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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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  2. Chris Bloor & Troy Matheson, 2008. "Analysing shock transmission in a data-rich environment: A large BVAR for New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2008/09, Reserve Bank of New Zealand.
  3. 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.
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  5. Matteo Iacoviello & Stefano Neri, 2007. "Housing Market Spillovers: Evidence from an Estimated DSGE Model," Boston College Working Papers in Economics 659, Boston College Department of Economics, revised 23 Oct 2009.
  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.
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  17. 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.
  18. 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.
  19. 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.
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  22. 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.
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  27. 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.
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  31. Rangan Gupta & Faaiqa Hartley, 2011. "The Role of Asset Prices in Forecasting Inflation and Output in South Africa," Working Papers 201115, University of Pretoria, Department of Economics.
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