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Integration and Contagion in US Housing Markets

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  • John Cotter
  • Stuart Gabriel
  • Richard Roll

Abstract

This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metropolitan house price returns as well as MSA contemporaneous and lagged jump correlations. Finally, the paper evaluates contagion in housing markets via parametric assessment of MSA house price spatial dynamics. A R-squared measure reveals an upward trend in MSA housing market integration over the 2000s to approximately .83 in 2010. Among California MSAs, the trend was especially pronounced, as average integration increased from about .55 in 1997 to close to .95 in 2008! The 2000s bubble period similarly was characterized by elevated incidence of statistical jumps in housing returns. Again, jump incidence and MSA jump correlations were especially high in California. Analysis of contagion among California markets indicates that house price returns in San Francisco often led those of surrounding communities; in contrast, southern California MSA house price returns appeared to move largely in lock step. The high levels of housing market integration evidenced in the analysis suggest limited investor opportunity to diversify away MSA-specific housing risk. Further, results suggest that macro and policy shocks propagate through a large number of MSA housing markets. Research findings are relevant to all market participants, including institutional investors in MBS as well as those who regulate housing, the housing GSEs, mortgage lenders, and related financial institutions.

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  • John Cotter & Stuart Gabriel & Richard Roll, 2011. "Integration and Contagion in US Housing Markets," Papers 1110.4119, arXiv.org.
    • Handle: RePEc:arx:papers:1110.4119
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    Cited by:

    1. I-Chun Tsai, 2019. "Interregional correlations in the US housing market at three price tiers," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 63(1), pages 1-24, August.
    2. Landier, Augustin & Sraer, David & Thesmar, David, 2017. "Banking integration and house price co-movement," Journal of Financial Economics, Elsevier, vol. 125(1), pages 1-25.
    3. David Genesove & Lu Han, 2012. "A Spatial Look at Housing Boom and Bust Cycles," NBER Chapters, in: Housing and the Financial Crisis, pages 105-141, National Bureau of Economic Research, Inc.
    4. Cheol Eun & Lingling Wang & Tim Zhang, 2022. "House Price Growth Synchronization and Business Cycle Alignment," The Journal of Real Estate Finance and Economics, Springer, vol. 65(4), pages 675-710, November.
    5. Todd Sinai, 2012. "House Price Moments in Boom-Bust Cycles," NBER Chapters, in: Housing and the Financial Crisis, pages 19-68, National Bureau of Economic Research, Inc.
    6. Weida Kuang & Qilin Wang, 2018. "Cultural similarities and housing market linkage: evidence from OECD countries," Frontiers of Business Research in China, Springer, vol. 12(1), pages 1-25, December.
    7. Girum D. Abate & Luc Anselin, 2016. "House price fluctuations and the business cycle dynamics," CREATES Research Papers 2016-06, Department of Economics and Business Economics, Aarhus University.
    8. Fernando Ferreira & Joseph Gyourko, 2012. "Heterogeneity in Neighborhood-Level Price Growth in the United States, 1993-2009," American Economic Review, American Economic Association, vol. 102(3), pages 134-140, May.
    9. Gornall, Will & Strebulaev, Ilya A., 2018. "Financing as a supply chain: The capital structure of banks and borrowers," Journal of Financial Economics, Elsevier, vol. 129(3), pages 510-530.
    10. Alfred Larm Teye & Daniel Felix Ahelegbey, 2017. "Spatial and Temporal House Price Diffusion in the Netherlands: A Bayesian Network Approach," ERES eres2017_337, European Real Estate Society (ERES).
    11. Teye, Alfred Larm & Ahelegbey, Daniel Felix, 2017. "Detecting spatial and temporal house price diffusion in the Netherlands: A Bayesian network approach," Regional Science and Urban Economics, Elsevier, vol. 65(C), pages 56-64.

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    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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