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On the Predictive Content of Leading Indicators: The Case of US Real Estate Markets

Author

Listed:
  • Sotiris Tsolacos

    (Centre for Spatial and Real Estate Economics (CSpREE)University of Reading)

  • Chris Brooks

    (ICMA Centre, Henley Business School, University of Reading)

  • Ogonna Nneji

    (ICMA Centre, Henley Business School, University of Reading)

Abstract

This paper employs a probit model and a Markov switching model using information from the Conference Board Leading Indicator series to detect the turning points in four key US commercial rents series. We find that both the approaches based on the leading indicator have considerable power to predict changes in the direction of commercial rents up to two years ahead, exhibiting strong improvements over a naïve model, especially for the warehouse and apartment sectors. The empirical support for the adequacy of these prediction methodologies, from both in-sample and real time forecasting assessments, makes them a valuable tool to real estate professionals forecasting the US real estate markets. We find that while the Markov switching model nominally appears to be more successful in predicting periods of negative growth, it lags behind actual turnarounds in market outcomes whereas the probit is able to detect turning points several quarters ahead.

Suggested Citation

  • Sotiris Tsolacos & Chris Brooks & Ogonna Nneji, 2013. "On the Predictive Content of Leading Indicators: The Case of US Real Estate Markets," ICMA Centre Discussion Papers in Finance icma-dp2013-02, Henley Business School, University of Reading, revised Jun 2013.
    • Handle: RePEc:rdg:icmadp:icma-dp2013-02
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    Keywords

    Leading indicator; US rents; turning point forecasting; direction prediction;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • R39 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other

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