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Housing markets play a crucial role in economies and the collapse of areal-estate bubble usually destabilizes the financial system and causeseconomic recessions. We investigate the systemic risk and spatiotemporaldynamics of the US housing market (1975-2011) at the state level based on theRandom Matrix Theory (RMT). We identify rich economic information in thelargest eigenvalues deviating from RMT predictions and unveil that thecomponent signs of the eigenvectors contain either geographical information orthe extent of differences in house price growth rates or both. Our results showthat the US housing market experienced six different regimes, which isconsistent with the evolution of state clusters identified by the boxclustering algorithm and the consensus clustering algorithm on the partialcorrelation matrices. Our analysis uncovers that dramatic increases in thesystemic risk are usually accompanied with regime shifts, which provides ameans of early detection of housing bubbles.

Systemic risk and spatiotemporal dynamics of the US housing market