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International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

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Brooks, C. and Tsolacos, S. (2003) International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks. Journal of Property Research, 20 (2). pp. 133-155. ISSN 1466-4453

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To link to this article DOI: 10.1080/0959991032000109517

Abstract/Summary

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.

Item Type:Article
Refereed:Yes
Divisions:Henley Business School > ICMA Centre
ID Code:21317
Uncontrolled Keywords:Real Estate Returns, Vector Autoregressive Models, Neural Networks, Forecasting
Publisher:Routledge

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