A geographically weighted regression approach to investigating local built-environment effects on home prices in the hou
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A geographically weighted regression approach to investigating local built‑environment effects on home prices in the housing downturn, recovery, and subsequent increases Chih‑Hao Wang1 · Na Chen2 Received: 26 July 2019 / Accepted: 2 April 2020 © Springer Nature B.V. 2020
Abstract The 2007 financial crisis profoundly affected most American metropolitan areas. Over the past 10 years, Columbus, Ohio, has experienced a housing downturn, recovery, and subsequent increases. This allows to investigate the response of housing market in different periods of the recession. Ordinary and geographically-weighted regression (GWR) models were developed to examine global and local built-environment effects on home-price appreciations for the three periods while controlling for other physical and socioeconomic variables. The results found that home buyers showed an unchanged preference for residential privacy and amenity and avoided those features that might attract negative external effects from a period to another. The home-price appreciation rates showed different spatial patterns across the study region in the three periods. Nevertheless, the results suggested that suburban areas, particularly those in northern Columbus, better resisted, recovered from, and adapted to the recession. In the wake of the recession, a smaller house was preferred by home buyers. GWR models also provided some interesting findings. In the downturn, accessibility to a park or library helped sustain home prices in the northwest. Bus stop density had a positive effect in eastern Columbus in the recovery, most likely due to the high fuel price at that time. Neighborhoods with a higher income better retained their home value in the downturn, especially those in southern Columbus. Finally, this study found that the recession hit harder on minority neighborhoods in all three periods. This finding suggests that housing policies should focus on these neighborhoods with other social support. Keywords Housing recession · Hedonic pricing theory · Built environment · GWR · Spatial statistics
* Chih‑Hao Wang [email protected] Na Chen [email protected] 1
Department of Geography and City and Regional Planning, California State University, Fresno, 2555 E San Ramon M/S SB69, Fresno, CA 93740, USA
2
College of Design, Architecture, Art, and Planning, University of Cincinnati, Cincinnati, OH 45221, USA
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C.-H. Wang, N. Chen
1 Introduction The built environment of a city has been long recognized as an important factor in home values. This idea is based on the hedonic pricing theory that a home price can be considered as a price combination of a set of internal house attributes and surrounding environmental features, such as land uses, transportation infrastructure, and public facilities and services. The effects of built-environment features on home values have been intensively examined in the literature. However, few studies focused on comparing such effects in a housing downturn with those in a recovery and the time after. Since the fi
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