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Bubbles or fundamentals? Modeling provincial house prices in China allowing for cross-sectional dependence

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  • Mao, Guangyu
  • Shen, Yan

Abstract

This paper provides an empirical analysis of changes in real housing prices in China using quarterly province-level data from 2001 to 2014. It examines the extent to which real housing price at the provincial level are driven by economic fundamentals, such as real per capita disposable income, real interest rate, and size of urban population. The econometric modeling takes explicit account of provincial heterogeneity, nonstationarity of variables, and cross-sectional dependence across provinces by virtue of the Common Correlated Effects model. We find that fundamentals play a less significant role in explaining the house prices in China. Inconsistent with economic theories, the most important fundamental, real income, cannot completely justify the housing price inflation. Therefore, there may be a housing price bubble in the market.

Suggested Citation

  • Mao, Guangyu & Shen, Yan, 2019. "Bubbles or fundamentals? Modeling provincial house prices in China allowing for cross-sectional dependence," China Economic Review, Elsevier, vol. 53(C), pages 53-64.
  • Handle: RePEc:eee:chieco:v:53:y:2019:i:c:p:53-64
    DOI: 10.1016/j.chieco.2018.08.001
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    3. Wang, Lei & Li, Shouwei & Wang, Jining & Meng, Yi, 2020. "Real estate bubbles in a bank-real estate loan network model integrating economic cycle and macro-prudential stress testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
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    More about this item

    Keywords

    Bubbles; Common factors; Cross-sectional dependence; Housing price;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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