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

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Abstract

We study housing dynamics in China using vector autoregressions identified with theoryconsistent sign restrictions. We study five 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 housing being a status asset to be competitive in the marriage market; 4) an increase in the savings rate; and 5) expected productivity progress. Our results show that fundamental shocks (population, credit and productivity) play a major role in the dynamics of house prices and residential investment before 2009. Preference shocks seem especially relevant in the last several years, and when the estimation uses price indices not coming from China’s National Bureau of Statistics.

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  • Bian, Timothy Yang & Gete, Pedro, 2014. "What drives housing dynamics in China? a sign restrictions VAR approach," Globalization and Monetary Policy Institute Working Paper 193, Federal Reserve Bank of Dallas.
  • Handle: RePEc:fip:feddgw:193
    DOI: 10.24149/gwp193
    Note: Published as: Bian, Timothy Yang and Pedro Gete (2015), "What Drives Housing Dynamics in China? A Sign Restrictions VAR Approach," Journal of Macroeconomics 46: 96-112.
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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. 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.
    4. 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.
    5. repec:bla:ecorec:v:92:y:2016:i:299:p:590-605 is not listed on IDEAS
    6. 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.
    7. 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.
    8. repec:eee:regeco:v:67:y:2017:i:c:p:1-10 is not listed on IDEAS
    9. 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.
    10. 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.
    11. 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.

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    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • F44 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - International Business Cycles
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • 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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