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Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails

Author

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  • Tamás Kiss

    (Division of Economics, School of Business, Örebro University, 701 82 Örebro, Sweden)

  • Hoang Nguyen

    (Division of Statistics, School of Business, Örebro University, 701 82 Örebro, Sweden)

  • Pär Österholm

    (Division of Economics, School of Business, Örebro University, 701 82 Örebro, Sweden
    National Institute of Economic Research, 102 23 Stockholm, Sweden)

Abstract

In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from January 1954 to September 2019, the properties of the models were assessed both within- and out-of-sample. We found strong evidence in favour of modelling both GARCH effects and non-Gaussianity. Accounting for these properties improves within-sample performance as well as point and density forecasts.

Suggested Citation

  • Tamás Kiss & Hoang Nguyen & Pär Österholm, 2021. "Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails," JRFM, MDPI, vol. 14(11), pages 1-17, October.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:506-:d:660957
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    1. Ibrahim Ahamada & Sanchez José Luis Diaz, 2013. "A retrospective analysis of the house prices macro-relationship in the United States," Post-Print hal-00816717, HAL.
    2. Giorgio Fagiolo & Mauro Napoletano & Andrea Roventini, 2008. "Are output growth-rate distributions fat-tailed? some evidence from OECD countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 639-669.
    3. Juan I Martín-Legendre & Pablo Castellanos-García & José M Sánchez-Santos, 2019. "Housing and financial wealth effects on consumption: Evidence from the Spanish Survey of Household Finances," Economics Bulletin, AccessEcon, vol. 39(3), pages 1930-1940.
    4. Hess Chung & Jean‐Philippe Laforte & David Reifschneider & John C. Williams, 2012. "Have We Underestimated the Likelihood and Severity of Zero Lower Bound Events?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(s1), pages 47-82, February.
    5. Campbell, John Y. & Cocco, Joao F., 2007. "How do house prices affect consumption? Evidence from micro data," Journal of Monetary Economics, Elsevier, vol. 54(3), pages 591-621, April.
    6. Josephine Dufitinema, 2021. "Stochastic volatility forecasting of the Finnish housing market," Applied Economics, Taylor & Francis Journals, vol. 53(1), pages 98-114, January.
    7. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1596-1609, September.
    8. Mr. Ananthakrishnan Prasad & Mr. Selim A Elekdag & Mr. Phakawa Jeasakul & Romain Lafarguette & Mr. Adrian Alter & Alan Xiaochen Feng & Changchun Wang, 2019. "Growth at Risk: Concept and Application in IMF Country Surveillance," IMF Working Papers 2019/036, International Monetary Fund.
    9. Ascari, Guido & Fagiolo, Giorgio & Roventini, Andrea, 2015. "Fat-Tail Distributions And Business-Cycle Models," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 465-476, March.
    10. Ibrahim Ahamada & Jose Luis Diaz Sanchez, 2013. "A Retrospective Analysis of the House Prices Macro-Relationship in the United States," International Journal of Central Banking, International Journal of Central Banking, vol. 9(4), pages 153-174, December.
    11. Myer, F C Neil & Webb, James R, 1994. "Statistical Properties of Returns: Financial Assets versus Commercial Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 8(3), pages 267-282, May.
    12. Victor Pontines, 2010. "Fat-tails and house prices in OECD countries," Applied Economics Letters, Taylor & Francis Journals, vol. 17(14), pages 1373-1377.
    13. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    14. Ibrahim Ahamada & Sanchez José Luis Diaz, 2013. "A retrospective analysis of the house prices macro-relationship in the United States," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00816717, HAL.
    15. Charles Leung & Youngman Leong & Siu Wong, 2006. "Housing Price Dispersion: An Empirical Investigation," The Journal of Real Estate Finance and Economics, Springer, vol. 32(3), pages 357-385, May.
    16. Kiss, Tamás & Österholm, Pär, 2020. "Fat tails in leading indicators," Economics Letters, Elsevier, vol. 193(C).
    17. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    18. 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.
    19. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    20. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    21. Vasco Cúrdia & Marco Del Negro & Daniel L. Greenwald, 2014. "Rare Shocks, Great Recessions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1031-1052, November.
    22. 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.
    23. Robertson, John C & Tallman, Ellis W & Whiteman, Charles H, 2005. "Forecasting Using Relative Entropy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 383-401, June.
    24. Lanne, Markku & Meitz, Mika & Saikkonen, Pentti, 2017. "Identification and estimation of non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 196(2), pages 288-304.
    25. Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.
    26. Granziera, Eleonora & Kozicki, Sharon, 2015. "House price dynamics: Fundamentals and expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 152-165.
    27. Richard A. Graff & Adrian Harrington & Michael S. Young, 1997. "The Shape of Australian Real Estate Return Distributions and Comparisons to the United States," Journal of Real Estate Research, American Real Estate Society, vol. 14(3), pages 291-308.
    28. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    29. Todd E. Clark & Francesco Ravazzolo, 2015. "Macroeconomic Forecasting Performance under Alternative Specifications of Time‐Varying Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 551-575, June.
    30. Liu, Xiaochun, 2019. "On tail fatness of macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 62(C).
    31. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    32. Acemoglu, Daron & Scott, Andrew, 1997. "Asymmetric business cycles: Theory and time-series evidence," Journal of Monetary Economics, Elsevier, vol. 40(3), pages 501-533, December.
    33. Michael S. Young & Stephen L. Lee & Steven P. Devaney, 2006. "Non‐Normal Real Estate Return Distributions by Property Type in the UK," Journal of Property Research, Taylor & Francis Journals, vol. 23(2), pages 109-133, March.
    34. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    35. Cogley, Timothy & Morozov, Sergei & Sargent, Thomas J., 2005. "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1893-1925, November.
    36. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    37. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    38. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    39. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    40. Geert Bekaert & Alexander Popov, 2019. "On the Link Between the Volatility and Skewness of Growth," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 67(4), pages 746-790, December.
    41. David G McMillan, 2012. "Long-run stock price-house price relation: evidence from an ESTR model," Economics Bulletin, AccessEcon, vol. 32(2), pages 1737-1746.
    42. Tobias Adrian & Federico Grinberg & Nellie Liang & Sheheryar Malik & Jie Yu, 2022. "The Term Structure of Growth-at-Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(3), pages 283-323, July.
    43. Ma, Chao, 2020. "Momentum and Reversion to Fundamentals: Are They Captured by Subjective Expectations of House Prices?," Journal of Housing Economics, Elsevier, vol. 49(C).
    44. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    45. James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, September.
    46. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-328, April.
    47. Young, Michael S & Graff, Richard A, 1995. "Real Estate Is Not Normal: A Fresh Look at Real Estate Return Distributions," The Journal of Real Estate Finance and Economics, Springer, vol. 10(3), pages 225-259, May.
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    More about this item

    Keywords

    non-Gaussianity; GARCH; probability integral transform; Kullback–Leibler information criterion;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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