Understanding spatial variations in the impact of accessibility on land value using geographically weighted regression
This paper aims to understand the spatial variability in house prices and accessibility. The motivation for understanding the connection between accessibility and house prices stems from the increasing attention given in recent years to the potential for funding transport infrastructure by land value capture policies. Establishing whether there is identifiable land value uplift, and further quantifying this uplift, is a prerequisite to sensible discussions on the potential for land value capture. Although there has been substantial related research in the United States, not only have there been fewer studies in the United Kingdom, but these have concentrated on London. London, as a capital city, differs in many respects from other cities. Large conurbations such as Manchester, Sheffield, and Tyne and Wear are more typical of British cities. This study focuses on the Tyne and Wear area, which has an extensive public transport system, with a light rail system—the Tyne and Wear Metro—forming the backbone of the public transport system. The investigation reported in this paper is underpinned by the use of Geographically Weighted Regression (GWR) methodology with property prices as the dependent variable, which in turn is explained by independent variables designed to standardize for household features and spatially defined factors including the transport accessibility of the house location. This methodology allows for estimation of the importance of transport accessibility in determining house prices. The empirical results show that, on average, the internal factors of the property and the socio-economic classification of its location are dominant determinants of property prices, but transport accessibility variables are also significant. However, the local model approach of GWR shows a significant spatially varying relationship between property prices and transport accessibility to be identified. This study contributes to a quantification of the impact of accessibility on house prices. Moreover, the paper demonstrates the application of a relatively new methodology in the transport field that takes account of the spatial nature of the data required in this process.
Volume (Year): 5 (2012)
Issue (Month): 2 ()
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- David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, 06.
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- Daniel P. McMillen & John McDonald, 2004. "Reaction of House Prices to a New Rapid Transit Line: Chicago's Midway Line, 1983-1999," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(3), pages 463-486, 09.
- Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical tests for spatial nonstationarity based on the geographically weighted regression model," Environment and Planning A, Pion Ltd, London, vol. 32(1), pages 9-32, January.
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