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Housing price diffusions in mainland China: evidence from a spatially penalized graphical VAR model

Author

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  • Xiandeng Jiang

    (Southwestern University of Finance and Economics)

  • Le Chang

    (Australian National University)

  • Yanlin Shi

    (Macquarie University)

Abstract

Although China has a large territory, few studies consider the factor of geographical distance when studying spatial interactions among city-level prices. We study housing price diffusions among seventy medium- and large-sized Chinese cities between July 2005 and August 2017 by using a spatially penalized graphical vector autoregression method that incorporates the intercity geographical distance. Our study reveals that the cities with the largest positive net centrality in each region are Nanchang (Central), Nanjing (Southeast), Changchun (Northeast), Beijing (North), Xiamen (South), Chongqing (Southwest) and Xining (Northwest). Furthermore, our results suggest that the four largest cities do not present the largest net centrality. Instead, many non-top-developed and non-major cities can play a significant role in affecting the regional housing markets. Our findings demonstrate that neglecting the influences of non-major cities may reduce the effectiveness and efficiency of relevant policies.

Suggested Citation

  • Xiandeng Jiang & Le Chang & Yanlin Shi, 2023. "Housing price diffusions in mainland China: evidence from a spatially penalized graphical VAR model," Empirical Economics, Springer, vol. 64(2), pages 765-795, February.
    • Handle: RePEc:spr:empeco:v:64:y:2023:i:2:d:10.1007_s00181-022-02264-y
    DOI: 10.1007/s00181-022-02264-y
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    More about this item

    Keywords

    Housing prices; Graphical models; Geographical distance; Centrality; Spatial spillovers;
    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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R22 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other Demand

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