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The Time-Series Properties of House Prices: A Case Study of the Southern California Market

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  • Rangan Gupta
  • Stephen Miller

Abstract

We examine the time-series relationship between house prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the house price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend links the house prices in these eight MSAs, a purchasing power parity finding for the house prices in Southern California. Second, we perform temporal Granger causality tests revealing intertwined temporal relationships. The Santa Anna MSA leads the pack in temporally causing house prices in six of the other seven MSAs, excluding only the San Luis Obispo MSA. The Oxnard MSA experienced the largest number of temporal effects from other MSAs, six of the seven, excluding only Los Angeles. The Santa Barbara MSA proved the most isolated in that it temporally caused house prices in only two other MSAs (Los Angeles and Oxnard) and house prices in the Santa Anna MSA temporally caused prices in Santa Barbara. Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive (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 MSAs. Finally, we consider the ability of theses time-series models to provide accurate out-of-sample predictions of the decline in the house prices after their peaks in 2005 and 2006. Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts of timing of the peak and decline if the house prices.
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  • 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.
    • Handle: RePEc:kap:jrefec:v:44:y:2012:i:3:p:339-361
    DOI: 10.1007/s11146-010-9234-7
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    1. Charles Himmelberg & Christopher Mayer & Todd Sinai, 2005. "Assessing High House Prices: Bubbles, Fundamentals and Misperceptions," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 67-92, Fall.
    2. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 8(Fall).
    3. Todd Kuethe & Valerien Pede, 2011. "Regional Housing Price Cycles: A Spatio-temporal Analysis Using US State-level Data," Regional Studies, Taylor & Francis Journals, vol. 45(5), pages 563-574.
    4. Rangan Gupta, 2006. "FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs," South African Journal of Economics, Economic Society of South Africa, vol. 74(4), pages 611-628, December.
    5. MacKinnon, James G & Haug, Alfred A & Michelis, Leo, 1999. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 563-577, Sept.-Oct.
    6. David A. Dickey & Dennis W. Jansen & Daniel L. Thornton, 1994. "A Primer on Cointegration with an Application to Money and Income," Palgrave Macmillan Books, in: B. Bhaskara Rao (ed.), Cointegration, chapter 2, pages 9-45, Palgrave Macmillan.
    7. 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.
    8. Rangan Gupta & Stephen Miller, 2012. "“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(3), pages 763-782, June.
    9. Rapach, David E. & Strauss, Jack K., 2009. "Differences in housing price forecastability across US states," International Journal of Forecasting, Elsevier, vol. 25(2), pages 351-372.
    10. Mark J. Holmes & Arthur Grimes, 2008. "Is There Long-run Convergence among Regional House Prices in the UK?," Urban Studies, Urban Studies Journal Limited, vol. 45(8), pages 1531-1544, July.
    11. Rangan Gupta & Moses M. Sichei, 2006. "A Bvar Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 74(3), pages 391-409, September.
    12. James P. LeSage & Zheng Pan, 1995. "Using Spatial Contiguity as Bayesian Prior Information in Regional Forecasting Models," International Regional Science Review, , vol. 18(1), pages 33-53, January.
    13. Clapp, John M. & Tirtiroglu, Dogan, 1994. "Positive feedback trading and diffusion of asset price changes: Evidence from housing transactions," Journal of Economic Behavior & Organization, Elsevier, vol. 24(3), pages 337-355, August.
    14. Robert J. Shiller, 2007. "Understanding recent trends in house prices and homeownership," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 89-123.
    15. Jonathan McCarthy & Richard Peach, 2004. "Are home prices the next \\"bubble\\"?," Economic Policy Review, Federal Reserve Bank of New York, issue Dec, pages 1-17.
    16. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    17. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    18. Stiglitz, Joseph E, 1990. "Symposium on Bubbles," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 13-18, Spring.
    19. Dua, Pami & Miller, Stephen M & Smyth, David J, 1999. "Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework," The Journal of Real Estate Finance and Economics, Springer, vol. 18(2), pages 191-205, March.
    20. Karl E. Case & Robert J. Shiller, 2003. "Is There a Bubble in the Housing Market?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 34(2), pages 299-362.
    21. Granger, Clive W J, 1986. "Developments in the Study of Cointegrated Economic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 213-228, August.
    22. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    23. Stuart A. Gabriel & Joe P. Mattey & William L. Wascher, 1999. "House price differentials and dynamics: evidence from the Los Angeles and San Francisco metropolitan areas," Economic Review, Federal Reserve Bank of San Francisco, pages 3-22.
    24. Tirtiroglu, Dogan, 1992. "Efficiency in housing markets: Temporal and spatial dimensions," Journal of Housing Economics, Elsevier, vol. 2(3), pages 276-292, September.
    25. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    26. Todd H. Kuethe & Valerien Pede, 2009. "Regional Housing Price Cycles: A Spatio-Temporal Analysis Using Us State Level," Working Papers 09-04, Purdue University, College of Agriculture, Department of Agricultural Economics.
    27. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    28. Steven Cook, 2005. "Detecting long-run relationships in regional house prices in the UK," International Review of Applied Economics, Taylor & Francis Journals, vol. 19(1), pages 107-118.
    29. Mark J. Holmes & Arthur Grimes, 2005. "Is there long-run convergence of regional house prices in the UK?," Working Papers 05_11, Motu Economic and Public Policy Research.
    30. Dua, Pami & Miller, Stephen M, 1996. "Forecasting Connecticut Home Sales in a BVAR Framework Using Coincident and Leading Indexes," The Journal of Real Estate Finance and Economics, Springer, vol. 13(3), pages 219-235, November.
    31. Pami Dua & Stephen M. Miller & David J. Smyth, 1996. "Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework," Working papers 1996-08, University of Connecticut, Department of Economics.
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    More about this item

    Keywords

    House prices; Cointegration; Temporal causality; Forecasting;
    All these keywords.

    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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