PROGRESS IN GEOGRAPHY ›› 2021, Vol. 40 ›› Issue (2): 283-292.doi: 10.18306/dlkxjz.2021.02.009

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Influencing factors of housing price differentiation based on the spatial quantile model: A case study of Wuhan City

LU Xinhai1, CAI Dawei1,*(), ZENG Chen2,3   

  1. 1. School of Public Administration, Central China Normal University, Wuhan 430079, China
    2. College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China
    3. Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2020-04-10 Revised:2020-07-13 Online:2021-02-28 Published:2021-04-28
  • Contact: CAI Dawei
  • Supported by:
    National Natural Science Foundation of China, No(71673096);National Natural Science Foundation of China, No(41771563);National Natural Science Foundation of China, No(72042020);China Postdoctoral Science Foundation, No(2019T12013)


The change of housing price is an important issue related to urbanization and high-quality social and economic development in China. In order to explore the influencing factors in terms of the quantiles of housing price, this study took Wuhan City as a case, which is the central city of central China, and used the spatial quantile regression (SQR) model for quantitative analysis. The two-stage least squares (2SLS) model result was compared with the SQR model output to reveal its superiority. The research shows that: 1) The SQR model not only can consider the spatial autocorrelation of the housing price, but also have the capability to embed the conditional distribution characteristics, which helps to better describe the driving effects of micro-factors on different housing prices. 2) In view of the quantiles, the spatial autocorrelation of the high prices is stronger than the low prices. The influencing factors show volatility and heterogeneity. Compared with the mean result of the 2SLS model, the influencing degree of each factor in the SQR model increases or decreases with the change of the quantile. There is a significant difference in the degree of influence on housing price of different levels. 3) Overall, age of the residential building and medical facilities are negative influencing factors, and building characteristics including floor area ratio, location, and neighborhood such as nearby educational facilities are positive influencing factors. Based on the results, reasonable increase of the floor area ratio and green space ratio of low- and middle-priced residential areas, and increased investment in rail transportation and educational facilities in low-priced residential areas can be taken as alternatives for the government to formulate differentiated policy measures for housing with different levels of prices.

Key words: housing price, differentiation, spatial quantile regression model, Wuhan City