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International Evidence of the Predictability of Prices of Securititised Real Estate Assets: Econometric Models versus Neural Networks

Author

Listed:
  • Chris Brooks

    () (ICMA Centre, University of Reading)

  • Sotiris Tsolacos

    () (Jones Lang LaSalle)

Abstract

This paper examines the performance of various statistical models and commonly used financial indicators for forecasting securitised real estate index returns 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 securitised returns than the term structure or the dividend yield. Predictions obtained from the VAR and other univariate time-series models are compared with the predictions of an artificial neural network model. We find 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, while for quarterly and half-yearly forecasting, it is hard to better the random walk with drift. Therefore, investors in securitised real estate assets should be aware that the application of a forecast model without evidence of its predictive performance in a particular market could lead to sub-optimal forecasts.

Suggested Citation

  • Chris Brooks & Sotiris Tsolacos, 2001. "International Evidence of the Predictability of Prices of Securititised Real Estate Assets: Econometric Models versus Neural Networks," ICMA Centre Discussion Papers in Finance icma-dp2001-08, Henley Business School, Reading University.
  • Handle: RePEc:rdg:icmadp:icma-dp2001-08
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    File URL: http://www.icmacentre.ac.uk/pdf/discussion/DP2001-08.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Dirk Brounen & Piet Eichholtz & David Ling, 2007. "Trading Intensity and Real Estate Performance," The Journal of Real Estate Finance and Economics, Springer, vol. 35(4), pages 449-474, November.

    More about this item

    Keywords

    Real estate returns; vector autoregressive models; neural networks; forecasting;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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