Plakandaras, VasiliosGupta, RanganGogas, PeriklisPapadimitriou, Theophilos2016-05-052016-05-052015-02Plakandaras, V, Gupta, R, Gogas, P & Papadimitriou, T 2015, 'Forecasting the U.S. real house price index', Economic Modelling, vol. 45, pp. 259-267.0264-9993 (print)1873-6122 (online)10.1016/j.econmod.2014.10.050http://hdl.handle.net/2263/52456The 2006 sudden and immense downturn inU.S. house prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paperwe 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), 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 price drops with direct policy implications.en© 2014 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Economic Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Economic Modelling, vol. 45, pp. 259-267, 2015. doi : 10.1016/j.econmod.2014.10.050.House pricesForecastingMachine learningSupport Vector RegressionPostprint Article