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Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment

  • Rangan Gupta

    ()

    (Department of Economics, University of Pretoria)

This paper considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated based on dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides, the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare one- to twenty four-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:1-2009:3, based on an insample of 1968:2-1994:12, relative to a random walk model and a small-scale VAR model comprising of just the five real house price growth rates. In addition to the forecast comparison exercise across large- and small-scale models, we also look at the ability of the “optimal” model (i.e., the model that produces the minimum average mean squared forecast error (MSFE)) for a specific region, in predicting ex ante real house prices (in levels) over the period of 2009:4 till 2012:2. Factor-based models (classical or Bayesian) performs the best for the North East, Mid- West, West census regions and the aggregate US economy, and equally as well to a small-scale VAR for the South region. The “optimal” factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.

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Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201214.

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Length: 28 pages
Date of creation: Apr 2012
Date of revision:
Handle: RePEc:pre:wpaper:201214
Contact details of provider: Postal: PRETORIA, 0002
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Web page: http://www.up.ac.za/economics

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  1. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
  2. Rangan Gupta & Alan Kabundi & Stephen M. Miller, 2010. "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.
  3. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
  4. Rangan Gupta & Alain Kabundi, 2010. "Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 168-185.
  5. Rangan Gupta & Faaiqa Hartley, 2013. "The Role of Asset Prices in Forecasting Inflation and Output in South Africa," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 12(3), pages 239-291, December.
  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. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," Working Paper 96-13, Federal Reserve Bank of Atlanta.
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  10. Chris Bloor & Troy Matheson, 2010. "Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand," Empirical Economics, Springer, vol. 39(2), pages 537-558, October.
  11. Rangan Gupta & Stephen M. Miller & Dylan van Wyk, 2010. "Financial Market Liberalization, Monetary Policy, and Housing Price Dynamics," Working papers 2010-06, University of Connecticut, Department of Economics.
  12. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2005. "The generalised dynamic factor model: one sided estimation and forecasting," ULB Institutional Repository 2013/10129, ULB -- Universite Libre de Bruxelles.
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  14. Sonali Das & Rangan Gupta & Alain Kabundi, 2009. "The Blessing Of Dimensionality In Forecasting Real House Price Growth In The Nine Census Divisions Of The Us," Working Papers 200902, University of Pretoria, Department of Economics.
  15. Sonali Das & Rangan Gupta & Alain Kabundi, 2008. "Could We Have Predicted The Recent Downturn In The South African Housing Market?," Working Papers 200831, University of Pretoria, Department of Economics.
  16. Sei-Wan Kim & Radha Bhattacharya, 2009. "Regional Housing Prices in the USA: An Empirical Investigation of Nonlinearity," The Journal of Real Estate Finance and Economics, Springer, vol. 38(4), pages 443-460, May.
  17. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
  18. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
  19. 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.
  20. Gerald Carlino & Robert DeFina, 1997. "The differential regional effects of monetary policy: evidence from the U.S. States," Working Papers 97-12, Federal Reserve Bank of Philadelphia.
  21. Marc Hallin & Mario Forni & Marco Lippi & Lucrezia Reichlin, 2003. "Do financial variables help forecasting inflation and real activity in the Euro area ?," ULB Institutional Repository 2013/2123, ULB -- Universite Libre de Bruxelles.
  22. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
  23. 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.
  24. Bernanke, Ben & Gertler, Mark, 1995. "Inside the Black Box: The Credit Channel of Monetary Policy Transmission," Working Papers 95-15, C.V. Starr Center for Applied Economics, New York University.
  25. Thomas Hyclak & Geraint Johnes, 1999. "original: House prices and regional labor markets," The Annals of Regional Science, Springer, vol. 33(1), pages 33-49.
  26. Rangan Gupta & Stephen M. Miller, 2009. "The Time-Series Properties on Housing Prices: A Case Study of the Southern California Market," Working papers 2009-10, University of Connecticut, Department of Economics, revised Dec 2009.
  27. Rangan Gupta & Alain Kabundi, 2009. "The Effect Of Monetary Policy On House Price Inflation: A Factor Augmented Vector Autoregression (Favar) Approach," Working Papers 200903, University of Pretoria, Department of Economics.
  28. Carlos Vargas-Silva, 2007. "Monetary policy and the U.S. housing market: A VAR analysis imposing sign restrictions," Working Papers 0705, Sam Houston State University, Department of Economics and International Business.
  29. 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.
  30. Sonali Das & Rangan Gupta & Alain Kabundi, 2011. "Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(2), pages 288-302, March.
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  33. Gerald Carlino & Robert Defina, 1998. "The Differential Regional Effects Of Monetary Policy," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 572-587, November.
  34. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
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