Taking the housing prices of 151 districts and counties in the Chengdu-Chongqing urban agglomeration from 2017 to 2019 as the basic data, the housing price data is geospatially expressed in ArcGIS, and the Theil index is calculated to analyze the differentiation pattern of the housing prices of the Chengdu-Chongqing urban agglomeration, using geo-detector. The effect of different factors on housing prices in different regions shows that the geographical distribution of housing prices in the Chengdu-Chongqing urban agglomeration is basically the same as the city scale and economic development of each city. The housing prices of the Chengdu-Chongqing urban agglomeration show a dual-core dominance and a collapse in the central region. The characteristics of industrial structure, housing supply, population scale, land cost, economic strength, real estate investment scale, housing policy, and higher education resources have a significant impact on the housing prices of Chengdu-Chongqing urban agglomeration. Different indicators have different effects on housing prices in different cities.
Cite this paper
Feng, R. and Han, R. (2021). Study on the Spatial Differentiation Pattern of Housing Prices in Chengdu-Chongqing City Cluster. Open Access Library Journal, 8, e7118. doi: http://dx.doi.org/10.4236/oalib.1107118.
Zhang, J.H. (2019) Research on the Effect of Real Estate Economy on the Growth of China’s National Economy. Fortune Today (China Intellectual Property), No. 12, 21-22.
Basu, S. and Thibodeau, T.G. (1998) Analysis of Spatial Autocorrelation in House Prices. Journal of Real Estate Finance & Economics, 17, 61-85.
https://doi.org/10.1023/A:1007703229507
Hanink, D.M., Cromley, R.G. and Ebenstein, A. (2012) Spatial Variation in the Determinants of House Prices and Apartment Rents in China. Journal of Real Estate Finance and Economics, 45, 347-363. https://doi.org/10.1007/s11146-010-9262-3
Holmes, M.J., Otero, J. and Panagiotidis, T. (2019) Property Heterogeneity and Convergence Club Formation among Local House Prices. Journal of Housing Economics, 43, 1-13. https://doi.org/10.1016/j.jhe.2018.09.002
Cui, N.N., Gu, H.Y. and Shen, T.Y. (2019) Spatial Differentiation and Correlation of Housing Prices and Rents in Beijing. Geographical Research, 38, 1420-1434.
Hu, X.T., Pu, L.J. and Pan, J. (2006) Study on the Spatiotemporal Impact of Urban Land Market on Housing Prices: Based on the Comparative Analysis of Nanjing and Guangzhou. Journal of Jiangxi Agricultural University, 28, 793-796.
Song, W.X. and Liu, C.H. (2018) Research on the Price Differentiation Mechanism of Urban Commercial Housing in the Integrated Area of the Yangtze River Delta. Geographical Research, 37, 92-102.
Wang, S.J., Wang, Y., Lin, X.Q., et al. (2016) The Characteristics and Influencing Mechanism of Spatial Differences in Housing Prices in Counties in China. Acta Geographica Sinica, No. 8, 1329-1342.
Han, Y.H., Yin, S.G. and Li, Z.J. (2018) Analysis of the Spatial Differentiation Pattern of Housing Prices in Counties in the Yangtze River Delta and Its Influencing Factors. Human Geography, 33, 93-101.
Mao, Z.G. and Wu, Y.M. (2020) Analysis of the Spatial Structure Evolution of the Consumption Level of Urban Agglomerations in Western China: Taking Chengyu Urban Agglomeration as an Example. Journal of Xiangtan University, 2, 50-55.
Wang, J.F., Li, X.H., Christakos, G., et al. (2010) Geographical Detectors-Based Health Risk Assessment and Its Application in the Neural Tube Defects Study of the Heshun Region, China. International Journal of Geographical Information Science, 24, 107-127. https://doi.org/10.1080/13658810802443457
