Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals

36 Pages Posted: 29 Dec 2009 Last revised: 8 Apr 2012

See all articles by Rangan Gupta

Rangan Gupta

University of Pretoria - Department of Economics

Alain Kabundi

University of Johannesburg - Department of Economics

Stephen M. Miller

University of Nevada, Las Vegas - Department of Economics; University of Connecticut - Department of Economics

Date Written: December 28, 2009

Abstract

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its turning point in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets – extracting common factors (principle components) in a Factor-Augmented Vector Autoregressive or Factor-Augmented Bayesian Vector Autoregressive models or Bayesian shrinkage in a large-scale Bayesian Vector Autoregressive models. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). Finally, we use each model to forecast the turning point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a turning point with any accuracy, suggesting that attention to developing forward-looking microfounded dynamic stochastic general equilibrium models of the housing market, over and above fundamentals, proves crucial in forecasting turning points.

Keywords: US House prices, Forecasting, DSGE models, Factor Augmented Models, Large-Scale BVAR models

JEL Classification: C32, R31

Suggested Citation

Gupta, Rangan and Kabundi, Alain and Miller, Stephen M., Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals (December 28, 2009). Economic Modelling, July 2011, Available at SSRN: https://ssrn.com/abstract=1529020

Rangan Gupta

University of Pretoria - Department of Economics ( email )

Lynnwood Road
Hillcrest
Pretoria, 0002
South Africa

Alain Kabundi

University of Johannesburg - Department of Economics ( email )

P.O. Box 524
Auckland Park 2006, Johannesburg
South Africa

Stephen M. Miller (Contact Author)

University of Nevada, Las Vegas - Department of Economics ( email )

4505 S. Maryland Parkway
Box 456005
Las Vegas, NV 89154
United States
702-895-3776 (Phone)
702-895-1354 (Fax)

HOME PAGE: http://faculty.unlv.edu/smiller/

University of Connecticut - Department of Economics

365 Fairfield Way, U-1063
Storrs, CT 06269-1063
United States

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