Forecasting EREIT Returns

37 Pages Posted: 17 Oct 2007 Last revised: 25 Sep 2009

See all articles by Camilo Serrano

Camilo Serrano


Martin Hoesli

University of Geneva - Geneva School of Economics and Management (GSEM); Swiss Finance Institute; University of Aberdeen - Business School

Date Written: October 1, 2007


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.

Keywords: Forecasting, Multifactor Models, EREITs, Securitized Real Estate

JEL Classification: G12, C21, C45

Suggested Citation

Serrano, Camilo and Hoesli, Martin Edward Ralph, Forecasting EREIT Returns (October 1, 2007). Journal of Real Estate Portfolio Management, Vol. 13, No. 4, 2007, Swiss Finance Institute Research Paper No. 07-35, Available at SSRN:

Camilo Serrano

IAZI AG - CIFI SA ( email )

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Martin Edward Ralph Hoesli (Contact Author)

University of Geneva - Geneva School of Economics and Management (GSEM) ( email )

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Geneva 4, Geneva 1211
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Swiss Finance Institute

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University of Aberdeen - Business School ( email )

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