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

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  • Felix Schindler

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. Copyright Springer Science+Business Media, LLC 2013

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  • Felix Schindler, 2013. "Predictability and Persistence of the Price Movements of the S&P/Case-Shiller House Price Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 46(1), pages 44-90, January.
  • Handle: RePEc:kap:jrefec:v:46:y:2013:i:1:p:44-90
    DOI: 10.1007/s11146-011-9316-1
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    2. Amélie Mauroux, 2015. "L’information préventive améliore-t-elle la perception des risques? Impact de l’Information Acquéreur Locataire sur le prix des logements," Policy Papers 2015.05, FAERE - French Association of Environmental and Resource Economists.
    3. Lake, A., 2020. "Behavioural Finance at Home: Testing Deviations of House Prices from their Fundamental Values," Cambridge Working Papers in Economics 20104, Faculty of Economics, University of Cambridge.
    4. Daniel Broxterman & Tingyu Zhou, 2023. "Information Frictions in Real Estate Markets: Recent Evidence and Issues," The Journal of Real Estate Finance and Economics, Springer, vol. 66(2), pages 203-298, February.
    5. Omokolade Akinsomi & Goodness C. Aye & Vassilios Babalos & Fotini Economou & Rangan Gupta, 2016. "Real estate returns predictability revisited: novel evidence from the US REITs market," Empirical Economics, Springer, vol. 51(3), pages 1165-1190, November.
    6. R. Kelley Pace & Raffaella Calabrese, 2022. "Ignoring Spatial and Spatiotemporal Dependence in the Disturbances Can Make Black Swans Appear Grey," The Journal of Real Estate Finance and Economics, Springer, vol. 65(1), pages 1-21, July.
    7. Dittmann Iwona, 2017. "Similarity of Changes in Average Prices of Residential Properties in Europe in 2010-2016," Real Estate Management and Valuation, Sciendo, vol. 25(4), pages 63-74, December.
    8. Amélie Mauroux, 2015. "L’information préventive améliore-t-elle la perception des risques? Impact de l’Information Acquéreur Locataire sur le prix des logements," Working Papers 2015.17, FAERE - French Association of Environmental and Resource Economists.
    9. Zheng, Min & Wang, Hefei & Wang, Chengzhang & Wang, Shouyang, 2017. "Speculative behavior in a housing market: Boom and bust," Economic Modelling, Elsevier, vol. 61(C), pages 50-64.
    10. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    11. Roland Füss & Felix Schindler, 2011. "Diversifikationsvorteile verbriefter Immobilienanlagen in einem Mixed‐Asset‐Portfolio," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 12(2), pages 170-191, May.
    12. Valadkhani, Abbas & Costello, Greg & Ratti, Ronald, 2016. "House price cycles in Australia’s four largest capital cities," Economic Analysis and Policy, Elsevier, vol. 52(C), pages 11-22.
    13. Felix Schindler, 2014. "Persistence and Predictability in UK House Price Movements," The Journal of Real Estate Finance and Economics, Springer, vol. 48(1), pages 132-163, January.
    14. Wen-Yi Chen & Tsangyao Chang & Yu-Hui Lin, 2018. "Investigating the Persistence of Suicide in the United States: Evidence from the Quantile Unit Root Test," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 813-833, January.

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    More about this item

    Keywords

    Housing market; Times series model; Predictability; Persistence; C22; C53; R30;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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