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

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  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Stephen M. Miller

    ()
    (Department of Economics, 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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File URL: http://web.unlv.edu/projects/RePEc/pdf/0902.pdf
File Function: First version, 2009
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Bibliographic Info

Paper provided by University of Nevada, Las Vegas , Department of Economics in its series Working Papers with number 0902.

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Length: 37 pages
Date of creation: Jan 2009
Date of revision:
Handle: RePEc:nlv:wpaper:0902

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Keywords: Ripple effect; Housing prices; Forecasting;

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  1. Pittsburgh, phoenix
    by ryan in The bellows on 2009-02-03 19:31:56
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Cited by:
  1. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2010. "Unit Roots and Structural Change: An Application to US House-Price Indices," Working papers 2010-04, University of Connecticut, Department of Economics, revised Dec 2010.
  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. Payne, James E., 2012. "The Long-Run Relationship among Regional Housing Prices: An Empirical Analysis of the U.S," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 42(1).
  4. Balcilar, Mehmet & Gupta, Rangan & Shah, Zahra B., 2011. "An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa," Economic Modelling, Elsevier, vol. 28(3), pages 891-899, May.
  5. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals," Working papers 2009-42, University of Connecticut, Department of Economics.
  6. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods and Applications, Springer, vol. 23(2), pages 229-264, June.
  7. Mark J. Holmes & Theodore Panagiotidis & Jesus Otero, 2011. "Investigating Regional House Price Convergence in the United States: Evidence from a pair-wise approach," Discussion Paper Series 2011_12, Department of Economics, University of Macedonia, revised Jun 2011.
  8. Eli Beracha & Hilla Skiba, 2011. "Momentum in Residential Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 43(3), pages 299-320, October.
  9. Rangan Gupta & Stephen M. Miller, 2009. "The Time-Series Properties of House Prices: A Case Study of the Southern California Market," Working Papers 0912, University of Nevada, Las Vegas , Department of Economics, revised Dec 2009.

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