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Unit Roots and Structural Change: An Application to US House-Price Indices

Author

Listed:
  • Giorgio Canarella

    (California State University, Los Angeles, and University of Nevada, Las Vegas)

  • Stephen M. Miller

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

  • Stephen K. Pollard

    (California State University, Los Angeles)

Abstract

This paper addresses two issues. First, we employ unit-root tests that allow for two endogenous breaks as suggested by Lumdaine and Papell (1997) and, more recently, Lee and Strazicich (2003) to investigate the integration properties of the returns on the S&P/Case-Shiller Home Price Indices. The findings of the tests that assume structural stability provide no evidence against the unit-root hypothesis in all returns series. Conversely, the Lumdaine-Papell and Lee-Strazicich tests indicate that significant structural breaks exist in the US housing market. Only the Lee-Strazicich test, however, which incorporates structural changes under the null hypothesis, finds that the returns to houses exhibit trend stationarity with structural breaks, in most cases, rather than a random walk. Second, we apply these tests to analyze what UK researchers call the "ripple effect" in the British housing markets. Following Meen (1999), we investigate the stationarity of the metropolitan house-price ratios. The findings of the Lumsdaine-Papell test provide no evidence against the unit-root hypothesis in all house-price ratio series. Conversely, the Lee-Strazicich test finds broken-trend stationarity of the metropolitan house-price ratios for Boston, Miami, and New York. This provides limited evidence that some ripple effects do indeed exists in the US housing market.

Suggested Citation

  • 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.
  • Handle: RePEc:uct:uconnp:2010-04
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    References listed on IDEAS

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    Cited by:

    1. Christina Christou & Rangan Gupta & Wendy Nyakabawo & Mark E. Wohar, 2017. "Do House Prices Hedge Inflation in the US? A Quantile Cointegration Approach," Working Papers 201707, University of Pretoria, Department of Economics.
    2. Rui Lin & Xin Zhang & Xiuting Li & Jichang Dong, 2015. "Heterogeneous convergence of regional house prices and the complexity in China," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics, vol. 33(2), pages 325-348.
    3. Aye, G.C. & Goswami, S. & Gupta, R., 2013. "Metropolitan House Prices In Regions of India: Do They Converge?," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 13(1), pages 135-144.
    4. Zhang Jing & de Jong Robert & Haurin Donald, 2016. "Are US real house prices stationary? New evidence from univariate and panel data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 1-18, February.
    5. 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.
    6. Tsangyao Chang & Tsung-Pao Wu & Rangan Gupta, 2015. "Are house prices in South Africa really nonstationary? Evidence from SPSM-based panel KSS test with a Fourier function," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 32-53, January.
    7. Ming-Chu Chiang & I-Chun Tsai, 2016. "Ripple effect and contagious effect in the US regional housing markets," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 55-82, January.
    8. Goodness C. Aye & Samrat Goswami & Rangan Gupta, 2012. "Metropolitan House Prices In India: Do They Converge?," Working Papers 201220, University of Pretoria, Department of Economics.
    9. Yunlong Gong & Peter Boelhouwer & Jan de Haan, 2014. "Spatial Dependence in House Prices: Evidence from China's Interurban Housing Market," ERSA conference papers ersa14p448, European Regional Science Association.
    10. Montañés, Antonio & Olmos, Lorena & Reyes, Marcelo, 2016. "Does crisis affect convergence process? The case of the Spanish provinces," MPRA Paper 69543, University Library of Munich, Germany.
    11. repec:spr:soinre:v:133:y:2017:i:1:d:10.1007_s11205-016-1357-7 is not listed on IDEAS
    12. Tuck Cheong Tang & Pei Pei Tan, 2015. "Real Interest Rate and House Prices in Malaysia: An Empirical Stud," Economics Bulletin, AccessEcon, vol. 35(1), pages 270-275.
    13. William Miles, 2015. "Bubbles, Busts and Breaks in UK Housing," International Real Estate Review, Asian Real Estate Society, vol. 18(4), pages 455-471.
    14. Montañés, A. & Olmos, L., 2013. "Convergence in US house prices," Economics Letters, Elsevier, vol. 121(2), pages 152-155.
    15. Alfred Larm Teye & Daniel Felix Ahelegbey, 2017. "Spatial and Temporal House Price Diffusion in the Netherlands: A Bayesian Network Approach," ERES eres2017_337, European Real Estate Society (ERES).
    16. Nicholas Apergis & Tsangyao Chang & Christina Christou & Rangan Gupta, 2017. "Convergence of Health Care Expenditures Across the US States: A Reconsideration," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 303-316, August.
    17. repec:kap:jrefec:v:54:y:2017:i:3:d:10.1007_s11146-016-9581-0 is not listed on IDEAS
    18. Mei-Se Chien, 2013. "The Non-linear Ripple Effect of Housing Prices in Taiwan: A Smooth Transition Regressive Model," ERES eres2013_51, European Real Estate Society (ERES).
    19. Francisca Richter & Youngme Seo, 2011. "Inter-regional home price dynamics through the foreclosure crisis," Working Paper 1119, Federal Reserve Bank of Cleveland.

    More about this item

    Keywords

    House-price indexes; Time-series properties; Ripple effects;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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