# RECORD 527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd
# TITLE: Geospatial Heterogeneity in Monetary Value of Proximity to Waterfront Ecosystem Services in the Gulf of Mexico
# YEAR: 2021.0  VENUE: Water  DOI: 10.3390/w13172401
# AUTHORS: Ram P. Dahal; R. Grala; J. Gordon; I. Munn; D. Petrolia
# PAGES: 20  CANDIDATE TABLE PAGES (1-based): [9]
# ABSTRACT: Open spaces, including waterfront areas, are critical to coastal communities and provide many benefits, including recreation opportunities, economic development, ecological benefits, and other ecosystem services. However, it is not clear how values of waterfront ecosystem services vary across geographical areas which prevents development and adoption of site-specific natural resource conservation plans and suitable long-term land management strategies. This study estimated the monetary value of distance to different waterfront types in coastal counties of Mississippi and Alabama (U.S.) using a geographically weighted regression (GWR) approach as an extension to a traditional hedonic pricing method (HPM). In addition, the study utilized publicly available data from the U.S. Census Bureau instead of certified rolls of county property assessors and Multiple Listing Service (MLS) data which can be costly and difficult to obtain. Residents valued most waterfront types which was reflected in greater assessed prices for houses in proximity to these waterfronts. However, the value of ecosystem services associated with waterfronts differed geospatially. The marginal implicit prices ranged from −$6343 to $6773 per km depending on a waterfront type. These estimates will be useful to city developers, land-use planners, and other stakeholders to make more informed and balanced decisions related to natural resource preservation associated with coastal areas, land-use planning, and zoning. In addition, information from this study can be used in developing healthy living environments where local economy can benefit from increased property tax revenues associated with waterfronts and their ecosystem services.


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water
Article
Geospatial Heterogeneity in Monetary Value of Proximity to
Waterfront Ecosystem Services in the Gulf of Mexico
Ram P. Dahal 1,*
, Robert K. Grala 2, Jason S. Gordon 3, Ian A. Munn 2 and Daniel R. Petrolia 4





Citation: Dahal, R.P.; Grala, R.K.;
Gordon, J.S.; Munn, I.A.; Petrolia, D.R.
Geospatial Heterogeneity in
Monetary Value of Proximity to
Waterfront Ecosystem Services in the
Gulf of Mexico. Water 2021, 13, 2401.
https://doi.org/10.3390/w13172401
Academic Editor: Maria Mimikou
Received: 2 August 2021
Accepted: 27 August 2021
Published: 31 August 2021
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Copyright: © 2021 by the authors.
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conditions of the Creative Commons
Attribution (CC BY) license (https://
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4.0/).
1
Wisconsin Department of Natural Resources, P.O. Box 7921, Madison, WI 53707-7921, USA
2
Department of Forestry, Mississippi State University, P.O. Box 9681, Starkville, MS 39762, USA;
r.grala@msstate.edu (R.K.G.); iam1@msstate.edu (I.A.M.)
3
Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA;
jason.gordon@uga.edu
4
Department of Agricultural Economics, Mississippi State University, P.O. Box 5187, Starkville, MS 39762, USA;
d.petrolia@msstate.edu
*
Correspondence: ram.dahal@wisconsin.gov
Abstract: Open spaces, including waterfront areas, are critical to coastal communities and provide
many benefits, including recreation opportunities, economic development, ecological benefits, and
other ecosystem services. However, it is not clear how values of waterfront ecosystem services vary
across geographical areas which prevents development and adoption of site-specific natural resource
conservation plans and suitable long-term land management strategies. This study estimated the
monetary value of distance to different waterfront types in coastal counties of Mississippi and
Alabama (U.S.) using a geographically weighted regression (GWR) approach as an extension to a
traditional hedonic pricing method (HPM). In addition, the study utilized publicly available data from
the U.S. Census Bureau instead of certified rolls of county property assessors and Multiple Listing
Service (MLS) data which can be costly and difficult to obtain. Residents valued most waterfront
types which was reflected in greater assessed prices for houses in proximity to these waterfronts.
However, the value of ecosystem services associated with waterfronts differed geospatially. The
marginal implicit prices ranged from −$6343 to $6773 per km depending on a waterfront type. These
estimates will be useful to city developers, land-use planners, and other stakeholders to make more
informed and balanced decisions related to natural resource preservation associated with coastal
areas, land-use planning, and zoning. In addition, information from this study can be used in
developing healthy living environments where local economy can benefit from increased property
tax revenues associated with waterfronts and their ecosystem services.
Keywords: geographically weighted regression; hedonic pricing method; environmental amenities;
spatial variability; marginal implicit price; open space
1. Introduction
As coastal areas experience rapid population growth, waterfront open space is increas-
ingly limited resulting in overuse and a decreased potential to provide ecological, sociocul-
tural, and economic benefits [1,2]. Urban waterfronts represent publicly or privately-owned
landscapes that are part of a city and in contact with a water body such as an ocean, lake,
river, or estuary [3–6]. These areas provide many benefits and ecosystem services such
as natural scenery; ecological benefits such as wildlife habitat and urban heat reduction;
recreational opportunities such as hiking and boating; and produce economic benefits
related to higher real estate values and increased tax revenues [7–10]. Waterfronts are
therefore important to coastal communities and their visitors, and future land-use planning
decisions should balance their preservation and development in ways that enhance cultural,
environmental, and economic values of open spaces and adjacent areas. Considering the
Water 2021, 13, 2401. https://doi.org/10.3390/w13172401
https://www.mdpi.com/journal/water


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Water 2021, 13, 2401
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value had a nonlinear relationship with house age and the value decreased at a decreasing
rate with age.
Table 2. Parameter estimates from the ordinary least square (OLS) global and geographically weighted regression (GWR)
local models used to estimate the monetary value associated with proximity to waterfront ecosystem services in Mississippi
and Alabama (U.S.), and results of the spatial variability test.
Variables
Global Model
Local Model
Test for Spatial
Variability (Difference
of Criterion) a
Parameter
Coefficient
White Std.
Error
Min.
Lower
Quartile
Median
Upper
Quartile
Max.
Intercept
8.647 ***
0.915
7.523
7.746
7.891
10.040
10.178
1575.438 ***
Rooms
0.059 *
0.031
0.031
0.042
0.049
0.098
0.104
−35.518 ***
House Age
−0.014 ***
0.003
−0.017
−0.016
−0.015
−0.014
−0.013
−14.978 ***
House Age–squared
0.000 ***
0.000
0.000
0.000
0.000
0.000
0.000
0.432
Ln(Income)
0.420 ***
0.067
0.266
0.273
0.496
0.507
0.525
5057.518 ***
Median Age
0.007 **
0.003
−0.002
−0.001
0.000
0.014
0.014
−12.500 ***
Poverty
−0.001
0.002
−0.002
−0.002
−0.001
0.001
0.001
1.059
Ln(Pop den)
0.000
0.021
−0.031
−0.026
0.014
0.021
0.036
−20.126 ***
Vacant
−0.009 **
0.004
−0.012
−0.009
−0.008
−0.008
−0.006
1.249
Recreation
0.019 ***
0.006
0.009
0.015
0.022
0.024
0.033
−0.832 *
Ln(Road)
0.004
0.014
−0.004
0.002
0.004
0.009
0.028
−15.059 ***
Ln(Rail)
0.013
0.013
−0.021
−0.008
0.011
0.017
0.028
−15.908 ***
Ln(School)
0.000
0.022
−0.021
−0.016
0.011
0.012
0.016
−49.912 ***
Ln(Shopping centers)
−0.036 *
0.021
−0.051
−0.033
−0.029
−0.024
−0.013
−51.620 ***
Ln(Hospital)
−0.086 ***
0.025
−0.157
−0.143
−0.137
−0.055
−0.032
−29.118 ***
Ln(Airport)
0.006
0.026
0.000
0.016
0.052
0.062
0.076
−149.902 ***
Ln(Park)
−0.010
0.021
−0.036
−0.032
0.010
0.016
0.023
−37.697 ***
Ln(Bay)
−0.059 ***
0.009
−0.064
−0.057
−0.054
−0.052
−0.047
−9.532 ***
Ln(River)
−0.004
0.012
−0.032
−0.028
−0.025
−0.008
−0.005
−2.123 **
Ln(Stream)
−0.030 *
0.017
−0.078
−0.059
−0.055
−0.046
−0.033
−76.215 ***
Ln(Bayou)
0.038 **
0.018
0.032
0.036
0.048
0.061
0.073
−47.041 ***
Ln(Water)
−0.029 ***
0.011
−0.040
−0.035
−0.015
−0.014
−0.012
−12.116 ***
R2
0.518
0.572
R2 adj
0.500
0.530
AICc
447.863
427.544
CV
0.123
0.120
Residual Sum of sq.
69.201
61.425
Bandwidth (m.)
533.083
N
620
620
Note: The dependent variable is ln (housing value), * p < 0.10, ** p < 0.05, *** p < 0.01, and a negative value of difference criterion suggests a
spatial variability.
Neighborhood characteristics had both positive and negative effects on house values
(Table 2). Median household income (p < 0.001) and percentage of houses used for recre-
ational purposes (p = 0.001) were positively associated with house value in OLS model
as well as in GWR model. A 1.0% increase in income and percentage of houses used for
recreational purposes were associated with house value increases of 0.27% to 0.53% and
0.9% to 3.30%, respectively, in GWR model. However, the percentage of vacant houses
was negatively related with house values. Median household age had both positive and
negative relationship with house value in GWR model. Distance-related neighborhood
variables, such as distance to the nearest shopping center and hospital, were negatively
related to house value. With a 1.0% increase in distance to the nearest shopping center and
hospital, a house value decreased by 0.013% to 0.033% and 0.032% to 0.143%, respectively,
in GWR model.
3.2. Ecosystem Service Attributes
Proximities to different waterfront types and their ecosystem services were related
to house values. Results from the OLS model showed that of six variables, four were
statistically significant at the 10% or better significance level. Stationarity for each of the six
variables was rejected, suggesting the monetary value of ecosystem services was spatially
heterogeneous. The OLS model indicated that proximity to selected waterfront types (bay,


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Water 2021, 13, 2401
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stream, and other water body) was associated with increased house values. However,
proximity to a bayou was negatively related to the house value.
The relationship of distance to different waterfronts and house values, holding all
other variables constant, is plotted in Figure 3. The figure shows downward sloping
relationships between house value and distance to bay, river, stream, and other water
body and an upward sloping relationship with distance to a bayou. A downward sloping
relationship implies that houses located nearer to waterfronts (bay, river, stream, and other
water body) were assessed higher values, whereas upward slope indicated that houses
located distant from waterfronts (bayou) were assessed higher values.
Figure 3. Relationships between assessed house value and proximity to waterfront ecosystem services.
Marginal implicit prices for each of the significant waterfront coefficients were esti-
mated at the mean assessed house value of $139,204 and distance of one mile (1.609 km) to
the selected waterfront types (Table 3) using Equation (1). The GWR model indicated that
the implicit price of distance to a bay varied across the study area (Figure 4A). The local
parameter coefficients of distance to a bay estimated by the GWR model were significant
throughout the study area suggesting benefits of a bay were reflected in the house value
throughout the study area and the implicit price was estimated to be between $4039 and
$5574 for each 1 km decrease in distance to a bay (Table 3). The GWR model suggested that
the marginal implicit price, assumed to be constant across the study area in the OLS model,
in fact, varied between locations and the houses were valued at a relatively higher rate on
peripheries of urban centers than other areas (Figure 4A).
Table 3. Estimated marginal implicit prices per 1 km decrease in distance to waterfront ecosystem
services.
Waterfront Type
Marginal Implicit Price per km
OLS
GWR *
Nearest bay
$5133
$4039 to $5574
Nearest stream
$2566
$2898 to $6773
Nearest river
NS
$2455 to $2802
Nearest bayou
−$3267
−$3608 to −$6343
Nearest other water body
$2534
$2287 to $3432
Note: NS represents a statistically insignificant coefficient. * Only significant GWR estimates were translated to
US$ values.