Forecasting the U.S. Real House Price Index

TL;DR

This paper introduces a hybrid forecasting model combining EEMD and SVR to predict U.S. house prices, outperforming traditional models and serving as an early warning system for price drops.

Contribution

The paper presents a novel hybrid EEMD-SVR methodology that improves forecasting accuracy of house prices over existing models.

Findings

Outperforms traditional models with half the error in out-of-sample tests.

Can serve as an early warning system for sudden house price drops.

Demonstrates practical policy implications for economic stability.

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.