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"Ripple Effects" and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix

Author Info

Listed author(s):
  • Rangan Gupta

    (University of Pretoria)

  • Stephen M. Miller

    (University of Connecticut and University of Nevada, Las Vegas)

Abstract

We examine the time-series relationship between housing prices in Los Angeles, Las Vegas, and Phoenix. First, temporal Granger causality tests reveal that Los Angeles housing prices cause housing prices in Las Vegas (directly) and Phoenix (indirectly). In addition, Las Vegas housing prices cause housing prices in Phoenix. Los Angeles housing prices prove exogenous in a temporal sense and Phoenix housing prices do not cause prices in the other two markets. Second, we calculate out-of-sample forecasts in each market, using various vector autoregessive (VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different cities. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of turning points.

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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2009-05.

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Length: 38 pages
Date of creation: Jan 2009
Date of revision: Jun 2009
Publication status: Published in Annals of Regional Science, available on-line November 18, 2010
Handle: RePEc:uct:uconnp:2009-05
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University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063

Phone: (860) 486-4889
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Web page: http://www.econ.uconn.edu/

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Keywords: Ripple effect; housing prices; forecasting;

Other versions of this item:

Find related papers by JEL classification:
  • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
  • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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  11. 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.
  12. Perron, Pierre & Rodriguez, Gabriel, 2003. "GLS detrending, efficient unit root tests and structural change," Journal of Econometrics, Elsevier, vol. 115(1), pages 1-27, July.
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  17. 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.
  18. Omer Ozcicek & W. DOUGLAS McMILLIN, 1999. "Lag length selection in vector autoregressive models: symmetric and asymmetric lags," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 517-524.
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  20. Sims, Christopher A, 1987. "Vector Autoregressions and Reality: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 443-449, October.
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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Pittsburgh, phoenix
    by ryan in The bellows on 2009-02-03 19:31:56
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