IDEAS home Printed from https://ideas.repec.org/a/cuf/journl/y2019v20i1houlongli.html
   My bibliography  Save this article

Comovement of Home Prices: A Conditional Copula Approach

Author

Listed:
  • Lei Hou

    (Institute of World Economics and Politics, Chinese Academy of Social Sciences)

  • Wei Long

    (Department of Economics, Tulane University)

  • Qi Li

    (Department of Economics, Texas A&M University)

Abstract

Even though housing markets in different areas are relatively localized, regional home prices have become closely correlated and tend to be simultaneously affected by many national economic factors. In this paper, through the dynamic copula model, we confirm that regional home price dependence is time-varying and the conventional time-invariant copulas underestimate the degree of dependence during economic expansions and recessions. In essence, the U.S. residential real estate market has become more integrated since the mid-1980s. Using the conditional copula model, we further identify how the dependence among regional housing markets evolves along with some fundamental economic factors such as unemployment rate and interest rate. These findings can help investors and home buyers to better identify and evaluate the systematic risk in the nationwide housing market.

Suggested Citation

  • Lei Hou & Wei Long & Qi Li, 2019. "Comovement of Home Prices: A Conditional Copula Approach," Annals of Economics and Finance, Society for AEF, vol. 20(1), pages 297-318, May.
    • Handle: RePEc:cuf:journl:y:2019:v:20:i:1:houlongli
    as

    Download full text from publisher

    File URL: http://down.aefweb.net/AefArticles/aef200112HouLongLi.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hafner, Christian M. & Reznikova, Olga, 2010. "Efficient estimation of a semiparametric dynamic copula model," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2609-2627, November.
    3. Jarl G. Kallberg & Crocker H. Liu & Paolo Pasquariello, 2014. "On the Price Comovement of U.S. Residential Real Estate Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(1), pages 71-108, March.
    4. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 125-154.
    5. Robert J. Shiller, 2007. "Understanding recent trends in house prices and homeownership," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 89-123.
    6. Hans Manner & Olga Reznikova, 2012. "A Survey on Time-Varying Copulas: Specification, Simulations, and Application," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 654-687, November.
    7. Elif F. Acar & Radu V. Craiu & Fang Yao, 2011. "Dependence Calibration in Conditional Copulas: A Nonparametric Approach," Biometrics, The International Biometric Society, vol. 67(2), pages 445-453, June.
    8. David M. Zimmer, 2015. "Analyzing Comovements In Housing Prices Using Vine Copulas," Economic Inquiry, Western Economic Association International, vol. 53(2), pages 1156-1169, April.
    9. Del Negro, Marco & Otrok, Christopher, 2007. "99 Luftballons: Monetary policy and the house price boom across U.S. states," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 1962-1985, October.
    10. Rodriguez, Juan Carlos, 2007. "Measuring financial contagion: A Copula approach," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 401-423, June.
    11. J. Fan & M. Farmen & I. Gijbels, 1998. "Local maximum likelihood estimation and inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 591-608.
    12. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    13. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    14. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bingduo Yang & Christian M. Hafner & Guannan Liu & Wei Long, 2021. "Semiparametric estimation and variable selection for single‐index copula models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 962-988, November.
    2. Acar, Elif F. & Czado, Claudia & Lysy, Martin, 2019. "Flexible dynamic vine copula models for multivariate time series data," Econometrics and Statistics, Elsevier, vol. 12(C), pages 181-197.
    3. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    4. repec:kan:wpaper:202105 is not listed on IDEAS
    5. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    6. Aboura, Sofiane & Chevallier, Julien, 2015. "Volatility returns with vengeance: Financial markets vs. commodities," Research in International Business and Finance, Elsevier, vol. 33(C), pages 334-354.
    7. Avdulaj, Krenar & Barunik, Jozef, 2015. "Are benefits from oil–stocks diversification gone? New evidence from a dynamic copula and high frequency data," Energy Economics, Elsevier, vol. 51(C), pages 31-44.
    8. Kim, Jong-Min & Jung, Hojin, 2016. "Linear time-varying regression with Copula–DCC–GARCH models for volatility," Economics Letters, Elsevier, vol. 145(C), pages 262-265.
    9. Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.
    10. Gregor Weiß, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
    11. Gong Jinguo & Shi Daimin & Wu Weiou & McMillan David, 2015. "Non-parametric estimation of copula parameters: testing for time-varying correlation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 93-106, February.
    12. S Zhang & I Paya & D Peel, 2009. "Linkages between Shanghai and Hong Kong stock indices," Working Papers 599248, Lancaster University Management School, Economics Department.
    13. Han, Yingwei & Li, Ping & Xia, Yong, 2017. "Dynamic robust portfolio selection with copulas," Finance Research Letters, Elsevier, vol. 21(C), pages 190-200.
    14. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    15. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    16. repec:dau:papers:123456789/13359 is not listed on IDEAS
    17. Weiß, Gregor N.F., 2011. "Are Copula-GoF-tests of any practical use? Empirical evidence for stocks, commodities and FX futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 173-188, May.
    18. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    19. Chollete, Loran & Ning, Cathy, 2010. "Asymmetric Dependence in US Financial Risk Factors?," UiS Working Papers in Economics and Finance 2011/2, University of Stavanger.
    20. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    21. Min, Aleksey & Czado, Claudia, 2014. "SCOMDY models based on pair-copula constructions with application to exchange rates," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 523-535.
    22. Reboredo, Juan C., 2013. "Modeling EU allowances and oil market interdependence. Implications for portfolio management," Energy Economics, Elsevier, vol. 36(C), pages 471-480.

    More about this item

    Keywords

    Comovement; Copula; Dependence; Home price;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cuf:journl:y:2019:v:20:i:1:houlongli. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Qiang Gao (email available below). General contact details of provider: https://edirc.repec.org/data/emcufcn.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.