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Identification of Real Estate Cycles in China Based on Artificial Neural Networks

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  • Hong Zhang
  • Shuai Gao
  • Michael J. Seiler
  • Yang Zhang

Abstract

In this paper, we use artificial neural networks to determine the real estate cycle in China. We identify its development phases based on 1993–2008 historical training samples. The results indicate that China's real estate market has oscillational characteristics and the artificial neural networks have predictive accuracy. In the context of continuously deepening governmental interventions, the volatility in real estate cycles has become more evident since 2008, when the market reached its peak in 2009, but quickly plunged into recession in 2010, and approached its trough in 2011. A series of governmental macro-control policies since 2008 have had tremendous impact on the duration and frequency of China's real estate cycles via actions aimed at controlling the expansion of the real estate industry.

Suggested Citation

  • Hong Zhang & Shuai Gao & Michael J. Seiler & Yang Zhang, 2015. "Identification of Real Estate Cycles in China Based on Artificial Neural Networks," Journal of Real Estate Literature, Taylor & Francis Journals, vol. 23(1), pages 65-83, January.
    • Handle: RePEc:taf:rjelxx:v:23:y:2015:i:1:p:65-83
    DOI: 10.1080/10835547.2015.12090399
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