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Are Securitized Real Estate Returns more Predictable than Stock Returns?

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Abstract

This paper examines whether the predictability of securitized real estate returns differs from that of stock returns. It also provides a cross-country comparison of securitized real estate return predictability. In contrast to most of the literature on this issue, the analysis is not based on a multifactor asset pricing framework as such analyses may bias the results. We use a time series approach and thus create a level playing field to compare the predictability of the two asset classes. Forecasts are performed with ARMA and ARMA–EGARCH models and evaluated by comparing the entire empirical distributions of prediction errors, as well as with a trading strategy. The results, based on daily data for the 1990–2007 period, show that securitized real estate returns are generally more predictable than stock returns in countries with mature and well established REIT regimes. ARMA–EGARCH models are found to have portfolio outperformance potential even in the presence of transaction costs, with generally better results for securitized real estate than for stocks.

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Notes

  1. For studies related to these issues in the direct real estate literature, see Young and Graff (1996, 1997) for the U.S., Graff et al. (1999) for Australia, and Lee and Ward (2001) and Devaney et al. (2007) for the U.K.

  2. We also performed preliminary analyses using monthly data but the autocorrelation functions suggest that the data do not follow ARMA processes. Hence, time series forecasts could not be devised at this frequency.

  3. The correlation of Datastream’s total return indices and MSCI’s total return indices is around 95% in all the countries. However, the MSCI total return indices are only available since January 2001.

  4. EPRA Monthly Statistical Bulletin, December 2007.

  5. Akaike’s information criterion (AIC) is often also used to select between competing models, but as noted by Mills (1990), the AIC can result in the selection of an over-parametrized model.

  6. For a review of the volatility forecasting literature, see Poon and Granger (2003). They summarize the methodologies and empirical findings of 93 papers that study the forecasting performance of various volatility models and find that the choice of a model is to some extent data and period specific.

  7. Since we do not perform our forecasts with a single, static model, but with a model that evolves and adapts itself through time, we do not use other diagnostic tests as we are already using the SBC criterion to choose the most appropriate specification.

  8. Table 5 makes it possible to assess the significance of differences, but the determination of which asset class is more predictable is based on graphical inspection of the distributions (Figs. 1 and 2).

  9. The ARMA results are consistent with Nelling and Gyourko’s (1998) findings for the United States as their AR models reveal that EREITs and mid caps are equally predictable.

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Acknowledgements

We are grateful to the participants of the AREUEA 2008 meetings in New Orleans, the European Real Estate Society 2008 conference in Krakow (Poland), and the 2008 Real Estate Research Symposium in Rotterdam (the Netherlands). An anonymous reviewer provided helpful comments. The usual disclaimer applies.

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Authors and Affiliations

  1. University of Geneva (HEC), 40 boulevard du Pont-d’ Arve, CH-1211, Geneva 4, Switzerland

    Camilo Serrano

  2. University of Geneva (HEC and SFI), 40 boulevard du Pont-d’ Arve, CH-1211, Geneva 4, Switzerland

    Martin Hoesli

  3. University of Aberdeen (Business School), Edward Wright Building, Aberdeen, AB24 3QY, Scotland, UK

    Martin Hoesli

  4. Bordeaux Ecole de Management (CEREBEM), F-33405, Talence Cedex, France

    Martin Hoesli

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  1. Camilo Serrano
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Serrano, C., Hoesli, M. Are Securitized Real Estate Returns more Predictable than Stock Returns?. J Real Estate Finan Econ 41, 170–192 (2010). https://doi.org/10.1007/s11146-008-9162-y

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