Forecasting EREIT Returns

Camilo Serrano and Martin Hoesli
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Camilo Serrano: University of Geneva

No 07-35, Swiss Finance Institute Research Paper Series from 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.

Keywords: Forecasting; Multifactor Models; EREITs; Securitized Real Estate (search for similar items in EconPapers)
JEL-codes: C21 C45 G12 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2007-10
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp0735

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