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Forecasting EREIT Returns

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  • Camilo Serrano
  • Martin Hoesli

Abstract

Executive Summary. This paper analyzes the role played by financial assets, direct real estate, and the Fama and French (1993) factors in explaining equity real estate investment trust (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 autoregres-sive (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 ob-tained 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," Journal of Real Estate Portfolio Management, Taylor & Francis Journals, vol. 13(4), pages 293-310, January.
    • Handle: RePEc:taf:repmxx:v:13:y:2007:i:4:p:293-310
    DOI: 10.1080/10835547.2007.12089784
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    Cited by:

    1. 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.

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