Time-Varying Correlation in Housing Prices

Abstract

In the wake of the housing crisis, credit rating agencies have received much blame, particularly for the statistical tools they used to measure correlations in housing prices in different locations. Several studies have proposed alternative statistical models, but to date, all such approaches assume that correlations remain constant over time. This paper argues that, regardless of the correlation patterns built into such statistical models, correlations might strengthen during times of financial turmoil. Consequently, mortgage-backed securities might have been appropriately diversified during " normal" times, but less so during extreme market swings. Using monthly data on housing prices in four major U.S. cities, the main findings confirm that housing prices do, indeed, exhibit correlations that change over time, and more importantly, those correlations appear to strengthen in the midst of market turmoil.

Has the euro sustainably increased home price co‐movement?

“…Houses are also an asset, and GARCH models have been used in investigating housing returns (Miles, is an early example). Indeed, the specific DCC‐GARCH estimator we will use has been utilized to examine housing market dynamics across US metropolitan regions (Zimmer, ). Thus GARCH is a tool that has frequently been applied in studies of housing.…”

What Forces Drive the Dynamic Interaction Between Regional Housing Prices?

“…the copula is a convenient tool to integrate bivariate distributions even when the distributions are unknown or extremely complex. 5 additionally, there is concurrently a growing application of the time-varying copula method in housing market research, which shows that the copula is a decent tool for modeling housing prices (Zimmer 2012(Zimmer , 2015. Zimmer (2012) indicates that jointly related asset prices may exhibit departures from normality, particularly in the tails, and he explores the housing price connection during the financial crisis using various copula specifications.…”

Regime switches and permanent changes in impacts of housing risk factors on MSA‐level housing returns

“…Also, Moscone, Tosetti, and Canepa (2014) suggest that a pronounced spatial concentration during the housing bust resulted from worsened social and economic conditions. On the basis of their estimated wavelet power spectra, Flor and Klarl (2017) find that MSAs exhibited a high degree of co‐movements after the burst of the housing bubble in 2007. Zimmer (2015) documents that cross‐city price correlations vary over time and that they are stronger during financial turmoil than during normal periods. Zhu, Fuss, and Rottke (2013) propose an obvious rise in MSA interdependence with stronger interdependence intensity and larger contagious range during the 2007–2009 crisis.…”