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

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Author Info

  • 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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Bibliographic Info

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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Web page: http://www.econ.uconn.edu/
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Keywords: Ripple effect; housing prices; forecasting;

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References

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Blog mentions

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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. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
  2. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working papers 2011-02, University of Connecticut, Department of Economics, revised Aug 2012.
  3. 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.
  4. Mark J. Holmes & Jesús Otero & Theodore Panagiotidis, 2011. "Investigating Regional House Price Convergence in the United States: Evidence from a Pair-Wise Approach," Working Paper Series 29_11, The Rimini Centre for Economic Analysis.
  5. Mehmet Balcilar & Rangan Gupta & Zahra Shah, 2010. "An In-Sample and Out-of-Sample Empirical Investigation of the Nonlinearity in House Prices of South Africa," Working Papers 201008, University of Pretoria, Department of Economics.
  6. 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.
  7. 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).
  8. Rangan Gupta & Stephen Miller, 2012. "The Time-Series Properties of House Prices: A Case Study of the Southern California Market," The Journal of Real Estate Finance and Economics, Springer, vol. 44(3), pages 339-361, April.
  9. 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.

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