IDEAS home Printed from https://ideas.repec.org/p/fip/fedcwp/1524.html
   My bibliography  Save this paper

Clustered Housing Cycles

Author

Listed:
  • Hernandez-Murillo, Ruben

    () (Federal Reserve Bank of Cleveland)

  • Owyang, Michael T.

    () (Federal Reserve Bank of St. Louis)

  • Rubio, Margarita

    () (University of Nottingham)

Abstract

Using a panel of U.S. city-level building permits data, we estimate a Markov-switching model of housing cycles that allows for idiosyncratic departures from a national housing cycle. These departures occur for clusters of cities that experience simultaneous housing contractions. We find that cities do not form housing regions in the traditional geographic sense. Instead, similarities in factors affecting the demand for housing (such as average winter temperature and the unemployment rate) appear to be more important determinants of cyclical comovements than similarities in factors affecting the supply for housing (such as housing density and the availability of developable land).

Suggested Citation

  • Hernandez-Murillo, Ruben & Owyang, Michael T. & Rubio, Margarita, 2015. "Clustered Housing Cycles," Working Papers (Old Series) 1524, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1524
    as

    Download full text from publisher

    File URL: https://www.clevelandfed.org/~/media/content/newsroom%20and%20events/publications/working%20papers/2015/wp%201524%20clustered%20housing%20cycles%20pdf.pdf?la=en
    File Function: Full text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Edward E. Leamer, 2007. "Housing is the business cycle," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 149-233.
    2. Morris A. Davis, 2010. "housing and the business cycle," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
    3. Matteo Iacoviello, 2005. "House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle," American Economic Review, American Economic Association, vol. 95(3), pages 739-764, June.
    4. Yunjong Eo & Chang-Jin Kim, 2016. "Markov-Switching Models with Evolving Regime-Specific Parameters: Are Postwar Booms or Recessions All Alike?," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 940-949, December.
    5. Jim Clayton & Norman Miller & Liang Peng, 2010. "Price-volume Correlation in the Housing Market: Causality and Co-movements," The Journal of Real Estate Finance and Economics, Springer, vol. 40(1), pages 14-40, January.
    6. Albert Saiz, 2010. "The Geographic Determinants of Housing Supply," The Quarterly Journal of Economics, Oxford University Press, vol. 125(3), pages 1253-1296.
    7. John Cotter & Stuart Gabriel & Richard Roll, 2012. "Can Metropolitan Housing Risk be Diversified? A Cautionary Tale from the Recent Boom and Bust," Papers 1208.0371, arXiv.org.
    8. Yannis M. Ioannides & Jeffrey E. Zabel, 2003. "Neighbourhood effects and housing demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(5), pages 563-584.
    9. Holly, Sean & Hashem Pesaran, M. & Yamagata, Takashi, 2011. "The spatial and temporal diffusion of house prices in the UK," Journal of Urban Economics, Elsevier, vol. 69(1), pages 2-23, January.
    10. Ryan R. Brady, 2011. "Measuring the diffusion of housing prices across space and over time," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 213-231, March.
    11. Owyang, Michael T. & Piger, Jeremy M. & Wall, Howard J. & Wheeler, Christopher H., 2008. "The economic performance of cities: A Markov-switching approach," Journal of Urban Economics, Elsevier, vol. 64(3), pages 538-550, November.
    12. Paap, Richard & Segers, Rene & van Dijk, Dick, 2009. "Do Leading Indicators Lead Peaks More Than Troughs?," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 528-543.
    13. Gerald Carlino & Keith Sill, 2001. "Regional Income Fluctuations: Common Trends And Common Cycles," The Review of Economics and Statistics, MIT Press, vol. 83(3), pages 446-456, August.
    14. Matteo Iacoviello & Stefano Neri, 2010. "Housing Market Spillovers: Evidence from an Estimated DSGE Model," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(2), pages 125-164, April.
    15. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2005. "Business Cycle Phases in U.S. States," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 604-616, November.
    16. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    17. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    18. Landier, Augustin & Sraer, David & Thesmar, David, 2013. "Banking Deregulation and The Rise in House Price Comovement," IDEI Working Papers 799, Institut d'Économie Industrielle (IDEI), Toulouse.
    19. James D. Hamilton & Michael T. Owyang, 2012. "The Propagation of Regional Recessions," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 935-947, November.
    20. Oikarinen, Elias, 2006. "Price Linkages between Stock, Bond and Housing Markets - Evidence from Finnish Data," Discussion Papers 1004, The Research Institute of the Finnish Economy.
    21. Ghent, Andra C. & Owyang, Michael T., 2010. "Is housing the business cycle? Evidence from US cities," Journal of Urban Economics, Elsevier, vol. 67(3), pages 336-351, May.
    22. 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.
    23. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
    24. Emanuel Moench & Serena Ng, 2011. "A hierarchical factor analysis of U.S. housing market dynamics," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 1-24, February.
    25. Jan K. Brueckner, 2000. "Urban Sprawl: Diagnosis and Remedies," International Regional Science Review, , vol. 23(2), pages 160-171, April.
    26. Fruhwirth-Schnatter, Sylvia & Kaufmann, Sylvia, 2008. "Model-Based Clustering of Multiple Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 78-89, January.
    27. 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.
    28. Coulson, N. Edward & Liu, Crocker H. & Villupuram, Sriram V., 2013. "Urban economic base as a catalyst for movements in real estate prices," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 1023-1040.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:jmacro:v:56:y:2018:i:c:p:152-171 is not listed on IDEAS
    2. Michael Funke & Robert Kirkby & Petar Mihaylovski, 2017. "House Prices and Macroprudential Policy in an Estimated DSGE Model of New Zealand," CESifo Working Paper Series 6487, CESifo Group Munich.
    3. Funke, Michael & Kirkby, Robert & Mihaylovski, Petar, 2018. "House prices and macroprudential policy in an estimated DSGE model of New Zealand," Journal of Macroeconomics, Elsevier, vol. 56(C), pages 152-171.
    4. Fontana, Alessandro & Corradin, Stefano, 2013. "House price cycles in Europe," Working Paper Series 1613, European Central Bank.

    More about this item

    Keywords

    clustered Markov switching; business cycles; building permits; comovements;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fip:fedcwp:1524. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (4D Library). General contact details of provider: http://edirc.repec.org/data/frbclus.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.