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What drives housing dynamics in China? A sign restrictions VAR approach

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  • Bian, Timothy Yang
  • Gete, Pedro

Abstract

We study housing dynamics in China using vector autoregressions identified with theory-consistent sign restrictions. We study seven potential drivers: (1) population increases; (2) a relaxation of credit standards, for example, due to the shadow banking system; (3) increasing preferences towards housing, for example, due to a housing bubble, or to housing being a status asset in the marriage market; (4) an increase in the savings rate; (5) expected productivity progress; (6) changes in land supply; and (7) tax policy, a proxy for policy stimulus. Our results show that, even if all shocks play relevant roles, productivity, savings glut, and policy stimulus have been the dominant drivers. When the sample is closer to 2014, housing preferences and credit shocks increase their importance to explain house prices and volume, while population shocks explain a larger share of the dynamics of residential investment. The results show some differences if we use house price indices constructed by the government or by private sources. The official indices show smaller increases in house prices and assign a smaller role to credit and preference shocks.

Suggested Citation

  • Bian, Timothy Yang & Gete, Pedro, 2015. "What drives housing dynamics in China? A sign restrictions VAR approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 96-112.
  • Handle: RePEc:eee:jmacro:v:46:y:2015:i:c:p:96-112
    DOI: 10.1016/j.jmacro.2015.08.004
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    Cited by:

    1. Min Jiang & Liangjie Xin & Xiubin Li & Minghong Tan, 2016. "Spatiotemporal Variation of China’s State-Owned Construction Land Supply from 2003 to 2014," Sustainability, MDPI, Open Access Journal, vol. 8(11), pages 1-16, November.
    2. Scheufele, Rolf & Bäurle, Gregor, 2015. "Credit cycles and real activity - the Swiss case," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112931, Verein für Socialpolitik / German Economic Association.
    3. Mali Chivakul & Waikei R Lam & Xiaoguang Liu & Wojciech Maliszewski & Alfred Schipke, 2015. "Understanding Residential Real Estate in China," IMF Working Papers 15/84, International Monetary Fund.
    4. Adrienne Mack & Enrique Martínez-García, 2011. "A cross-country quarterly database of real house prices: a methodological note," Globalization and Monetary Policy Institute Working Paper 99, Federal Reserve Bank of Dallas.
    5. Stefan Bruder & Michael Wolf, 2017. "Balanced bootstrap joint confidence bands for structural impulse response functions," ECON - Working Papers 246, Department of Economics - University of Zurich, revised Jan 2018.
    6. Liu, Chunping & Ou, Zhirong, 2017. "What determines China's housing price dynamics? New evidence from a DSGE-VAR," Cardiff Economics Working Papers E2017/4, Cardiff University, Cardiff Business School, Economics Section.
    7. Gete, Pedro, 2015. "Housing demands, savings gluts and current account dynamics," Globalization and Monetary Policy Institute Working Paper 221, Federal Reserve Bank of Dallas, revised 01 Aug 2015.
    8. Jonas Kibala Kuma, 2018. "Structural VAR Model : Theory review and practices on software
      [Le Modèle VAR Structurel : Eléments de théorie et pratiques sur logiciels]
      ," Post-Print cel-01771221, HAL.
    9. Chen, Hongyi & Chow, Kenneth & Tillmann, Peter, 2017. "The effectiveness of monetary policy in China: Evidence from a Qual VAR," China Economic Review, Elsevier, vol. 43(C), pages 216-231.
    10. repec:bla:ecorec:v:92:y:2016:i:299:p:590-605 is not listed on IDEAS
    11. repec:eee:regeco:v:67:y:2017:i:c:p:1-10 is not listed on IDEAS

    More about this item

    Keywords

    Vector autoregression; Sign restrictions; China; House prices;

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis

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