Forecasting the U.S. Real House Price Index

27 Pages Posted: 2 May 2014

See all articles by Vasilios Plakandaras

Vasilios Plakandaras

Democritus University of Thrace

Rangan Gupta

University of Pretoria - Department of Economics

Periklis Gogas

Democritus University of Thrace - Department of Economics

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace

Date Written: April 30, 2014

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.

Keywords: house prices, forecasting, machine learning, Support Vector Regression

JEL Classification: C32, C53, R31

Suggested Citation

Plakandaras, Vasilios and Gupta, Rangan and Gogas, Periklis and Papadimitriou, Theophilos, Forecasting the U.S. Real House Price Index (April 30, 2014). Available at SSRN: https://ssrn.com/abstract=2431627 or http://dx.doi.org/10.2139/ssrn.2431627

Vasilios Plakandaras

Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

Rangan Gupta

University of Pretoria - Department of Economics ( email )

Lynnwood Road
Hillcrest
Pretoria, 0002
South Africa

Periklis Gogas (Contact Author)

Democritus University of Thrace - Department of Economics ( email )

Komotini, 69100
Greece

HOME PAGE: http://www.econ.duth.gr/personel/dep/gkogkas/index.en.shtml

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

HOME PAGE: http://econ.duth.gr/author/papadimi/

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