文献类型：期刊文献

英文题名：Systemic risk and spatiotemporal dynamics of the US housing market

作者：Meng, Hao[1,2];Xie, Wen-Jie[1,2];Jiang, Zhi-Qiang[1,3];Podobnik, Boris[4,5,6,7,8];Zhou, Wei-Xing[1,2,3];Stanley, H. Eugene[4,5]

机构：[1]E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China;[3]E China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China;[4]Boston Univ, Ctr Polymer Studies, Boston, MA 02215 USA;[5]Boston Univ, Dept Phys, Boston, MA 02215 USA;[6]Zagreb Sch Econ & Management, Zagreb 10000, Croatia;[7]Univ Rijeka, Fac Civil Engn, Rijeka 51000, Croatia;[8]Univ Ljubljana, Fac Econ, Ljubljana 1000, Slovenia

年份：2014

卷号：4

外文期刊名：SCIENTIFIC REPORTS

收录：;WOS:【SSCI(收录号:WOS:000329843500009)，SCI-EXPANDED(收录号:WOS:000329843500009)】；

基金：HM, WJX, ZQJ and WXZ received support from the National Natural Science Foundation of China Grant 11075054 and 71131007, the Shanghai (Follow-up) Rising Star Program Grant 11QH1400800, the Shanghai "Chen Guang'' Project Grant 2012CG34, and Fundamental Research Funds for the Central Universities. BP and HES received support from the Defense Threat Reduction Agency (DTRA), the Office of Naval Research (ONR), and the National Science Foundation (NSF) Grant CMMI 1125290.

语种：英文

摘要：Housing 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 richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find 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. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles.