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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

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Bibliographic Info

Article provided by Springer in its journal Journal of Real Estate Finance & Economics.

Volume (Year): 18 (1999)
Issue (Month): 2 (March)
Pages: 191-205

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Handle: RePEc:kap:jrefec:v:18:y:1999:i:2:p:191-205

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Web page: http://www.springerlink.com/link.asp?id=102945

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Cited by:
  1. Sonali Das & Rangan Gupta & Alain Kabundi, 2009. "Could we have predicted the recent downturn in the South African Housing Market?," Working Papers 149, Economic Research Southern Africa.
  2. 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.
  3. Rangan Gupta & Alan Kabundi & Stephen M. Miller, 2009. "Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals," Working Papers 1001, University of Nevada, Las Vegas , Department of Economics.
  4. 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.
  5. 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.
  6. Rangan Gupta & Sonali Das, 2008. "Predicting Downturns in the US Housing Market: A Bayesian Approach," Working Papers 200821, University of Pretoria, Department of Economics.
  7. 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 2009-13, University of Connecticut, Department of Economics.

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