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Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

Pami Dua (), Stephen Miller () and David J. Smyth
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David J. Smyth: Louisiana State University

No 1996-08, Working papers from University of Connecticut, Department of Economics

Abstract: This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.

Date: 1996-11
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Published in Journal of Real Estate Finance and Economics, March 1999

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