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Spatial Autocorrelations and Urban Housing Market Segmentation

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  • Yong Tu
  • Hua Sun
  • Shi-Ming Yu

Abstract

This paper seeks to let data define urban housing market segments, replacing the conventional administrative or any pre-defined boundaries used in the previous housing submarket literature. We model housing transaction data using a conventional hedonic function. The hedonic residuals are used to estimate an isotropic semi-variogram, from which residual variance–covariance matrix is constructed. The correlations between hedonic residuals are used as identifier to assign housing units into clusters. Standard submarket identification tests are applied to each cluster to examine the segmentation of housing market. The results are compared with the prevailing structure of market segments. Weighted mean square test shows that the defined submarket structure can improve the precision of price prediction by 17.5%. This paper is experimental in the sense that it represents one of the first attempts at investigating market segmentation through house price spatial autocorrelations. Copyright Springer Science+Business Media, LLC 2007

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  • Yong Tu & Hua Sun & Shi-Ming Yu, 2007. "Spatial Autocorrelations and Urban Housing Market Segmentation," The Journal of Real Estate Finance and Economics, Springer, vol. 34(3), pages 385-406, April.
    • Handle: RePEc:kap:jrefec:v:34:y:2007:i:3:p:385-406
    DOI: 10.1007/s11146-007-9015-0
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    15. Mahdieh Yazdani, 2021. "House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach," Papers 2108.02442, arXiv.org.
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