Spatial Autocorrelations and Urban Housing Market Segmentation

Posted: 9 Nov 2006

See all articles by Yong Tu

Yong Tu

National University of Singapore (NUS) - Department of Real Estate

Hua Sun

Iowa State University

Shi-Ming Yu

National University of Singapore (NUS)

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 autocorrelation.

Keywords: housing submarket, isotropic semi-variogram, price spatial autocorrelation

Suggested Citation

Tu, Yong and Sun, Hua and Yu, Shi-Ming, Spatial Autocorrelations and Urban Housing Market Segmentation. Journal of Real Estate Finance and Economics, Vol. 34, No. 3, 2007, Available at SSRN: https://ssrn.com/abstract=943077

Yong Tu (Contact Author)

National University of Singapore (NUS) - Department of Real Estate ( email )

United States

Hua Sun

Iowa State University ( email )

Ames, IA 50011-2063
United States
1-5152947514 (Phone)

Shi-Ming Yu

National University of Singapore (NUS) ( email )

1E Kent Ridge Road
NUHS Tower Block Level 7
Singapore, 119228
Singapore

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
1,037
PlumX Metrics