1# RECORD 467b5db9559e6802d7f96e2e470749e1440b69fd2# TITLE: VALUING OPEN SPACE IN A MARSHLAND ENVIRONMENT: DEVELOPMENT ALTERNATIVES FOR COASTAL GEORGIA3# YEAR: 2016.0 VENUE: Journal of Agricultural and Applied Economics DOI: 10.1017/aae.2016.254# AUTHORS: Ajita Atreya; Warren P. Kriesel; J. Mullen5# PAGES: 20 CANDIDATE TABLE PAGES (1-based): [13, 14]6# ABSTRACT: Abstract In a coastal environment, open space can exist as land set aside by a real estate developer or as tidal marshland. In this article, we determine the relative values of both types of open spaces in a coastal county in Georgia using a spatial hedonic price framework. Results indicate that (1) there is a price premium associated with the marshlands and (2) developers have market incentives to incorporate more open space into their designs of residential subdivisions. Regarding marshlands, we also find that accessibility is an important variable that adds much more value to a property than just the proximity.789===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) =====1011Journal of Agricultural and Applied Economics, 48, 4 ( 2016): 383–40212C⃝2016 The Author(s).13This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence14(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided15the original work is properly cited.16doi:10.1017/aae.2016.2517VALUING OPEN SPACE IN A MARSHLAND18ENVIRONMENT: DEVELOPMENT19ALTERNATIVES FOR COASTAL GEORGIA20AJITA ATREYA ∗21The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania22WARREN KRIESEL23Department of Agriculture and Applied Economics, University of Georgia, Athens, Georgia24JEFFREY D. MULLEN25Department of Agriculture and Applied Economics, University of Georgia, Athens, Georgia26Abstract. In a coastal environment, open space can exist as land set aside by a real27estate developer or as tidal marshland. In this article, we determine the relative28values of both types of open spaces in a coastal county in Georgia using a spatial29hedonic price framework. Results indicate that (1) there is a price premium30associated with the marshlands and (2) developers have market incentives to31incorporate more open space into their designs of residential subdivisions.32Regarding marshlands, we also find that accessibility is an important variable that33adds much more value to a property than just the proximity.34Keywords. Coastal, Georgia, market incentive, spatial hedonic35JEL Classifications: R21, R58, Q51361. Introduction37In most jurisdictions of the United States, land use policy is made at the local38level within the context of existing federal and state regulations. County and39city government officials scrutinize developers’ requests for zoning variations40and building permits. Local officials make their decisions under two potentially41conflicting objectives. First, they are concerned with providing adequate42financing for their jurisdiction, which can be accomplished by increasing the43extent of developed land and improving their real estate tax base. Second, they44are charged with meeting environmental quality objectives, which sometimes45requires the preservation of land in an undeveloped state.46This publication was supported in part by an institutional grant (NA10OAR4170098) to the Georgia47Sea Grant College Program from the National Sea Grant Office, National Oceanic and Atmospheric48Administration, U.S. Department of Commerce.49∗Corresponding author’s e-mail: atreya@wharton.upenn.edu5038351https://doi.org/10.1017/aae.2016.25 Published online by Cambridge University Press525354===== PAGE 13 (score 86, numeric-paren 59, table-words 2, var-words 11) =====5556Valuing Open Space in a Marshland Environment 39557Table 3. SARAR Model Results using an Inverse Distance Matrix Truncated at 200 Meters58(dependent variable: lnPrice)59Variables60Model 161Model 262Model 363lnHousesize640.82∗∗∗650.75∗∗∗660.75∗∗∗67(0.01)68(0.02)69(0.02)70lnParcelsize710.13∗∗∗720.13∗∗∗730.10∗∗∗74(0.01)75(0.01)76(0.01)77Beds78−0.01790.001800.00281(0.008)82(0.009)83(0.008)84Age85−0.007∗∗∗86−0.007∗∗∗87−0.008∗∗∗88(0.0007)89(0.0007)90(0.0007)91Agesq927.5e-05∗∗∗937.3e-05∗∗∗947.5e-05∗∗∗95(6.1e-06)96(6.0e-06)97(5.9e-06)98Fireplace990.04∗∗∗1000.034∗∗∗1010.04∗∗∗102(0.01)103(0.01)104(0.01)105Brick106−0.0131070.011080.01109(0.01)110(0.01)111(0.01)112Garage1130.07∗∗∗1140.09∗∗∗1150.10∗∗∗116(0.01)117(0.01)118(0.01)119Deck1200.06∗∗∗1210.04∗∗∗1220.04∗∗∗123(0.01)124(0.01)125(0.01)126Pool1270.10∗∗∗1280.10∗∗∗1290.10∗∗∗130(0.02)131(0.02)132(0.02)133lnCommonspace1340.07∗∗∗1350.06∗∗∗1360.04∗∗∗137(0.005)138(0.005)139(0.006)140lnMajorroads1410.02∗∗∗1420.0081430.002144(0.005)145(0.005)146(0.005)147lnSchool1480.03∗∗∗1490.04∗∗∗1500.04∗∗∗151(0.008)152(0.009)153(0.008)154SFHA1550.03∗1560.009157−0.0004158(0.01)159(0.015)160(0.015)161Bltpostfir1620.06∗∗∗1630.04∗∗1640.06∗∗∗165(0.01)166(0.018)167(0.018)168lnNonwhite169−0.09∗∗∗170−0.0861∗∗∗171−0.086∗∗∗172(0.006)173(0.006)174(0.006)175lnIncome1760.26∗∗∗1770.25∗∗∗1780.27∗∗∗179(0.02)180(0.019)181(0.0189)182lnPark183−0.09∗∗∗184−0.08∗∗∗185(0.008)186(0.008)187lnMarsh188−0.07∗∗∗189−0.04∗∗∗190(0.005)191(0.006)192Waterview1930.10∗∗∗1940.17∗∗∗195(0.02)196(0.02)197Wateraccess1980.38∗∗∗199(0.06)200Dock2010.51∗∗∗202(0.03)203Year20052040.22∗∗∗2050.21∗∗∗2060.21∗∗∗207(0.016)208(0.02)209(0.01)210https://doi.org/10.1017/aae.2016.25 Published online by Cambridge University Press211212213===== PAGE 14 (score 46, numeric-paren 24, table-words 5, var-words 7) =====214215396216AJITA ATREYA ET AL.217Table 3. Continued218Variables219Model 1220Model 2221Model 3222Year20062230.41∗∗∗2240.40∗∗∗2250.40∗∗∗226(0.017)227(0.02)228(0.07)229Year20072300.51∗∗∗2310.50∗∗∗2320.50∗∗∗233(0.018)234(0.018)235(0.02)236Year20082370.37∗∗∗2380.36∗∗∗2390.37∗∗∗240(0.02)241(0.019)242(0.02)243Year20092440.21∗∗∗2450.20∗∗∗2460.21∗∗∗247(0.02)248(0.02)249(0.020)250Year20102510.05∗∗2520.04∗∗2530.05∗∗254(0.02)255(0.02)256(0.020)257Constant2583.67∗∗∗2595.08∗∗∗2605.07∗∗∗261(0.21)262(0.22)263(0.22)264Lambda2650.03∗∗∗2660.02∗∗∗2670.026∗∗∗268(0.003)269(0.003)270(0.003)271Rho2723.65∗∗∗2733.56∗∗∗2743.5∗∗∗275(0.10)276(0.07)277(0.0814)278Notes: N = 11,011. Standard errors in parentheses. ∗∗∗P < 0.01, ∗∗P < 0.05, ∗P < 0.1. SARAR, spatial279autoregressive model with autoregressive residuals.280(i.e., all continuous variables were transformed to their natural logarithms).5281Throughout, heteroskedasticity consistent standard errors are reported. The282estimate for λ, the spatial autoregressive coefficient, is significant and in the283expected direction indicating a positive adjacency effect; that is, a higher sale284price of adjacent properties should result in a higher average sale price, ceteris285paribus. Regarding the interpretation of the regression coefficients in the SARAR286model, a marginal effect is calculated by multiplying an estimate into a spatial287multiplier, 1/(1 −λ), as suggested by Kim, Phipps, and Anselin (2003).288All coefficients for the structural variables have their expected sign and are289statistically significant except Beds and Brick.6 The quadratic specification for the290variable Age seems to capture the diminishing marginal effects as shown by the291positive and significant Agesq variable. Housesize and Parcelsize have significant292impacts on property prices. A 1% increase in Housesize and Parcelsize increases293property prices by 0.75% to 0.82% and 0.10% to 0.13%, respectively, which is294equivalent to approximately $1,389 to $1,506 and $22 to $27 when evaluated295for an average priced property across the three models (Table 4).7 In Table 4,296model 3 results indicate that a property with a fireplace (Fireplace), garage297(Garage), deck (Deck), or pool (Pool), however, commands even higher price2985 We performed the Box-Cox transformation of the dependent variable and concluded that the299natural log of price as the dependent variable was the best specification for our model. Others have used300semiparametric models, but those models are difficult to interpret.3016 The interpretation hereafter will be in regard to model 3 unless specified otherwise.3027 Note that the spatial multiplier (1/1 −λ) is taken into account when calculating marginal effects.303https://doi.org/10.1017/aae.2016.25 Published online by Cambridge University Press304305306===== PAGE 15 (score 16, numeric-paren 0, table-words 1, var-words 14) =====307308Valuing Open Space in a Marshland Environment 397309Table 4. Marginal Effects Evaluated for an Average Property in Chatham County310Variables311Model 1312Model 2313Model 3314Housesize315$1,506.1316$1,375.4317$1,389.1318Parcelsize319$26.7320$27.3321$21.8322Beds323−$1,292.8324$114.5325$212.1326Age327−$67.2328−$68.3329−$75.5330Agesq331$0.7332$0.7333$0.7334Fireplace335$12,055.6336$10,218.9337$11,708.3338Brick339−$3,819.6340$2,779.2341$3,106.3342Garage343$22,111.8344$27,319.3345$28,404.5346Deck347$17,009.1348$12,168.2349$12,992.6350Pool351$29,094.5352$29,150.5353$27,747.4354Commonspace355$4,476.1356$3,726.1357$3,075.0358Majorroads359$14.8360$5.8361$2.0362School363$3.4364$4.7365$5.5366SFHA367$8,206.1368$2,678.8369−$115.0370Bltpostfir371$16,501.8372$12,050.0373$16,606.7374Nonwhite375−$821.5376−$777.9377−$783.0378Income379$1.3380$1.2381$1.2382Park383−$7.6384−$7.9385Marsh386−$53.0387−$31.1388Waterview389$29,534.4390$49,282.3391Wateraccess392$112,901.3393Dock394$154,716.7395Note: Marginal effect calculation takes into account the spatial multiplier.396premiums equal to approximately $11,708, $28,404, $12,992, and $27,747,397respectively, when evaluated for an average priced home.398All coefficients for neighborhood and location variables are of the expected399sign and are statistically significant except the proximity to roads (lnMajorroads)400and location in SFHA. In Chatham County, the coefficient of lnCommonspace401indicates that a 1% increase in the percentage of common area in the402neighborhood increases the property prices on average by approximately $3,075403when evaluated for an average price home. This result indicates that developers404have an incentive to keep more common space in their design feature for greater405revenue. The proximity to school significantly decreases the price of the property,406which may be attributed to the level of noise from a school. An increase in the407proportion of nonwhites in a community is negatively related to the property408prices, whereas an increase in income is positively related to the property prices409in Chatham County.410Regarding the environmental amenity variables, all of those included in the411model have their expected positive sign and are statistically significant. The412variables lnPark, lnMarsh, and Waterview all seem to be important factors413adding value to a property. The finding that both lnCommonspace and lnMarsh414are significant, positive contributors to property prices is surprising. The initial415https://doi.org/10.1017/aae.2016.25 Published online by Cambridge University Press416