1# RECORD 527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd2# TITLE: Geospatial Heterogeneity in Monetary Value of Proximity to Waterfront Ecosystem Services in the Gulf of Mexico3# YEAR: 2021.0 VENUE: Water DOI: 10.3390/w131724014# AUTHORS: Ram P. Dahal; R. Grala; J. Gordon; I. Munn; D. Petrolia5# PAGES: 20 CANDIDATE TABLE PAGES (1-based): [9]6# 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.789===== PAGE 1 (score 13, numeric-paren 0, table-words 0, var-words 13) =====1011water12Article13Geospatial Heterogeneity in Monetary Value of Proximity to14Waterfront Ecosystem Services in the Gulf of Mexico15Ram P. Dahal 1,*16, Robert K. Grala 2, Jason S. Gordon 3, Ian A. Munn 2 and Daniel R. Petrolia 4171819Citation: Dahal, R.P.; Grala, R.K.;20Gordon, J.S.; Munn, I.A.; Petrolia, D.R.21Geospatial Heterogeneity in22Monetary Value of Proximity to23Waterfront Ecosystem Services in the24Gulf of Mexico. Water 2021, 13, 2401.25https://doi.org/10.3390/w1317240126Academic Editor: Maria Mimikou27Received: 2 August 202128Accepted: 27 August 202129Published: 31 August 202130Publisher’s Note: MDPI stays neutral31with regard to jurisdictional claims in32published maps and institutional affil-33iations.34Copyright: © 2021 by the authors.35Licensee MDPI, Basel, Switzerland.36This article is an open access article37distributed38under39the40terms41and42conditions of the Creative Commons43Attribution (CC BY) license (https://44creativecommons.org/licenses/by/454.0/).46147Wisconsin Department of Natural Resources, P.O. Box 7921, Madison, WI 53707-7921, USA48249Department of Forestry, Mississippi State University, P.O. Box 9681, Starkville, MS 39762, USA;50r.grala@msstate.edu (R.K.G.); iam1@msstate.edu (I.A.M.)51352Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA;53jason.gordon@uga.edu54455Department of Agricultural Economics, Mississippi State University, P.O. Box 5187, Starkville, MS 39762, USA;56d.petrolia@msstate.edu57*58Correspondence: ram.dahal@wisconsin.gov59Abstract: Open spaces, including waterfront areas, are critical to coastal communities and provide60many benefits, including recreation opportunities, economic development, ecological benefits, and61other ecosystem services. However, it is not clear how values of waterfront ecosystem services vary62across geographical areas which prevents development and adoption of site-specific natural resource63conservation plans and suitable long-term land management strategies. This study estimated the64monetary value of distance to different waterfront types in coastal counties of Mississippi and65Alabama (U.S.) using a geographically weighted regression (GWR) approach as an extension to a66traditional hedonic pricing method (HPM). In addition, the study utilized publicly available data from67the U.S. Census Bureau instead of certified rolls of county property assessors and Multiple Listing68Service (MLS) data which can be costly and difficult to obtain. Residents valued most waterfront69types which was reflected in greater assessed prices for houses in proximity to these waterfronts.70However, the value of ecosystem services associated with waterfronts differed geospatially. The71marginal implicit prices ranged from −$6343 to $6773 per km depending on a waterfront type. These72estimates will be useful to city developers, land-use planners, and other stakeholders to make more73informed and balanced decisions related to natural resource preservation associated with coastal74areas, land-use planning, and zoning. In addition, information from this study can be used in75developing healthy living environments where local economy can benefit from increased property76tax revenues associated with waterfronts and their ecosystem services.77Keywords: geographically weighted regression; hedonic pricing method; environmental amenities;78spatial variability; marginal implicit price; open space791. Introduction80As coastal areas experience rapid population growth, waterfront open space is increas-81ingly limited resulting in overuse and a decreased potential to provide ecological, sociocul-82tural, and economic benefits [1,2]. Urban waterfronts represent publicly or privately-owned83landscapes that are part of a city and in contact with a water body such as an ocean, lake,84river, or estuary [3–6]. These areas provide many benefits and ecosystem services such85as natural scenery; ecological benefits such as wildlife habitat and urban heat reduction;86recreational opportunities such as hiking and boating; and produce economic benefits87related to higher real estate values and increased tax revenues [7–10]. Waterfronts are88therefore important to coastal communities and their visitors, and future land-use planning89decisions should balance their preservation and development in ways that enhance cultural,90environmental, and economic values of open spaces and adjacent areas. Considering the91Water 2021, 13, 2401. https://doi.org/10.3390/w1317240192https://www.mdpi.com/journal/water939495===== PAGE 9 (score 86, numeric-paren 33, table-words 20, var-words 13) =====9697Water 2021, 13, 2401989 of 2099value had a nonlinear relationship with house age and the value decreased at a decreasing100rate with age.101Table 2. Parameter estimates from the ordinary least square (OLS) global and geographically weighted regression (GWR)102local models used to estimate the monetary value associated with proximity to waterfront ecosystem services in Mississippi103and Alabama (U.S.), and results of the spatial variability test.104Variables105Global Model106Local Model107Test for Spatial108Variability (Difference109of Criterion) a110Parameter111Coefficient112White Std.113Error114Min.115Lower116Quartile117Median118Upper119Quartile120Max.121Intercept1228.647 ***1230.9151247.5231257.7461267.89112710.04012810.1781291575.438 ***130Rooms1310.059 *1320.0311330.0311340.0421350.0491360.0981370.104138−35.518 ***139House Age140−0.014 ***1410.003142−0.017143−0.016144−0.015145−0.014146−0.013147−14.978 ***148House Age–squared1490.000 ***1500.0001510.0001520.0001530.0001540.0001550.0001560.432157Ln(Income)1580.420 ***1590.0671600.2661610.2731620.4961630.5071640.5251655057.518 ***166Median Age1670.007 **1680.003169−0.002170−0.0011710.0001720.0141730.014174−12.500 ***175Poverty176−0.0011770.002178−0.002179−0.002180−0.0011810.0011820.0011831.059184Ln(Pop den)1850.0001860.021187−0.031188−0.0261890.0141900.0211910.036192−20.126 ***193Vacant194−0.009 **1950.004196−0.012197−0.009198−0.008199−0.008200−0.0062011.249202Recreation2030.019 ***2040.0062050.0092060.0152070.0222080.0242090.033210−0.832 *211Ln(Road)2120.0042130.014214−0.0042150.0022160.0042170.0092180.028219−15.059 ***220Ln(Rail)2210.0132220.013223−0.021224−0.0082250.0112260.0172270.028228−15.908 ***229Ln(School)2300.0002310.022232−0.021233−0.0162340.0112350.0122360.016237−49.912 ***238Ln(Shopping centers)239−0.036 *2400.021241−0.051242−0.033243−0.029244−0.024245−0.013246−51.620 ***247Ln(Hospital)248−0.086 ***2490.025250−0.157251−0.143252−0.137253−0.055254−0.032255−29.118 ***256Ln(Airport)2570.0062580.0262590.0002600.0162610.0522620.0622630.076264−149.902 ***265Ln(Park)266−0.0102670.021268−0.036269−0.0322700.0102710.0162720.023273−37.697 ***274Ln(Bay)275−0.059 ***2760.009277−0.064278−0.057279−0.054280−0.052281−0.047282−9.532 ***283Ln(River)284−0.0042850.012286−0.032287−0.028288−0.025289−0.008290−0.005291−2.123 **292Ln(Stream)293−0.030 *2940.017295−0.078296−0.059297−0.055298−0.046299−0.033300−76.215 ***301Ln(Bayou)3020.038 **3030.0183040.0323050.0363060.0483070.0613080.073309−47.041 ***310Ln(Water)311−0.029 ***3120.011313−0.040314−0.035315−0.015316−0.014317−0.012318−12.116 ***319R23200.5183210.572322R2 adj3230.5003240.530325AICc326447.863327427.544328CV3290.1233300.120331Residual Sum of sq.33269.20133361.425334Bandwidth (m.)335533.083336N337620338620339Note: The dependent variable is ln (housing value), * p < 0.10, ** p < 0.05, *** p < 0.01, and a negative value of difference criterion suggests a340spatial variability.341Neighborhood characteristics had both positive and negative effects on house values342(Table 2). Median household income (p < 0.001) and percentage of houses used for recre-343ational purposes (p = 0.001) were positively associated with house value in OLS model344as well as in GWR model. A 1.0% increase in income and percentage of houses used for345recreational purposes were associated with house value increases of 0.27% to 0.53% and3460.9% to 3.30%, respectively, in GWR model. However, the percentage of vacant houses347was negatively related with house values. Median household age had both positive and348negative relationship with house value in GWR model. Distance-related neighborhood349variables, such as distance to the nearest shopping center and hospital, were negatively350related to house value. With a 1.0% increase in distance to the nearest shopping center and351hospital, a house value decreased by 0.013% to 0.033% and 0.032% to 0.143%, respectively,352in GWR model.3533.2. Ecosystem Service Attributes354Proximities to different waterfront types and their ecosystem services were related355to house values. Results from the OLS model showed that of six variables, four were356statistically significant at the 10% or better significance level. Stationarity for each of the six357variables was rejected, suggesting the monetary value of ecosystem services was spatially358heterogeneous. The OLS model indicated that proximity to selected waterfront types (bay,359360361===== PAGE 10 (score 9, numeric-paren 0, table-words 3, var-words 3) =====362363Water 2021, 13, 240136410 of 20365stream, and other water body) was associated with increased house values. However,366proximity to a bayou was negatively related to the house value.367The relationship of distance to different waterfronts and house values, holding all368other variables constant, is plotted in Figure 3. The figure shows downward sloping369relationships between house value and distance to bay, river, stream, and other water370body and an upward sloping relationship with distance to a bayou. A downward sloping371relationship implies that houses located nearer to waterfronts (bay, river, stream, and other372water body) were assessed higher values, whereas upward slope indicated that houses373located distant from waterfronts (bayou) were assessed higher values.374Figure 3. Relationships between assessed house value and proximity to waterfront ecosystem services.375Marginal implicit prices for each of the significant waterfront coefficients were esti-376mated at the mean assessed house value of $139,204 and distance of one mile (1.609 km) to377the selected waterfront types (Table 3) using Equation (1). The GWR model indicated that378the implicit price of distance to a bay varied across the study area (Figure 4A). The local379parameter coefficients of distance to a bay estimated by the GWR model were significant380throughout the study area suggesting benefits of a bay were reflected in the house value381throughout the study area and the implicit price was estimated to be between $4039 and382$5574 for each 1 km decrease in distance to a bay (Table 3). The GWR model suggested that383the marginal implicit price, assumed to be constant across the study area in the OLS model,384in fact, varied between locations and the houses were valued at a relatively higher rate on385peripheries of urban centers than other areas (Figure 4A).386Table 3. Estimated marginal implicit prices per 1 km decrease in distance to waterfront ecosystem387services.388Waterfront Type389Marginal Implicit Price per km390OLS391GWR *392Nearest bay393$5133394$4039 to $5574395Nearest stream396$2566397$2898 to $6773398Nearest river399NS400$2455 to $2802401Nearest bayou402−$3267403−$3608 to −$6343404Nearest other water body405$2534406$2287 to $3432407Note: NS represents a statistically insignificant coefficient. * Only significant GWR estimates were translated to408US$ values.409