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Forecasting EREIT Returns

Author

Listed:
  • Camilo Serrano

    (University of Geneva)

  • Martin Hoesli

    (University of Geneva, University of Aberdeen, Bordeaux Business School and Swiss Finance Institute)

Abstract

This paper analyzes the role played by financial assets, direct real estate, and the Fama and French factors in explaining EREIT returns and examines the usefulness of these variables in forecasting returns. Four models are analyzed and their predictive potential is assessed by comparing three forecasting methods: time varying coefficient (TVC) regressions, vector autoregressive (VAR) systems, and neural networks models. Trading strategies on these forecasts are compared to a passive buy-and-hold strategy. The results show that EREIT returns are better explained by models including the Fama and French factors. The VAR forecasts are better than the TVC forecasts, but the best predictions are obtained with neural networks and especially when they are applied to the model using stock, bond, real estate, size, and book-to-market factors.

Suggested Citation

  • Camilo Serrano & Martin Hoesli, 2007. "Forecasting EREIT Returns," Swiss Finance Institute Research Paper Series 07-35, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp0735
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    Citations

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    Cited by:

    1. Camilo Serrano & Martin Hoesli, 2010. "Are Securitized Real Estate Returns more Predictable than Stock Returns?," The Journal of Real Estate Finance and Economics, Springer, vol. 41(2), pages 170-192, August.
    2. Massimo Guidolin & Francesco Ravazzolo & Andrea Tortora, 2014. "Myths and Facts about the Alleged Over-Pricing of U.S. Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 49(4), pages 477-523, November.
    3. Camilo Serrano & Martin Hoesli, 2012. "Fractional Cointegration Analysis of Securitized Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 44(3), pages 319-338, April.
    4. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.
    5. Massimo Guidolin & Francesco Ravazzolo & Andrea Donato Tortora, 2011. "Myths and facts about the alleged over-pricing of U.S. real estate. Evidence from multi-factor asset pricing models of REIT returns," Working Paper 2011/19, Norges Bank.
    6. Daniele Bianchi & Massimo Guidolin, 2014. "Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios," The Journal of Real Estate Finance and Economics, Springer, vol. 49(1), pages 116-164, July.
    7. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
    8. Omokolade Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2016. "Real estate returns predictability revisited: novel evidence from the US REITs market," Empirical Economics, Springer, vol. 51(3), pages 1165-1190, November.
    9. De Santis, Paola & Drago, Carlo, 2014. "Asimmetria del rischio sistematico dei titoli immobiliari americani: nuove evidenze econometriche [Systematic Risk Asymmetry of the American Real Estate Securities: Some New Econometric Evidence]," MPRA Paper 59381, University Library of Munich, Germany.
    10. Vishaal Baulkaran & Pawan Jain & Mark Sunderman, 2019. "Housing “Beta”: Common Risk Factor in Returns of Stocks," The Journal of Real Estate Finance and Economics, Springer, vol. 58(3), pages 438-456, April.
    11. Andrew Ang & Neil Nabar & Sam Wald, 2013. "Search for a Common Factor in Public and Private Real Estate Returns," NBER Working Papers 19194, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Forecasting; Multifactor Models; EREITs; Securitized Real Estate;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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