Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework
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 of creation:||Nov 1996|
|Publication status:||Published in Journal of Real Estate Finance and Economics, March 1999|
|Contact details of provider:|| Postal: University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063|
Phone: (860) 486-4889
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Web page: http://www.econ.uconn.edu/
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