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Spatial interactive effects on housing prices in Shanghai and Beijing

Author

Listed:
  • Guo, Juncong
  • Qu, Xi

Abstract

When connected through economic, financial, and policy shocks, cities can be considered to be neighbors in the social-economic sense. Therefore, a spatial correlation may exist in cities that are not geographically contiguous. The paper simultaneously studies the spatial correlation within each city and the spatial interactive effects among different cities on housing prices. We build a spatial autoregressive hedonic pricing model in a system of interrelated networks, which allows for multiple spatial interactive effects among housing units inside the same city and from other cities. For estimation, we propose a two-stage least squares (2SLS) method and the maximum likelihood estimation (MLE). Finite sample properties of these two methods are investigated in a Monte Carlo simulation. After applying these steps to March 2016 housing prices in Shanghai and Beijing, the empirical findings show that the spatial correlations within each city are significantly large, i.e., approximately 0.6–0.8, and spatial interactive effects between the two cities exist, although the magnitude is smaller, i.e., approximately 0.1–0.2.

Suggested Citation

  • Guo, Juncong & Qu, Xi, 2019. "Spatial interactive effects on housing prices in Shanghai and Beijing," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 147-160.
  • Handle: RePEc:eee:regeco:v:76:y:2019:i:c:p:147-160
    DOI: 10.1016/j.regsciurbeco.2018.07.006
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    Citations

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    Cited by:

    1. Ling Li & Fangzhou Xia, 2023. "City subcenter as a regional development policy: Impact on the property market," Journal of Regional Science, Wiley Blackwell, vol. 63(3), pages 643-673, June.
    2. Lulin Xu & Zhongwu Li, 2021. "A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 617-637, February.
    3. Fan, Ying & Fu, Yuqi & Yang, Zan, 2024. "Door-in-the-face heuristics: Intermediaries’ diversion in rental markets," Working Paper Series 24/2, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    4. Gabriel S. Lee & Stefanie Braun, 2021. "Agglomeration Spillover Effects in German Land and House Prices at the City and County Levels," Working Papers 207, Bavarian Graduate Program in Economics (BGPE).
    5. Yang, Zhenbing & Chen, Zhuo & Shao, Shuai & Yang, Lili, 2022. "Can housing price regulation improve R&D performance in universities? Evidence from China," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    6. Chen, Jie & Chen, Yu & Hill, Robert J. & Hu, Pei, 2022. "The user cost of housing and the price-rent ratio in Shanghai," Regional Science and Urban Economics, Elsevier, vol. 92(C).
    7. Lu, Yunzhi & Li, Jie & Yang, Haisheng, 2021. "Time-varying inter-urban housing price spillovers in China: Causes and consequences," Journal of Asian Economics, Elsevier, vol. 77(C).
    8. Yunlong Gong & Jan de Haan & Peter Boelhouwer, 2020. "Cross‐city spillovers in Chinese housing markets: From a city network perspective," Papers in Regional Science, Wiley Blackwell, vol. 99(4), pages 1065-1085, August.
    9. Fan, Ying & Fu, Yuqi & Yang, Zan & Chen, Ming, 2024. "Search frictions in rental markets: Evidence from urban China," China Economic Review, Elsevier, vol. 83(C).
    10. Ling Li & Wayne Xinwei Wan & Shenjing He, 2021. "The Heightened ‘Security Zone’ Function of Gated Communities during the COVID-19 Pandemic and the Changing Housing Market Dynamic: Evidence from Beijing, China," Land, MDPI, vol. 10(9), pages 1-21, September.
    11. 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).
    12. Fan, Ying & Fu, Yuqi & Yang, Zan & Chen, Ming, 2023. "Search Frictions in Rental Markets: Evidence from Urban China," Working Paper Series 23/11, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.

    More about this item

    Keywords

    Spatial autoregressive model; Spatial interactive effect; Hedonic housing price;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

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