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Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC

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  • Joseph Hiebert

    (David E. Shi Institute for Sustainable Communities, Furman University, Greenville, SC 29613, USA)

  • Karen Allen

    (Department of Earth and Environmental Sciences, Furman University, Greenville, SC 29613, USA)

Abstract

As global consumption and development rates continue to grow, there will be persistent stress placed on public goods, namely environmental amenities. Urban sprawl and development places pressure on forested areas, as they are often displaced or degraded in the name of economic development. This is problematic because environmental amenities are valued by the public, but traditional market analysis typically obscures the value of these goods and services that are not explicitly traded in a market setting. This research examines the non-market value of environmental amenities in Greenville County, SC, by utilizing a hedonic price model of home sale data in 2011. We overlaid home sale data with 2011 National Land Cover Data to estimate the value of a forest view, proximity to a forest, and proximity to agriculture on the value of homes. We then ran two regression models, an ordinary least squares (OLS) and a geographically weighted regression to compare the impact of space on the hedonic model variables. Results show that citizens in Greenville County are willing to pay for environmental amenities, particularly views of a forest and proximity to forested and agricultural areas. However, the impact and directionality of these variables differ greatly across space. These findings suggest the need for an integration of spatial dynamics into environmental valuation estimates to inform conservation policy and intentional city planning.

Suggested Citation

  • Joseph Hiebert & Karen Allen, 2019. "Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC," Land, MDPI, vol. 8(10), pages 1-16, October.
    • Handle: RePEc:gam:jlands:v:8:y:2019:i:10:p:147-:d:275512
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    2. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).

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