Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11146-007-9015-0
Title: Spatial autocorrelations and urban housing market segmentation
Authors: Tu, Y. 
Sun, H.
Yu, S.-M. 
Keywords: Housing submarket
Isotropic semi-variogram
Price spatial autocorrelation
Issue Date: 2007
Source: Tu, Y., Sun, H., Yu, S.-M. (2007). Spatial autocorrelations and urban housing market segmentation. Journal of Real Estate Finance and Economics 34 (3) : 385-406. ScholarBank@NUS Repository. https://doi.org/10.1007/s11146-007-9015-0
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. © Springer Science+Business Media, LLC 2007.
Source Title: Journal of Real Estate Finance and Economics
URI: http://scholarbank.nus.edu.sg/handle/10635/46293
ISSN: 08955638
DOI: 10.1007/s11146-007-9015-0
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