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

Author

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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.

Suggested Citation

  • Rangan Gupta & Stephen M. Miller, 2009. ""Ripple Effects” and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix," Working Papers 0902, University of Nevada, Las Vegas , Department of Economics.
  • Handle: RePEc:nlv:wpaper:0902
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    Cited by:

    1. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    2. Holmes, Mark J. & Otero, Jesús & Panagiotidis, Theodore, 2011. "Investigating regional house price convergence in the United States: Evidence from a pair-wise approach," Economic Modelling, Elsevier, vol. 28(6), pages 2369-2376.
    3. 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.
    4. 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.
    5. Carlos P. Barros & Luis A. Gil-Alana, 2013. "The Housing Markets in Spain and Portugal: Evidence of Persistence," Review of Economics & Finance, Better Advances Press, Canada, vol. 3, pages 19-32, November.
    6. 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 200912, University of Pretoria, Department of Economics.
    7. Rangan Gupta & Alain Kabundi, 2010. "The effect of monetary policy on house price inflation: A factor augmented vector autoregression (FAVAR) approach," Journal of Economic Studies, Emerald Group Publishing, vol. 37(6), pages 616-626, November.
    8. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2010. "Unit Roots and Structural Change: An Application to US House-Price Indices," Working Papers 1004, University of Nevada, Las Vegas , Department of Economics.
    9. Rangan Gupta & Christophe André & Luis Gil-Alana, 2015. "Comovement in Euro area housing prices: A fractional cointegration approach," Urban Studies, Urban Studies Journal Limited, vol. 52(16), pages 3123-3143, December.
    10. 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.
    11. 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.
    12. 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).
    13. Nissan, Edward & Payne, James E., 2013. "A Simple Test of σ-Convergence in U.S. Housing Prices across BEA Regions," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 43(2).
    14. Shu-hen Chiang, 2014. "Housing Markets in China and Policy Implications: Comovement or Ripple Effect," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 22(6), pages 103-120, November.
    15. Nicholas Apergis & Beatrice D. Simo-Kengne & Rangan Gupta, 2015. "Convergence In Provincial-Level South African House Prices: Evidence From The Club Convergence And Clustering Procedure," Review of Urban & Regional Development Studies, Wiley Blackwell, vol. 27(1), pages 2-17, March.
    16. repec:eee:regeco:v:65:y:2017:i:c:p:56-64 is not listed on IDEAS

    More about this item

    Keywords

    Ripple effect; Housing prices; Forecasting;

    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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