The 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.