Predicting Downturns in the US Housing Market: A Bayesian Approach

Posted: 25 May 2010

See all articles by Rangan Gupta

Rangan Gupta

University of Pretoria - Department of Economics

Sonali Das

CSIR

Date Written: May 25, 2010

Abstract

This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1 to 1994:Q4; and then forecasts one-to-four quarters-ahead real house price growth over the out-of-sample horizon of 1995:Q1 to 2006:Q4. The forecasts are evaluated by comparing them with those from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant of the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth over the recent period of 2007:Q1 to 2008:Q1. The results show that the BVARs, in whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in predicting the downturn in 18 of the 19 states.

Keywords: BVAR model, BVAR forecasts, forecast accuracy, SBVAR model, SBVAR forecasts, VAR model, VAR forecasts

JEL Classification: E17, E27, E37, E47

Suggested Citation

Gupta, Rangan and Das, Sonali, Predicting Downturns in the US Housing Market: A Bayesian Approach (May 25, 2010). Journal of Real Estate Finance and Economics, Vol. 41, No. 3, 2010, Available at SSRN: https://ssrn.com/abstract=1615686

Rangan Gupta (Contact Author)

University of Pretoria - Department of Economics ( email )

Lynnwood Road
Hillcrest
Pretoria, 0002
South Africa

Sonali Das

CSIR ( email )

P. O. Box 395
Brummeria
Pretoria, 0001
South Africa

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