TY - JOUR
T1 - Modified data-driven framework for housing market segmentation
AU - Wu, Chao
AU - Ye, Xinyue
AU - Ren, Fu
AU - Du, Qingyun
N1 - Funding Information:
This study was supported by the National Natural Science Foundation of China (Project No. 41571438) and US National Science Foundation (Project No. 1739491).
Publisher Copyright:
© 2018 American Society of Civil Engineers.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Housing market segmentation is significant at both the conceptual and empirical levels because it reflects the spatial heterogeneity of housing prices, improves the predictive accuracy of housing prices, and indicates dynamic changes in housing markets. The existing literature offers a popular framework, called the data-driven method, to delineate submarkets based on principal component analysis (PCA) and cluster analysis; however, the traditional framework does not consider spatial heterogeneity and has difficulty balancing the spatial relationships (i.e., distance and topological relationships) and attribute similarities. To address these limitations, this paper proposes a modified data-driven framework for delineating housing submarkets by integrating geographically weighted principal component analysis (GWPCA), a spatial heterogeneity test, a density-based spatial clustering (DBSC) algorithm, and hedonic validation. The modified framework is applied to housing-market segmentation in Shenzhen, China. The results indicate that the modified framework exhibits the best performance in submarket segmentation in Shenzhen. The framework has important implications and high potential for identifying housing submarkets statistically, and it can be generalized and applied to housing markets in other cities. In addition, the visualisation results can be used by appraisers for property valuation and by city planners for facility management and social-equality improvement and balance.
AB - Housing market segmentation is significant at both the conceptual and empirical levels because it reflects the spatial heterogeneity of housing prices, improves the predictive accuracy of housing prices, and indicates dynamic changes in housing markets. The existing literature offers a popular framework, called the data-driven method, to delineate submarkets based on principal component analysis (PCA) and cluster analysis; however, the traditional framework does not consider spatial heterogeneity and has difficulty balancing the spatial relationships (i.e., distance and topological relationships) and attribute similarities. To address these limitations, this paper proposes a modified data-driven framework for delineating housing submarkets by integrating geographically weighted principal component analysis (GWPCA), a spatial heterogeneity test, a density-based spatial clustering (DBSC) algorithm, and hedonic validation. The modified framework is applied to housing-market segmentation in Shenzhen, China. The results indicate that the modified framework exhibits the best performance in submarket segmentation in Shenzhen. The framework has important implications and high potential for identifying housing submarkets statistically, and it can be generalized and applied to housing markets in other cities. In addition, the visualisation results can be used by appraisers for property valuation and by city planners for facility management and social-equality improvement and balance.
KW - Geographically weighted principal component analysis (GWPCA)
KW - Principal component analysis (PCA)
KW - Residential factor
KW - Spatial clustering
KW - Submarket
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U2 - 10.1061/(ASCE)UP.1943-5444.0000473
DO - 10.1061/(ASCE)UP.1943-5444.0000473
M3 - Article
AN - SCOPUS:85052697003
SN - 0733-9488
VL - 144
JO - Journal of the Urban Planning and Development Division, ASCE
JF - Journal of the Urban Planning and Development Division, ASCE
IS - 4
M1 - 04018036
ER -