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Forecasting real housing price growth in the Eighth District states

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Author Info

  • David E. Rapach
  • Jack K. Strauss

Abstract

The authors consider forecasting real housing price growth for the individual states of the Federal Reserve's Eighth District. They first analyze the forecasting ability of a large number of potential predictors of state real housing price growth using an autoregressive distributed lag (ARDL) model framework. A number of variables, including the state housing price-to-income ratio, state unemployment rate, and national inflation rate, appear to provide information that is useful for forecasting real housing price growth in many Eighth District states. Given that it is typically difficult to determine a priori the particular variable or small set of variables that are the most relevant for forecasting real housing price growth for a given state and time period, the authors also consider various methods for combining the individual ARDL model forecasts. They find that combination forecasts are quite helpful in generating accurate forecasts of real housing price growth in the individual Eighth District states.

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Bibliographic Info

Article provided by Federal Reserve Bank of St. Louis in its journal Regional Economic Development.

Volume (Year): (2007)
Issue (Month): Nov ()
Pages: 33-42

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Handle: RePEc:fip:fedlrd:y:2007:i:nov:p:33-42:n:v.3no.2

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Related research

Keywords: Housing - Prices ; Federal Reserve District; 8th;

References

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  1. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  2. Patric H. Hendershott & John C. Weicher, 2002. "Forecasting Housing Markets: Lessons Learned," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 30(1), pages 1-11.
  3. David E. Rapach & Jack K. Strauss, 2005. "Forecasting employment growth in Missouri with many potentially relevant predictors: an analysis of forecast combining methods," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 97-112.
  4. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers.
  5. Geraint Johnes & Thomas Hyclak, . "House Prices and Regional Labor Markets," Working Papers ec15/93, Department of Economics, University of Lancaster.
  6. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  7. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  8. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
  9. Alan Greenspan & James Kennedy, 2005. "Estimates of home mortgage originations, repayments, and debt on one-to-four-family residences," Finance and Economics Discussion Series 2005-41, Board of Governors of the Federal Reserve System (U.S.).
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Citations

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Cited by:
  1. Rangan Gupta & Stephen M. Miller, 2009. ""Ripple Effects" and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix," Working papers 2009-05, University of Connecticut, Department of Economics, revised Jun 2009.
  2. 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 0916, University of Nevada, Las Vegas , Department of Economics.
  3. Rangan Gupta & Marius Jurgilas & Alain Kabundi & Stephen M. Miller, 2009. "Monetary Policy and Housing Sector Dynamics in a Large-Scale Bayesian Vector Autoregressive Model," Working Papers 200913, University of Pretoria, Department of Economics.
  4. 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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