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Forecasting the U.S. Real House Price Index

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  • 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.

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Bibliographic Info

Paper provided by Democritus University of Thrace, Department of Economics in its series DUTH Research Papers in Economics with number 10-2014.

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Length: 25 pages
Date of creation: 30 Apr 2014
Date of revision:
Handle: RePEc:ris:duthrp:2014_010

Contact details of provider:
Postal: Department of Economics, University Campus, Komotini, 69100, Greece
Phone: (25310) 39.503
Fax: (25310) 39.502
Web page: http://www.econ.duth.gr/
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Keywords: house prices forecasting; machine learning; Support Vector Regression;

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