1# RECORD 38d51e7656f94829f9f43fba3ef936f46b613e292# TITLE: A Hedonic Analysis of the Impact of Marine Aquaculture on Coastal Housing Prices in Maine3# YEAR: 2017.0 VENUE: Agricultural and Resource Economics Review DOI: 10.1017/age.2017.194# AUTHORS: Keith S. Evans; Xuan Chen; Christina A. Robichaud5# PAGES: 26 CANDIDATE TABLE PAGES (1-based): [16, 17, 20]6# ABSTRACT: Converting coastal waters to farmed production of seafood may generate conflicts with other resource users. This study explores the impact of marine aquaculture development on coastal homeowners. Using single-family home sales from 2012–2014 and spatial data on coastal aquaculture activity, we employ hedonics to assess the impacts of mariculture development in three study areas of Maine, USA. Our results suggest modest impacts on residential property values with significant spatial variation across study areas. This spatial variation represents a challenge for managers and highlights the potential benefits from coordinating the development of aquaculture to balance resource users' objectives with industry growth.789===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) =====1011A Hedonic Analysis of the Impact of12Marine Aquaculture on Coastal13Housing Prices in Maine14Keith S. Evans, Xuan Chen, and Christina A. Robichaud15Converting coastal waters to farmed production of seafood may generate conflicts16with other resource users. This study explores the impact of marine aquaculture17development on coastal homeowners. Using single-family home sales from 2012–182014 and spatial data on coastal aquaculture activity, we employ hedonics to19assess the impacts of mariculture development in three study areas of Maine,20USA. Our results suggest modest impacts on residential property values with21significant spatial variation across study areas. This spatial variation represents a22challenge for managers and highlights the potential benefits from coordinating23the development of aquaculture to balance resource users’ objectives with24industry growth.25Key26Words:27coastal28waters,29hedonic30pricing31model,32marine33aquaculture,34mariculture, property values35Aquaculture is an important source of fish protein. While wild-capture36production has flat-lined since the mid-1980s, due to excessive fishing37pressure and changing ocean conditions, world production from aquaculture38has grown exponentially to meet market demand (World Bank 2013, Food39and Agriculture Organization of the United Nations (FAO) 2016). Although40China has represented the majority of this growth, generating more than 6041percent of production by volume (FAO 2016), early research suggests that a42wide-range of marine production opportunities exist for the United States43(Knapp 2008, Valderrama and Anderson 2008, Kite-Powell, Rubino, and44Keith Evans is an Assistant Professor in the School of Economics and School of Marine Sciences,45University of Maine, Orono, ME 04469. Xuan Chen is an Assistant Professor in the School of46Economics, University of Maine, Orono, ME 04469. Christina Robichaud is a graduate student in47the School of Economics, University of Maine, Orono, ME 04469. Correspondence: Keith S. Evans48▪School of Economics ▪5782 Winslow Hall, Room 206 ▪Orono, ME 04469 ▪Phone 207.581.3178 ▪49email: keith.evans@maine.edu.50The authors thank Maine Multiple Listings Service for providing access to the sales transactions51data. We would also like to thank the participants at the 2016 NAREA workshop and two52anonymous reviewers for helpful suggestions. This project was supported by the National53Science Foundation under EPSCoR award #IIA-1355457 and the USDA National Institute of54Food and Agriculture under Hatch projects #ME021603 and #ME021704.55The views expressed are the authors’ and do not necessarily represent the policies or views of56any sponsoring agencies.57Agricultural and Resource Economics Review 46/2 (August 2017) 242–26758© The Author(s) 2017. This is an Open Access article, distributed under the terms of the Creative59Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits60unrestricted re-use, distribution, and reproduction in any medium, provided the original work is61properly cited.62https://doi.org/10.1017/age.2017.19 Published online by Cambridge University Press636465===== PAGE 16 (score 150, numeric-paren 124, table-words 1, var-words 11) =====6667Table 3. Parameter Estimates from the Box-Cox Hedonic Pricing Model. Sales prices are transformed using the Box-68Cox transformation parameter λ. Robust standard errors are reported in parentheses. Parameter estimates for69municipality and year fixed effects are available upon request.70Control variables71Casco Bay72Damariscotta73Penobscot Bay74Bathrooms750.324***760.323***770.583***780.584***791.080***801.110***81(0.036)82(0.036)83(0.131)84(0.132)85(0.167)86(0.171)87Living Space (1,000s square feet)881.092***891.091***901.247***911.249***921.338***931.371***94(0.033)95(0.033)96(0.139)97(0.140)98(0.221)99(0.226)100Lot size (100s acres)1013.137***1023.133***1035.604***1045.609***10511.074***10611.376***107(0.580)108(0.579)109(0.686)110(0.687)111(1.563)112(1.602)113Age (50 years)1140.218***1150.218***1160.270***1170.269***1180.589***1190.601***120(0.023)121(0.023)122(0.051)123(0.051)124(0.076)125(0.078)126Age squared1270.005***1280.005***1290.004***1300.004***1310.012***1320.012***133(0.001)134(0.001)135(0.001)136(0.001)137(0.002)138(0.002)139Cabin (0/1)1400.3171410.3111420.717*1430.714*1441.685**1451.744**146(0.250)147(0.249)148(0.368)149(0.369)150(0.783)151(0.810)152Distance to water (miles)1530.171***1540.171***1550.2371560.2441570.3961580.301159(0.041)160(0.041)161(0.172)162(0.173)163(0.252)164(0.259)165Distance sq to water (miles)1660.018***1670.018***1680.067**1690.068**1700.112***1710.101**172(0.006)173(0.006)174(0.031)175(0.031)176(0.043)177(0.044)178View of water (% 2–mile buffer)1792.282***1802.289***1813.396***1823.397***1835.458***1846.140***185(0.141)186(0.142)187(0.473)188(0.474)189(0.751)190(0.789)191Waterfront (0/1)1921.333***1931.333***1941.545***1951.549***1962.848***1972.941***198(0.101)199(0.101)200(0.172)201(0.172)202(0.297)203(0.304)204Govt. Beach/Dock (0/1)2050.221*2060.219*2070.1182080.1152090.865***2100.886***211(0.121)212(0.121)213(0.269)214(0.270)215(0.309)216(0.316)217Continued218Evans et al.219A Hedonic Analysis of the Impact of Marine Aquaculture 257220https://doi.org/10.1017/age.2017.19 Published online by Cambridge University Press221222223===== PAGE 17 (score 111, numeric-paren 84, table-words 3, var-words 9) =====224225Table 3. Continued226Control variables227Casco Bay228Damariscotta229Penobscot Bay230Elevation (100s feet)2310.101***2320.101***2330.328***2340.329***2350.0292360.038237(0.027)238(0.027)239(0.122)240(0.122)241(0.081)242(0.083)243Seasonal homes (% census tract)2440.007**2450.007**2460.0242470.0252480.0682490.087250(0.003)251(0.003)252(0.057)253(0.057)254(0.063)255(0.067)256Median income† ($10,000s)2570.0042580.0052591.245***2601.241***2610.613*2620.639*263(0.015)264(0.015)265(0.450)266(0.451)267(0.343)268(0.352)269Spending per student ($1,000s)2700.0482710.0492720.0432730.0422740.141*2750.136*276(0.032)277(0.032)278(0.049)279(0.049)280(0.073)281(0.074)282Sale in winter (0/1)2830.144***2840.144***2850.0652860.0672870.0542880.060289(0.041)290(0.041)291(0.149)292(0.149)293(0.195)294(0.201)295Constant29619.804***29719.778***29829.587***29929.613***30025.349***30125.787***302(0.203)303(0.203)304(2.639)305(2.643)306(1.852)307(1.916)308Aquaculture Variables and Transformation Parameter309Neighborhood attitudes3100.0053110.0123120.7783130.7703140.0003150.061316(0.058)317(0.055)318(0.512)319(0.512)320(0.335)321(0.336)322Aquaculture index (base)3230.8853240.694***32523.392**326(2.922)327(0.215)328(10.882)329Aquaculture index (alternate)3307.8943314.342***33257.609**333(9.837)334(1.367)335(24.721)336Transformation parameter (λ)3370.100***3380.099***3390.110**3400.111**3410.141***3420.143***343(0.010)344(0.010)345(0.022)346(0.022)347(0.017)348(0.017)349Number of observations350569835156983521238353123835416443551644356Adjusted R23570.7333580.7333590.5983600.5983610.5903620.590363F–statistics (Wald)364307.754365307.96836639.25536739.20036835.26036935.568370† Median household income ($10,000s) in the home’s census tract.371Significance levels: *** p < 0.01, ** p < 0.05, and * p < 0.10372Agricultural and Resource Economics Review373258 August 2017374https://doi.org/10.1017/age.2017.19 Published online by Cambridge University Press375376377===== PAGE 18 (score 13, numeric-paren 0, table-words 1, var-words 11) =====378379We find more interesting patterns related to the Aquaculture index, which380captures the spatial arrangement of leases in relation to a home. Our results381suggest variation in the impact on housing prices across the three regions.382We fail to find evidence of any impact in Casco Bay, while we find statistically383significant evidence for the other study areas. This pattern is robust to our384specification of the Aquaculture index (base and alternate form). It is difficult385to directly compare the magnitude of coefficients across models, as each is386transformed387using388a389different390value391for392the393Box-Cox394transformation395parameter.396Instead,397the398following399focuses400on401differences402in403sign404and405significance across regions.406For407example,408Aquaculture409index410is411significant412and413positive414for415the416Damariscotta River region. That is, after controlling for structural and417neighborhood characteristics, and attitudes surrounding aquaculture, houses418with “more” coastal aquaculture command a higher price on average,419suggesting that aquaculture may be viewed as an amenity in the region. In420Penobscot Bay, we find the opposite result, with coastal aquaculture lowering421sales prices – coastal aquaculture may be viewed as a disamenity in this422housing market. This pattern is consistent with the information on concerns423raised at public lease hearings (Table 2). Note that, on average, there are424more than twice as many concerns raised about the development of425mariculture and how it affects riparian homeowners and coastal users in426Penobscot Bay.427Given the evidence that aquaculture capitalizes into residential property428values in two study areas, we explore the relative magnitude of these429marginal impacts – are they large or small in these study areas? That is,430while statistically significant, are these results economically significant? A431comparison of the marginal effects for the components of the Aquaculture432index will provide insight into this,and answer our second research question.433Based on the design of this index, there are three margins of interest: the434density effect, the acreage effect, and the proximity effect (equation 3). The435density effect measures the marginal impact of an additional lease sited near436a home, holding the number of acres of aquaculture production and437proximity constant.8 The acreage effect and proximity effect have similar438interpretations. Given the Box-Cox transformation and the form of the439Aquaculture index, these margins are nonlinear and depend on a combination440of441parameter442estimates,443the444Box-Cox445transformation446parameter,447and,448importantly, the sales price of a home; this generates heterogeneity in the449marginal impacts across the sample. We calculate margins at the house-level450using451sample452values.453Further,454we455restrict456attention457to458houses459with460aquaculture sited within a 2-mile buffer zone of the home. Given skewness in461prices (evident from the estimates of the Box-Cox transformation parameter),4628 This is similar in theme to the expected contribution of an additional bedroom, ceteris paribus,463to the sales price of home, which holds square footage of the home constant.464Evans et al.465A Hedonic Analysis of the Impact of Marine Aquaculture 259466https://doi.org/10.1017/age.2017.19 Published online by Cambridge University Press467468469===== PAGE 20 (score 28, numeric-paren 12, table-words 3, var-words 8) =====470471Table 4. Median Marginal Willingness-to-Pay (MWTP) ($) for coastal aquaculture by study area and model. Estimates472calculated from the subset of observations that contained aquaculture within a 2-mile buffer zone. Density depicts the473margin for an additional lease holding the number of acres and distance of leases to houses constant – increasing the474density of leases contained in the existing acreage. Acreage depicts the margin for an additional acre of aquaculture,475holding the number of leases and distance from housing constant. Proximity depicts the margin for moving 1 meter closer476to aquaculture holding the number of leases and acres constant. One-tailed p-values simulated using 100,000 draws from477the parameters estimates (Table 3).478Study area479Model480Density481Acreage482Proximity483Casco Bay484Base48539.6448654.714870.01488Alternate489122.62490228.044910.05492Damariscottta493Base4941.69***49593.77***4960.01***497Alternate4983.59***499156.05***5000.01***501Penobscot Bay502Base5031,588.79**504704.57**5050.78***506Alternate5071,006.27***508637.75***5090.44***510Significance levels: *** p < 0.01, ** p < 0.05, and * p < 0.10511Evans et al.512A Hedonic Analysis of the Impact of Marine Aquaculture 261513https://doi.org/10.1017/age.2017.19 Published online by Cambridge University Press514515516===== PAGE 21 (score 13, numeric-paren 0, table-words 0, var-words 13) =====517518government agencies and other invested stakeholders (e.g., NGOs) (Knapp5192012, Knapp and Rubino 2016). Researchers argue that we could reduce520these tensions through improved availability of interdisciplinary research and521communication of the positive benefits of aquaculture to these groups of522users (Bricknell and Langston 2013, NOAA 2016b). These challenges523highlight the importance of acceptance by coastal resource users for the524successful expansion of mariculture. Resource managers and regulators with525a deeper understanding of the impacts of marine development (e.g.,526mariculture) could design spatial plans that balance concerns across a suite527of users and promote this emerging industry. To this end, quantifying the528impacts of mariculture development on coastal residential property values529provides resource managers with valuable information for integrative marine530spatial planning.531This paper presents an important addition to the literature and provides one532of the first empirical analyses of the effect of marine aquaculture on coastal,533residential property values. This revealed-preference study complements the534stated-preference work on public perceptions toward aquaculture (Shafer,535Inglis, and Martin 2010, D’Anna and Murray 2015, Jodice et al. 2015). Further,536the design of our hedonic model allows us to address questions of direct537interest to policy makers and land managers regarding the development of538mariculture. Specifically, we measure not just the impact from proximity to539aquaculture, but also from the spatial configuration of its siting, e.g., the540density and acreage of leases across the local seascape. The results from this541work provide insights into the impact of decentralized expansion of marine542aquaculture. Finally, our results can also inform future exploratory analyses543of transcripts from aquaculture lease hearings. For example, an exploration of544the patterns of concerns raised at these hearings, and how they vary by lease,545lessee, and community characteristics could546be important for coastal547managers. This type of analysis could provide information about the efficacy548of public participation in the siting process, as well as provide insight into549designing a siting framework that improves social acceptance of mariculture.550While there is a rich body of work that has evaluated the impact of land-based551farming (Abeles-Allison and Connor 1990, Palmquist, Roka, and Vukina 1997,552Le Goffe 2000, Herriges, Secchi, and Babcock 2005, Ready and Abdalla 2005,553Kim and Goldsmith 2009), few studies have attempted to investigate the554impact of water-based farming on residential property values (Northern555Economics 2010, Jodice et al. 2015, Sudhakaran 2015). Our work fills this556hole in the research. Our results suggest wide variation in how marine557development of aquaculture impacts property values, both across and within558study areas. In Casco Bay, we fail to find statistical evidence of impacts from559marine aquaculture. Recall that this is an urban area for Maine, with an560active working waterfront for shipping, recreation, and commercial fishing. It561is possible that mariculture is insubstantial relative to these other coastal562uses. In the Damariscotta River region, we find statistically significant and563positive effects of the development of marine aquaculture. However, these564Agricultural and Resource Economics Review565262 August 2017566https://doi.org/10.1017/age.2017.19 Published online by Cambridge University Press567