Please use this identifier to cite or link to this item:
|Title:||Spatial autocorrelations and urban housing market segmentation|
|Authors:||Tu, Y. |
Price spatial autocorrelation
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 16, 2018
WEB OF SCIENCETM
checked on Jul 23, 2018
checked on Jul 13, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.