at particular time periods, especially given substantial changes to the
underlying (assumed) housing market equilibrium brought about by economy-wide periods of
expansion and contraction (Carruthers et al., 2010; Colson and Zabel, 2013; Kuminoff and Pope,
2013). Here, we have accounted for the associated significant real estate fluctuations in this area
through flexible econometric controls in space and time (Boyle et al., 2012) by applying a
17 statistical methodology that identifies housing market price instability over time, interacting
water quality with these identified market segmentations, and then embedding these interactions
within a spatial fixed effect model to further account for any spatial heterogeneity in the
waterfront market.
Secondly, our results suggest that there may be in fact significant economic returns
associated with water quality protection in an economic contraction. This finding contrasts with
the existing evidence that recessions will crowd out concern for the environment and therefore
potentially results in lower environmental amenity values during a period of economic hardship
(Cho et al., 201; Kahn and Kotchen, 2010). Assuming that environmental quality is a normal
good such that people are willing to spend more on it as income increases all else being the same
(Bruneau and Echevarria, 2003), it is rational to expect that concern for the environment would
be eroded during a recession and reflected in lower implicit prices for environmental amenity
stemming from lower incomes. However, one could imagine several competing hypotheses
whereby the impact of recessions on an environmental amenity is instead reflected in higher
implicit prices for the environmental amenity in a housing market setting such as the one in this
study. Firstly, given that waterfront property is in limited supply in general, those waterfront
homes with good water quality associated with them potentially become more valuable to
homebuyers during a recession – a type of value-focused investment.12 This is certainly
plausible in our waterfront housing market in Martin County, Florida where good water quality
and protecting the environment is a priority, economic and otherwise (FLDEP, 2010; SFWMDa,
2010; SFWMDb, 2010). In this way, the social importance placed on good water quality in
Martin County would outweigh the negative shock to the economy brought about by the real
12
An idea similar to investors seeking out higher returns from value stocks during a recession. Motley Fool (2011)
found that returns to value stocks outperformed growth stocks during periods of recessions since 1969.
18 estate collapse (Kahn and Kotchen, 2010). Secondly, the mean home sales price value from our
dataset of $810,000 dollars indicates relatively high-income homebuyers participating in this real
estate market. Elliot et al. (1997) find a significant positive relationship between higher levels of
income and one’s willingness to invest in environmental spending. Taking higher education as a
proxy for higher income, Kahn and Kotchen (2010) also find evidence that concern for the
environment is more likely to be a priority as education (income) increases.
A couple of caveats, however, need to be addressed. First, our finding is based on the
assumption of rapid price adjustment within the housing market. Price may adjust only
gradually over a number of periods in response to the change in water quality given the timeconsuming search and negotiation process in housing market transactions. Future studies may
examine gradual price adjustment by the observed tendency for serial correlation in housing
prices. Second, there is compelling evidence for a strong correlation between coastal amenities
and property values. Although our data include only waterfront properties with similar coastal
amenities, we have no detailed information on other amenity factors, such as ocean viewshed and
beach access, that are highly valued in coastal housing markets (Bin et al., 2008; Hazen and
Sawyer, 2008).
It is important to note that our estimates provide only a limited measure of total economic
value of water quality improvement as perceived by nearby residential property owners. Improved water quality would generate many ecological, environmental, and recreational
benefits. The value of these services is likely not to be fully reflected in property values.
Nonetheless, evidence that water quality improvement is associated with higher property values
in tough economic times may help resource managers and policy makers make informed
decisions during these periods of constrained financial resources.
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23 Table 1. Summary Statistics
House sale price
Water quality
Upturn
Downturn
Period A
Period B
Period C
Period 1
Period 2
Period 3
Period 4
Number of bathroom
Lot size
Total square footage
Basement
Pool
Deck
Boat lift
Distance to CBD
Owner occupied
House density
Jensen
Palm City
Sewall
Stuart
Mean
810,111.28
76.80
0.61
0.39
0.26
0.19
0.55
0.32
0.21
0.15
0.32
2.33
0.50
2,874.59
0.02
0.59
104.74
0.34
26,300.57
0.70
2.58
0.04
0.37
0.06
0.53
Std. Dev.
830,792.96
14.14
0.49
0.49
0.44
0.39
0.50
0.47
0.40
0.36
0.47
1.07
0.53
1,549.40
0.14
0.49
308.91
0.47
10,079.21
0.46
18.11
0.21
0.48
0.24
0.50
Notes: The total number of observations is 1,526.
24 Minimum
46,400.47
31.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.04
328.00
0.00
0.00
0.00
0.00
227.00
0.00
0.00
0.00
0.00
0.00
0.00
Maximum
7,289,844.00
100.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
8.00
13.50
13,687.00
1.00
1.00
3,523.00
1.00
49,600.00
1.00
553.00
1.00
1.00
1.00
1.00
Table 2. Estimation Results for the Hedonic Models without Temporal Breakpoints
Constant
Water quality
Number of bathroom
Log of lot size
Log of square footage
Basement
Pool
Deck
Boat lift
Distance to CBD
Owner occupied
House density
Lambda
Log likelihood
Akaike info criterion
Schwarz criterion
Model 1
Coefficient
7.713a
0.003a
0.128a
0.108a
0.757a
0.224c
0.056
1.51e-04a
0.130a
-0.080a
-0.217a
-0.001
0.088a
-1501.709
3027.420
3091.383
Std. Error
0.470
0.001
0.019
0.026
0.047
0.117
0.037
5.39e-05
0.035
0.030
0.037
0.001
0.029
Model 2
Coefficient
7.647a
0.002c
0.127a
0.087a
0.747a
0.232b
0.071c
1.29e-04b
0.130a
-0.056c
-0.216a
-0.001
0.065b
-1484.070
2998.140
3078.096
Std. Error
0.463
0.001
0.019
0.026
0.046
0.116
0.037
5.35e-05
0.035
0.030
0.037
0.001
0.029
Notes: Model 1 has no fixed effects and Model 2 is with the spatial fixed effects. Dependent variable is natural log
of sales price. Superscripts a, b, and c denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
25 Table 3. Estimation Results for the Hedonic Models with Temporal Breakpoints
Constant
Downturn
Period B
Period C
Period 2
Period 3
Period 4
Water quality (WQ)
WQ*Downturn
WQ*Period B
WQ*Period C
WQ*Period 2
WQ*Period 3
WQ*Period 4
Number of bathroom
Log of lot size
Log of square footage
Basement
Pool
Deck
Boat lift
Distance to CBD
Owner occupied
House density
Lambda
Log likelihood
Akaike info criterion
Schwarz criterion
Model 1
Coefficient Std. Error
7.667a
0.466
-0.123
0.215
Model 2
Coefficient Std. Error
7.577a
0.470
0.774a
0.334
0.002c
2.6e-04
0.129a
0.090a
0.744a
0.237b
0.070c
1.3e-04b
0.130a
-0.054c
-0.223a
-0.001
0.068b
-1479.53
2993.05
3083.67
0.001
0.003
0.235
0.227
0.006b
0.002
-0.002
-0.002
0.003
0.003
0.125a
0.116a
0.711a
0.161
0.066c
8.2e-05
0.138a
-0.071b
-0.179a
-0.001
0.096a
-1411.76
2861.53
2962.81
0.019
0.026
0.046
0.116
0.037
5.3e-05
0.035
0.030
0.037
0.001
0.029
Model 3
Coefficient Std. Error
7.622a
0.459
0.018
0.025
0.044
0.111
0.035
5.1e-05
0.033
0.029
0.035
0.001
0.029
0.654a
0.136
0.403c
0.006a
0.223
0.330
0.227
0.002
-0.002
0.001
-0.005c
0.120a
0.111a
0.709a
0.133
0.064c
9.0e-05c
0.137a
-0.066b
-0.189a
-0.001
0.065b
-1403.40
2848.80
2960.74
0.003
0.004
0.003
0.018
0.025
0.044
0.111
0.035
5.1e-05
0.033
0.029
0.035
0.001
0.029
Notes: All models are estimated with the spatial fixed effects Dependent variable is natural log of sales price.
Superscripts a, b, and c denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
26 Table 4. Estimation Results for the Marginal Willingness to Pay
Water Quality
90% Lower Bound
$86.09
Mean WTP
$1,754.13
90% Upper Bound
$3,276.30
Notes: The marginal willingness to pay is evaluated at the observed mean values. A marginal change is
defined as one percentage point increase. A bootstrapping procedure is used to generate 90% confidence
intervals for the marginal willingness to pay (Krinsky and Robb, 1986). The reported confidence intervals are
based on 1,000 sets of random parameter vectors from the distribution of the estimated parameters.
27 Figure 1. Quarterly Median House Sales Price for Waterfront Properties 2001–2010
28 Figure 2. Quarterly Number of Sales for Waterfront Properties 2001–2010
29 Figure 3. Temporal Breakpoints in Median Sales Price (top) and Number of Sales (bottom)
Notes: The grey circles refer to the observations, while the black solid lines are the results of segmented regression.
The black circles towards the top of each panel refer to the estimated location of the break, together with the
corresponding 90% confidence intervals.
30 Figure 4. Example of Martin County Weekly Water Quality Data & Collection Locations
(Source: FOS, 2011)
31 Figure 5. Martin County Waterfront Home Sales by Water Quality Monitoring Location
32 1
Average Water Quality Percentage 0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
IRL
Inlet Area Manatee Narrow
Pocket Middle
River
Mean
Max
North
Fork
South
Fork
Wide
Middle
River
Winding
South
Fork
Min
Figure 6: Average Water Quality Percentage Score by Location from 2001 to 2010
33