Premium
Are Homes Near Water Bodies and Wetlands Worth More or Less? An Analysis of Housing Prices in One C onnecticut Town
Author(s) -
Cohen Jeffrey P.,
Cromley Robert G.,
Banach Kevin T.
Publication year - 2015
Publication title -
growth and change
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.657
H-Index - 55
eISSN - 1468-2257
pISSN - 0017-4815
DOI - 10.1111/grow.12073
Subject(s) - amenity , ordinary least squares , wetland , recreation , spatial dependence , econometrics , spatial analysis , spatial econometrics , environmental science , economics , geography , statistics , mathematics , ecology , finance , biology
Proximity to wetlands and water bodies can be considered an amenity (for open space and recreation value), as well as a possible nuisance (with the potential for flooding or development restrictions), although the overall effect may be different depending on location. Studies of the impacts of wetlands and water on housing prices can also be prone to spatial autocorrelation problems arising from omitted unobservables. M c M illen and R edfearn explain that locally weighted regressions ( LWR s) can address spatial autocorrelation. In addition to ordinary least squares ( OLS ), we use LWR to control for spatial effects and analyze how proximity to water bodies and wetland areas impact real sales prices of homes in one C onnecticut town in 2000–2009. With OLS regressions, proximity to wetland areas and water bodies are insignificant determinants of the real sale price of homes. When we control for spatial effects with a nonparametric (i.e., LWR ) approach, the significance of the water variable is different than from OLS —while greater distance from wetlands leads to an insignificant relationship with housing price, the water distance effect becomes negative and significant. These results imply that incorporating potential spatial heterogeneity in the data is crucial for accurately estimating the direction, magnitudes, and statistical significance of the relationships between environmental variables and housing prices.