Systemic risk and spatiotemporal dynamics of the US housing market
AbstractHousing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify rich economic information in the largest eigenvalues deviating from RMT predictions and unveil that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. Our results show that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. Our analysis uncovers that dramatic increases in the systemic risk are usually accompanied with regime shifts, which provides a means of early detection of housing bubbles.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1306.2831.
Date of creation: Jun 2013
Date of revision:
Publication status: Published in Scientific Reports 4, 3655 (2014)
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-16 (All new papers)
- NEP-RMG-2013-06-16 (Risk Management)
- NEP-URE-2013-06-16 (Urban & Real Estate Economics)
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