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

Author

Listed:
  • Pami Dua

    (University of Connecticut)

  • Stephen M. Miller

    (University of Connecticut)

  • David J. Smyth

    (Louisiana State University)

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.

Suggested Citation

  • Pami Dua & Stephen M. Miller & David J. Smyth, 1996. "Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework," Working papers 1996-08, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:1996-08
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    File URL: https://media.economics.uconn.edu/working/1996-08.pdf
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    References listed on IDEAS

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    12. Dua, Pami & Miller, Stephen M, 1996. "Forecasting Connecticut Home Sales in a BVAR Framework Using Coincident and Leading Indexes," The Journal of Real Estate Finance and Economics, Springer, vol. 13(3), pages 219-235, November.
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    Cited by:

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    2. Lozano, Francisco-Javier, 2013. "Evaluación de modelos de predicción para la venta de viviendas [Evaluation of forecasting models for house sales]," MPRA Paper 118652, University Library of Munich, Germany.

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