Economics at your fingertips  

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

Pami Dua (), Stephen Miller () and David J. Smyth
Additional contact information
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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Published in Journal of Real Estate Finance and Economics, March 1999

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Working papers from University of Connecticut, Department of Economics University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063. Contact information at EDIRC.
Bibliographic data for series maintained by Mark McConnel ().

Page updated 2019-11-06
Handle: RePEc:uct:uconnp:1996-08