Abstract
This paper employs a Component GARCH in Mean model to show that house prices across a number of major US cities between 1987 and 2009 have displayed asset market properties in terms of both risk-return relationships and asymmetric adjustment to shocks. In addition, tests for structural breaks in the mean and variance indicate structural instability across the data range. Multiple breaks are identified across all cities, particularly for the early 1990s and during the post-2007 financial crisis as housing has become an increasingly risky asset. Estimating the models over the individual sub-samples suggests that over the last 20 years the financial sector has increasingly failed to account for the levels of risk associated with real estate markets. This result has possible implications for the way in which financial institutions should be regulated in the future.
Notes
When employing this approach, often used with asset market based studies (Timmermann 2001; Granger and Hyung 2004), specific explanations for identified breaks are rarely provided. This is due to the nature of asset markets, where the market will often react to changes in policy or the economy long before they are implemented or even announced. This makes it difficult to attribute breaks to specific events. In addition, breaks in asset markets can typically occur due to bubbles or swings in investor perceptions, Hall et al. (1997) use this as a general explanation for shifts in regime in their study of housing markets. The alternative approach would involve specifying a particular break based on specific policy changes as originally used by Enders (1988). It is important in this literature, as elsewhere, to avoid spurious interpretation of breaks, which are likely to be the result of complex interactions of effects and not susceptible to simple analysis.
As an alternative the more commonly used Threshold GARCH-M (Glosten et al. 1993) model was also employed to test for positive risk-return trade-off and asymmetry, with broadly similar results. We could also have used a multivariate form of GARCH, but the substantial differences in how the models perform in different cities with respect to the structural breaks and CGARCH specification made this impossible.
This is a standard mean equation in nominal form, as in comparable tests on other assets, as used by Glosten et al. (1993), although other studies have incorporated ARIMA type models and the interest rate in the mean. However, this is beyond the scope of this study.
Note that although it is not done in this paper, it is relatively trivial to condition on observables—in the simplest case by nominating the ‘official’ or ‘widely accepted’ breakdates of each series.
For example, the IT is found to be the most sensitive to the existence of volatility breaks for independent and identically distributed data, but suffers severe size distortions for strongly dependent data or for non-mesokurtic distributions. In contrast, the KL and the SAC2 variants do not exhibit size distortions in these cases but their power is smaller, while SAC1 does not exhibit size distortions for non-mesokurtic data and although it does for strongly dependent data, its power is higher than KL and SAC2.
For example, a selection rule could suggest that a breakpoint can be considered only if two tests have identified it; or a breakpoint can be considered only if the resulting segments contain more than 10 observations.
Therefore, they provide the same value even if the observations of each segment are randomly ordered. In contrast, statistics that are based on sequential methods (such as the CUSUM tests) are influenced by the order of the observations.
See Karoglou (2010) for a discussion of non-normality and the presence of structural breaks as well as a more detailed discussion of some of the implications of this type of approach to finding breaks in the variance of a series.
A composite house price index covering the main US cities was also estimated, however there was no evidence of a significant risk-return trade-off or asymmetric adjustment (the results are available from the authors on request). This result reflects the varying nature of risk and the housing market across the US, and that in a composite form the effects tend to cancel each other out. This supports a similar result to Case and Shiller (1989), who found little evidence of any relationship between individual city prices and a composite index
References
Andreou, E., & Ghysels, E. (2002). Detecting multiple breaks in financial market volatility dynamics. Journal of Applied Econometrics, 17, 579–600.
Bond, S., Karolyi, G. A., & Saunders, A. (2003). International real estate returns: Multifactor, multicountry approach. Real Estate Economics, 31, 481–500.
Bos, T., & Hoontrakul, P. (2002). Estimation of mean and variance episodes in the price return of the stock exchange of Thailand. Research in International Business and Finance, 16, 535–554.
Brown, M., & Forsythe, A. (1974). Robust tests for the equality of variances. Journal of the American Statistical Association, 69, 364–367.
Cappoza, D. R., Hendershott, P. H., & Mack, C. (2004). An anatomy of price dynamics in illiquid markets: Analysis and evidence from local housing markets. Real Estate Economics, 32, 1–32.
Case, K. E., & Shiller, R. J. (1989). The efficiency of the market for single-family homes. The American Economic Review, 79, 125–137.
Case, K. E., Quigley, J. M., & Shiller, R. J. (2005). Comparing wealth effects: The stock market versus the housing market. Advances in Macroeconomics, 5, 1–32.
Chien, M. S. (2010). Structural breaks and the convergence of regional house prices. Journal of Real Estate Finance and Economics, 40, 77–88.
Den Haan, W. J., & Levin, A. (1998). Vector autoregressive covariance matrix estimation. Manuscript, University of California, San Diego, California
Dolde, W., & Tirtiroglue, D. (1997). Temporal and spatial information diffusion in real estate price changes and variances. Real Estate Economics, 25, 539–565.
Enders, W. (1988). ARIMA and cointegration tests of PPP under fixed and flexible exchange rate regimes. The Review of Economics and Statistics, 70, 504–508.
Engle, R. (2004). Risk and volatility: Econometric models and financial practice. The American Economic Review, 94, 405–420.
Engle, R. F., & Lee, G. G. J. (1999). A long-run and short-run component model of stock return volatility. In R. F. Engle & H. White (Eds.), Cointegration, causality and forecasting: A festschrift in honour of clive W. J. Granger (pp. 475–497). UK: Oxford University Press.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48, 1779–1801.
Granger, C. W. J., & Hyung, N. (2004). Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns. Journal of Empirical Finance, 11, 399–421.
Guirguis, H., Giannikos, C., & Anderson, R. (2005). The US housing market: Asset pricing forecasts using time varying coefficients. Journal of Real Estate Finance and Economics, 30, 33–53.
Hall, S., Psaradakis, Z., & Sola, M. (1997). Switching error-correction models of house prices in the United Kingdom. Economic Modelling, 14, 517–527.
Inclan, C., & Tiao, G. C. (1994). Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association, 89, 913–923.
Karoglou, M. (2006a). On the detection of structural changes in volatility dynamics with applications. Ph.D. Thesis, University of Leicester, Leicester
Karoglou, M. (2006b). The size and power of the CUSUM-type tests in detecting structural changes in financial markets volatility dynamics. Mimeograph: University of Leicester, Leicester.
Karoglou, M. (2010). Breaking down the non-normality of stock returns. European Journal of Finance, 16, 79–95.
Kim, C., & Nelson, C. (1999). Has the US economy become more stable? A Bayesian approach based on a Markov-switching model of the business cycle. The Review of Economics and Statistics, 81, 608–616.
Kokoszka, P., & Leipus, R. (1999). Testing for parameter changes in ARCH models. Lithuanian Mathematical Journal, 39, 182–195.
Levene, H. (1960). Robust tests for equality of variances. In I. Olkin (Ed.), Contributions to probability and statistics: Essays in Honor of Harold Hotelling (pp. 278–292). California: Stanford University Press.
Miles, W. (2008). Irreversibility, uncertainty and housing investment. Journal of Real Estate Finance and Economics, 36, 249–264.
Miller, N., & Peng, L. (2006). Exploring metropolitan housing price volatility. Journal of Real Estate Finance and Economics, 33, 5–18.
Sansó, A., Aragó, V., & Carrion-i-Silvestre, J. Ll. (2004). Testing for changes in the unconditional variance of financial time series. Revista de Economia Financeria, 4, 32–53.
Siegel, S., & Tukey, J. (1960). A nonparametric sum of ranks procedure for relative spread in unpaired samples. Journal of the American Statistical Association, 55, 429–445.
Sokal, R. R., & Rohlf, F. J. (1995). Biometry: The principles and practice of statistics in biological research. New York: W. H. Freeman and Co.
Sheskin, D. J. (2011). Handbook of parametric and nonparametric statistical procedures. London: Chapman and Hall/CRC.
Timmermann, A. (2001). Structural breaks, incomplete information, and stock prices. Journal of Business and Economic Statistics, 19, 299–314.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Karoglou, M., Morley, B. & Thomas, D. Risk and Structural Instability in US House Prices. J Real Estate Finan Econ 46, 424–436 (2013). https://doi.org/10.1007/s11146-011-9332-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11146-011-9332-1