Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method
AbstractHedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 1379.
Date of creation: 2006
Date of revision:
Find related papers by JEL classification:
- R0 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-01-14 (All new papers)
- NEP-GEO-2007-01-14 (Economic Geography)
- NEP-MIC-2007-01-14 (Microeconomics)
- NEP-URE-2007-01-14 (Urban & Real Estate Economics)
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