Geographically weighted regression models in estimating median home prices in towns of Massachusetts based on an urban sustainability framework
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Citation (published version)Yaxiong Ma, Sucharita Gopal. 2018. "Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework." SUSTAINABILITY, Volume 10, Issue 4, 27 pp. https://doi.org/10.3390/su10041026
Housing is a key component of urban sustainability. The objective of this study was to assess the significance of key spatial determinants of median home price in towns in Massachusetts that impact sustainable growth. Our analysis investigates the presence or absence of spatial non-stationarity in the relationship between sustainable growth, measured in terms of the relationship between home values and various parameters including the amount of unprotected forest land, residential land, unemployment, education, vehicle ownership, accessibility to commuter rail stations, school district performance, and senior population. We use the standard geographically weighted regression (GWR) and Mixed GWR models to analyze the effects of spatial non-stationarity. Mixed GWR performed better than GWR in terms of Akaike Information Criterion (AIC) values. Our findings highlight the nature and spatial extent of the non-stationary vs. stationary qualities of key environmental and social determinants of median home price. Understanding the key determinants of housing values, such as valuation of green spaces, public school performance metrics, and proximity to public transport, enable towns to use different strategies of sustainable urban planning, while understanding urban housing determinants—such as unemployment and senior population—can help modify urban sustainable housing policies.
Rights"This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited."