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Predictability and Persistence of the Price Movements of the S&P/Case-Shiller House Price Indices

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Abstract

This paper examines persistence in price movements and predictability of the US housing market both on a local level across 20 cities in the US and on a nationwide level. We use a time series approach instead of often applied multivariate approaches to exclude potential biases across local markets and provide trading strategies to compare predictability across markets and to test whether or not the detected persistence can be exploited by investors to earn excess returns. The results from the monthly and quarterly transaction-based S&P/Case-Shiller house price indices from 1987 to 2009 provide empirical evidence on strong persistence. This is confirmed by both parametric and non-parametric tests for nominal and real house prices based on expected inflation. Furthermore, the empirical findings suggest that investors might be able to obtain excess returns from both autocorrelation-based and moving average-based trading strategies compared to a buy-and-hold strategy, although the results depend on the transaction costs individual investors face.

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Notes

  1. A random walk process means that any shock to the index is permanent and that there is no tendency for the index level to return to a trend path over time. In contrast, if indices follow a mean-reverting process, there generally exists a tendency for the index level to return to its trend path over time, and investors may be able to forecast future index changes by using information on past price changes (Chaudhuri and Wu, 2003).

  2. See Standard and Poor’s (2009) for further information on index construction methodology.

  3. However, the monthly indices are moving averages of the actual month and the two preceding months. Thus, the results from short-term autocorrelation like autocorrelation of order one and two are highly influenced by this index construction methodology. This has to be considered when analyzing the results in “Empirical Results from Analyzing Persistence in Nominal Changes in S&P/CS HPI”. Furthermore, the variance ratio with lag interval q = 2 suffers from the same problem. As a robustness check, higher order autocorrelation and variance ratios with higher lag intervals are calculated as well.

  4. See Standard and Poor’s (2009). The data for the stock, the average value, and the aggregate value of single-family housing are reported for 2000 only, since the data are based on the decennial US Census.

  5. The descriptive statistics for the non-overlapping quarterly price changes of the S&P/CS HPI are presented in Table 13.

  6. The authors thank an anonymous referee for offering this valuable comment.

  7. In addition to the autoregressive model, we also specified a model using Hondrick-Prescott filters. However, the time series of expected inflation rates is smooth. Applying this time series for deflating house prices does not significantly change the characteristics of the time series of the nominal house prices, meaning that the statistical results from testing persistence do not differ much. For brevity, the results from the rational expectation formation model are not presented but available from the author upon request. In comparison to the AR model, the test statistics are similar and persistence is very high even if the strongest effect on deflating house prices and thus the lowest level of persistence in deflated house prices is expected.

  8. The difference of one month in the time span can be neglected and is not crucial for the performance.

  9. The two trading strategies are also conducted for inflation-adjusted data. However, the results do not mainly differ and are available from the authors upon request.

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Acknowledgements

We thank the editor J. B. Kau, an anonymous referee, Roland Füss, Tim-Alexander Kröncke, and Peter Westerheide for very constructive and helpful comments. All errors are ours.

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Authors and Affiliations

  1. Department of International Finance and Financial Management, Centre for European Economic Research (ZEW) – Mannheim, P.O. Box 10 34 43, 68034, Mannheim, Germany

    Felix Schindler

  2. Steinbeis University Berlin (SHB), Berlin, Germany

    Felix Schindler

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Correspondence to Felix Schindler.

Appendix

Appendix

Table 13 Descriptive statistics of quarterly changes in the S&P/CS HPIs
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Table 14 Autocorrelation of quarterly changes in the S&P/CS HPIs
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Table 15 Variance ratio estimates and variance ratio test statistics for quarterly changes in the S&P/CS HPIs
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Table 16 Results from the runs test for quarterly changes in the S&P/CS HPIs
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Table 17 Autocorrelation of quarterly changes in the inflation-adjusted S&P/CS HPIs
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Table 18 Variance ratio estimates and variance ratio test statistics for quarterly changes in the inflation-adjusted S&P/CS HPIs
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Table 19 Results from the runs test for quarterly changes in the inflation-adjusted S&P/CS HPIs
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Table 20 Nominal returns from buy-and-hold strategy compared to trading strategies based on quarterly autocorrelation pattern
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Table 21 Nominal returns from a buy-and-hold strategy compared to trading strategies based on quarterly moving averages (MA)
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Schindler, F. Predictability and Persistence of the Price Movements of the S&P/Case-Shiller House Price Indices. J Real Estate Finan Econ 46, 44–90 (2013). https://doi.org/10.1007/s11146-011-9316-1

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