1] School of Business, East China University of Science and Technology, Shanghai 200237, China [2] School of Science, East China University of Science and Technology, Shanghai 200237, China.
1] School of Business, East China University of Science and Technology, Shanghai 200237, China [2] Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China.
Sci Rep. 2014 Jan 13;4:3655. doi: 10.1038/srep03655.
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.
住房市场在经济中发挥着至关重要的作用，房地产泡沫的破裂通常会使金融体系失去稳定，并导致经济衰退。我们基于随机矩阵理论（RMT）研究了美国住房市场（1975-2011 年）的系统性风险和时空动态。我们发现，与股票市场相比，偏离 RMT 预测的最大特征值中包含更丰富的经济信息，并且特征向量的分量符号包含地理信息或房价增长率差异的程度，或者两者都有。通过观察特征值和特征向量等不同数量的演变，我们发现美国住房市场经历了六个不同的阶段，这与通过箱聚类算法和共识聚类算法在偏相关矩阵上确定的州聚类的演变一致。我们发现，系统性风险的急剧增加通常伴随着制度转变，这为住房泡沫的早期检测提供了一种手段。
