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Predicting Downturns in the US Housing Market: A Bayesian Approach

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  • Rangan Gupta

    ()

  • Sonali Das

    ()

Abstract

This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate), for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1 to 1994:Q4; and then forecasts one-to-four quarters ahead real house price growth over the out-of-sample horizon of 1995:Q1 to 2006:Q4. The forecasts are then evaluated by comparing them with the ones generated from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate variant the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict the downturns in the real house price growth over the recent period of 2007:Q1 to 2008:Q1. The results show that the BVARs, in whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in predicting the downturn in 18 of the 19 states, however, they always under-predict the size of the decline in the real house price growth rate – an indication of the need to incorporate the role of fundamentals in the models.

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Bibliographic Info

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

Volume (Year): 41 (2010)
Issue (Month): 3 (October)
Pages: 294-319

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Handle: RePEc:kap:jrefec:v:41:y:2010:i:3:p:294-319

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

Related research

Keywords: BVAR model; BVAR forecasts; Forecast accuracy; SBVAR model; SBVAR forecasts; VAR model; VAR forecasts; E17; E27; E37; E47;

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References

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  1. Anirvan Banerji & Pami Dua & Stephen M. Miller, 2002. "Performance Evaluation of the New Connecticut Leading Employment Index Using Lead Profiles and BVAR Models," Working papers 2002-34, University of Connecticut, Department of Economics, revised Jun 2005.
  2. Guangling (Dave) Liu & Rangan Gupta, 2006. "A Small-Scale DSGE Model for Forecasting the South African Economy," Working Papers 200621, University of Pretoria, Department of Economics.
  3. Samuel Zita & Rangan Gupta, 2008. "Modeling and Forecasting the Metical-Rand Exchange Rate," The IUP Journal of Monetary Economics, IUP Publications, vol. 0(4), pages 63-90, November.
  4. Rangan Gupta & Moses M. Sichei, 2006. "A BVAR Model for the South African Economy," Working Papers 200612, University of Pretoria, Department of Economics.
  5. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  6. David E. Rapach & Jack K. Strauss, 2007. "Forecasting real housing price growth in the Eighth District states," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 33-42.
  7. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
  8. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
  9. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods of Forecasting House Prices in Six Metropolitan Areas of South Africa," Working Papers 200813, University of Pretoria, Department of Economics.
  10. Guangling ‘Dave’ Liu & Rangan Gupta & Eric Schaling, 2007. "Forecasting the South African Economy: A DSGE-VAR Approach," Working Papers 51, Economic Research Southern Africa.
  11. Marco Del Negro, 2001. "Turn, turn, turn: Predicting turning points in economic activity," Economic Review, Federal Reserve Bank of Atlanta, issue Q2, pages 1-12.
  12. Rangan Gupta, 2006. "Forecasting the South African Economy with VARs and VECMs," Working Papers 200618, University of Pretoria, Department of Economics.
  13. Rangan Gupta, 2007. "Forecasting the South African Economy with Gibbs Sampled BVECMs," Working Papers 200701, University of Pretoria, Department of Economics.
  14. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January.
  15. Gupta, Rangan & Kabundi, Alain, 2011. "Forecasting Macroeconomic Variables Using Large Datasets: Dynamic Factor Model versus Large-Scale BVARs," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 46(1), pages 23-40.
  16. 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.
  17. Hossain Amirizadeh & Richard M. Todd, 1984. "More growth ahead for Ninth District states," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  18. Guangling (Dave) Liu & Rangan Gupta & Eric Schaling, 2008. "A New-Keynesian DSGE Model for Forecasting the South African Economy," Working Papers 200805, University of Pretoria, Department of Economics.
  19. William C. Gruben & Donald W. Hayes, 1991. "Forecasting the Louisiana economy," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Mar, pages 1-16.
  20. Rangan Gupta, 2009. "Bayesian Methods Of Forecasting Inventory Investment," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 113-126, 03.
  21. Rangan Gupta & Alain Kabundi, 2008. "A Dynamic Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 200815, University of Pretoria, Department of Economics.
  22. Anatoli Kuprianov & William Lupoletti, 1984. "The economic outlook for the Fifth District states in 1984 : forecasts from vector autoregression models," Economic Review, Federal Reserve Bank of Richmond, issue Jan, pages 12-23.
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Citations

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Cited by:
  1. Huang, MeiChi, 2014. "Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 2-16.
  2. Mirriam Chitalu Chama-Chiliba & Rangan Gupta & Nonophile Nkambule & Naomi Tlotlego, 2011. "Forecasting Key Macroeconomic Variables of the South African Economy Using Bayesian Variable Selection," Working Papers 201132, University of Pretoria, Department of Economics.
  3. Mehmet Balcilar & Rangan Gupta & Zahra Shah, 2010. "An In-Sample and Out-of-Sample Empirical Investigation of the Nonlinearity in House Prices of South Africa," Working Papers 201008, University of Pretoria, Department of Economics.
  4. 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.
  5. Rangan Gupta, 2012. "Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment," Working Papers 201214, University of Pretoria, Department of Economics.
  6. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.
  7. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2014. "A Two Stage Approach to Spatiotemporal Analysis with Strong and Weak Cross-Sectional Dependence," CESifo Working Paper Series 4592, CESifo Group Munich.
  8. Zietz, Joachim & Traian, Anca, 2014. "When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 271-281.
  9. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2012. "The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US," Working papers 2012-12, University of Connecticut, Department of Economics.

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