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Forecasting the U.S. Real House Price Index

Author

Listed:
  • Plakandaras, Vasilios

    (Democritus University of Thrace, Department of Economics)

  • Gupta, Rangan

    (Pretoria University, Department of Economics)

  • Papadimitriou, Theophilos

    (Democritus University of Thrace, Department of Economics)

  • Gogas, Periklis

    (Democritus University of Thrace, Department of Economics)

Abstract
The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.

Suggested Citation

  • Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
    • Handle: RePEc:ris:duthrp:2014_010
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    More about this item

    Keywords

    house prices forecasting; machine learning; Support Vector Regression;
    All these keywords.

    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

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