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Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Author

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  • Yanchuan Mou

    (College of Architecture and Environment, Sichuan University, No. 29 Jiuyanqiao Wangjiang Road, Chengdu 610064, China
    Department of City and Regional Planning, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Qingsong He

    (School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Department of City and Regional Planning, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Bo Zhou

    (College of Architecture and Environment, Sichuan University, No. 29 Jiuyanqiao Wangjiang Road, Chengdu 610064, China)

Abstract

Given the rapidly developing processes in the housing market of China, the significant regional difference in housing prices has become a serious issue that requires a further understanding of the underlying mechanisms. Most of the extant regression models are standard global modeling techniques that do not take spatial non-stationarity into consideration, thereby making them unable to reflect the spatial nature of the data and introducing significant bias into the prediction results. In this study, the geographically weighted regression model (GWR) was applied to examine the local association between housing price and its potential determinants, which were selected in view of the housing supply and demand in 338 cities across mainland China. Non-stationary relationships were obtained, and such observation could be summarized as follows: (1) the associations between land price and housing price are all significant and positive yet having different magnitudes; (2) the relationship between supplied amount of residential land and housing price is not statistically significant for 272 of the 338 cities, thereby indicating that the adjustment of supplied land has a slight effect on housing price for most cities; and (3) the significance, direction, and magnitude of the relationships between the other three factors (i.e., urbanization rate, average wage of urban employees, proportion of renters) and housing price vary across the 338 cities. Based on these findings, this paper discusses some key issues relating to the spatial variations, combined with local economic conditions and suggests housing regulation policies that could facilitate the sustainable development of the Chinese housing market.

Suggested Citation

  • Yanchuan Mou & Qingsong He & Bo Zhou, 2017. "Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1826-:d:114513
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    3. Yang Wang & Kangmin Wu & Jing Qin & Changjian Wang & Hong’ou Zhang, 2020. "Examining Spatial Heterogeneity Effects of Landscape and Environment on the Residential Location Choice of the Highly Educated Population in Guangzhou, China," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    4. Zhiheng Yang & Chenxi Li & Yongheng Fang, 2020. "Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China," Land, MDPI, vol. 9(1), pages 1-21, January.
    5. Wang, Haining & Cheng, Zhiming & Smyth, Russell & Sun, Gong & Li, Jie & Wang, Wangshuai, 2022. "University education, homeownership and housing wealth," China Economic Review, Elsevier, vol. 71(C).
    6. Ya Gao & Xiuting Li & Jichang Dong, 2019. "Does Housing Policy Sustainability Matter? Evidence from China," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    7. Xiaoping Zhou & Zhenyang Qin & Yingjie Zhang & Linyi Zhao & Yan Song, 2019. "Quantitative Estimation and Spatiotemporal Characteristic Analysis of Price Deviation in China's Housing Market," Sustainability, MDPI, vol. 11(24), pages 1-28, December.
    8. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).

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