Systemic risk and spatiotemporal dynamics of the US housing market

TL;DR

This study analyzes the US housing market from 1975 to 2011 using Random Matrix Theory to identify systemic risk, regime shifts, and early warning signals for housing bubbles at the state level.

Contribution

It applies RMT to detect economic information in housing market data and links regime shifts to systemic risk increases, offering a novel approach for early bubble detection.

Findings

Six distinct market regimes identified over the study period.

Systemic risk surges are linked to regime shifts.

Eigenvector signs reveal geographical and growth rate differences.

Abstract

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 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.