  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.
