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Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework

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  • Dua, Pami
  • Miller, Stephen M
  • Smyth, David J

Abstract

This article uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting U.S. home sales. The benchmark Bayesian model includes home sales, price of homes, mortgage rate, real personal disposable income, and 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 U.S. home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators. Copyright 1999 by Kluwer Academic Publishers

Suggested Citation

  • Dua, Pami & Miller, Stephen M & Smyth, David J, 1999. "Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework," The Journal of Real Estate Finance and Economics, Springer, vol. 18(2), pages 191-205, March.
  • Handle: RePEc:kap:jrefec:v:18:y:1999:i:2:p:191-205
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    Cited by:

    1. Caraiani, Petre, 2010. "Forecasting Romanian GDP Using a BVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 76-87, December.
    2. Khaled Guesmi & Nabila BOUKEF JLASSI & Ahmed Atil & Imen Haouet, 2016. "On the Influence of Oil Prices on Financial Variables," Economics Bulletin, AccessEcon, vol. 36(4), pages 2261-2274.
    3. Pami Dua & Nishita Raje & Satyananda Sahoo, 2004. "Interest Rate Modeling and Forecasting in India," Occasional papers 3, Centre for Development Economics, Delhi School of Economics.
    4. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    5. Seoung Hwan Suh & Kabsung Kim, 2014. "Global financial crisis and early warning system of Korean housing market," Chapters,in: The Global Financial Crisis and Housing, chapter 4, pages 62-81 Edward Elgar Publishing.
    6. Rangan Gupta & Sonali Das, 2010. "Predicting Downturns in the US Housing Market: A Bayesian Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 41(3), pages 294-319, October.
    7. Das, Sonali & Gupta, Rangan & Kabundi, Alain, 2009. "Could we have predicted the recent downturn in the South African housing market?," Journal of Housing Economics, Elsevier, vol. 18(4), pages 325-335, December.
    8. Hossein Hassani & Zara Ghodsi & Rangan Gupta & Mawuli Segnon, 2017. "Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 83-97, January.
    9. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods Of Forecasting House Prices In Six Metropolitan Areas Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(2), pages 298-313, June.
    10. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States," Working Papers 200912, University of Pretoria, Department of Economics.
    11. Kincal, Gokce & Fullerton, Thomas M., Jr. & Holcomb, James H. & Barraza de Anda, Martha P., 2010. "Cross Border Business Cycle Impacts on the El Paso Housing Market," MPRA Paper 29095, University Library of Munich, Germany, revised 2010.

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