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Time-Varying Correlation in Housing Prices

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  • David Zimmer

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. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • David Zimmer, 2015. "Time-Varying Correlation in Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 51(1), pages 86-100, July.
  • Handle: RePEc:kap:jrefec:v:51:y:2015:i:1:p:86-100
    DOI: 10.1007/s11146-014-9475-y
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    Citations

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    Cited by:

    1. Dittmann Iwona, 2017. "Similarity of Changes in Average Prices of Residential Properties in Europe in 2010-2016," Real Estate Management and Valuation, Sciendo, vol. 25(4), pages 63-74, December.
    2. Yang Deng & Helen X. H. Bao & Pu Gong, 2018. "Increased Tail Dependence in Global Public Real Estate Markets," International Real Estate Review, Global Social Science Institute, vol. 21(2), pages 145-168.
    3. Chang, Kuang-Liang, 2020. "Are cyclical patterns of international housing markets interdependent?," Economic Modelling, Elsevier, vol. 88(C), pages 14-24.
    4. Kang, Sang Hoon & Uddin, Gazi Salah & Ahmed, Ali & Yoon, Seong-Min, 2018. "Multi-scale causality and extreme tail inter-dependence among housing prices," Economic Modelling, Elsevier, vol. 70(C), pages 301-309.
    5. Simlai, Prodosh, 2019. "Subprime credit, idiosyncratic risk, and foreclosures," The Quarterly Review of Economics and Finance, Elsevier, vol. 74(C), pages 175-189.
    6. Rakesh K. Bissoondeeal & Leonidas Tsiaras, 2023. "Investigating the Links between UK House Prices and Share Prices with Copulas," The Journal of Real Estate Finance and Economics, Springer, vol. 67(3), pages 423-452, October.
    7. N. Kundan Kishor & Hardik A. Marfatia, 2017. "The Dynamic Relationship Between Housing Prices and the Macroeconomy: Evidence from OECD Countries," The Journal of Real Estate Finance and Economics, Springer, vol. 54(2), pages 237-268, February.
    8. Andréas Heinen & James B. Kau & Donald C. Keenan & Mi Lim Kim, 2021. "Spatial Dependence in Subprime Mortgage Defaults," The Journal of Real Estate Finance and Economics, Springer, vol. 62(1), pages 1-24, January.
    9. Chang, Kuang-Liang, 2020. "An investigation on mixed housing-cycle structures and asymmetric tail dependences," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

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    More about this item

    Keywords

    Copula; CDO; Dependence; Contagion; G21; C32; C51;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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