Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

An Empirical Investigation of Herding Behavior in the U.S. REIT Market


Our study investigates the market-wide herding behavior in the U.S. equity REIT market. Utilizing the quantile regression method, we find that herding is more likely to be present in the high quantiles of the REIT return dispersion. This implies that REIT investors tend to herd under turbulent market conditions. Our results also support the asymmetry of herding behaviors, that is, herding is more likely to occur and becomes stronger in declining markets than in rising markets. In addition, our findings show that the current financial crisis has caused a change in the circumstances under which herding can occur, as we find that during the current crisis REIT investors may not start to herd until the market becomes extremely turbulent whereas during the relatively normal period before the crisis, investors tend to herd when the market is moderately turbulent. Finally, we find that compared with the case of the ‘pre-modern’ era, REIT investors are more likely to herd in the ‘modern’ era, during which herding usually occurs when the market becomes tumultuous. This implies that the switch of REITs from passive externally managed entities into active self-managed ones has made the investors more responsive to market sentiment.

This is a preview of subscription content, log in to check access.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    Although our test of herding is similar to that of CCK, our measure of CSAD t differs from their. Their measure was derived from the conditional version of the CAPM, whereas ours follows the method used by (Christie and Huang 1995) and Gleason et al. (2004), which does not require the estimation of beta. This avoids the possible specification error associated with the asset pricing model.

  2. 2.

    During the period of 1980–2010, a total of 107 mortgage REITs have ever existed. However, the number of cross sections for mortgage REITs is small: it ranges from 4 to 43. This means that at some days, only 4 mortgage REITs exist. Further, we find for half of our sample period, the number of cross sections does not exceed 20 for mortgage REITs. The situation is even worse for hybrid REITs, for which a total of only 40 have ever existed. As can be imagined, for some extended periods of time the number of cross sections had been zero.

  3. 3.

    There have been a total of 383 equity REITs ever existing over our sample period. Given this, the number of cross sections remains sufficiently large: it ranges from 46 to 202.

  4. 4.

    It has been shown in the financial literature (e.g. Goyal and Santa-Clara 2003) that cross-sectional dispersion and time series volatility are significantly positively correlated, and they tend to move together. Such a point can be verified in this study. To do so, we first need to estimate the time series of market volatility. There are two alternative measures suitable for our study (e.g. Cotter and Stevenson 2008 and Zhou 2011): one is the power transformations of the market returns (e.g. absolute returns or squared returns), and the other is the conditional volatility obtained from GARCH-type models. Following the first alternative, the most apparent proxy for market volatility in our study is the absolute aggregate REIT return, i.e. |R m,t | . Using it, we find that the correlation coefficient between market volatility and CSAD are highly positive and significant: 0.70 for daily, 0.75 for weekly, and 0.79 for monthly. Similar results are found when the second alternative is followed (i.e. using volatility generated from a GARCH(1,1) model of R m,t ).

  5. 5.

    As noted by an anonymous reviewer, the reported results could be explained by an alternative hypothesis. That is, market sensitivities are unstable and they could become smaller during times of market stress. To test for the changes in market sensitivities, we first estimated the time-varying β for each equity REIT based on the capital asset pricing model (CAPM) through the application of the commonly used rolling window method. Then we run a fixed-effect panel regression: \( {\beta_{i,t}} = {a_1} + {a_2}{D_{i,t}} \), where i represents the ith REITs, t is the time index, and D is a dummy variable indicating whether market is under stress. To define D, we follow Christie and Huang (1995): D i,t  = 1 if the aggregate REIT market return (R m ) at time t lies in the extreme tails, which is usually identified by using small percentages (e.g. 1% or 5% of the return distribution). Given the above setting and using the 5% measure for the tails (the results are similar using the 1% as tails), we run the panel regression based on the daily data and find a 1 = 0.684 (0.000) and a 2 = 0.048 (0.000), where p-values are in parenthesis. As can be seen, a 2 is positive and significant. This suggests that during stressful times, market sensitivities turn out to be larger than under usual times. So the alternative hypothesis is not supported. It is worth noting that a window of 252 days (one-trading year) is used to obtain the time varying β in the above regression. However, further experiments show that our results are robust to different sizes of the window ranging from one trading quarter to 5 years and even alternative methods of beta estimation. Further, when weekly and monthly data are used, we find the results are qualitatively unchanged.

  6. 6.

    To define small cap stocks, we follow the common practice to use deciles. Specifically, we use deciles 8–10 to define small cap stocks. This practice is consistent with those of some empirical studies (e.g. Sa-Aadu et al. 2010), and also with the size based indices kept at Professor French’s data page ( where indices are classified as low 30 (the smallest 30% of stocks, i.e. small cap), med 40 (the intermediate 40% of stocks, i.e. mid cap), and high 30 (the largest 30% of stocks, i.e. large cap). For the above defined small cap stocks (REITs excluded), their daily and monthly price data are obtained directly from the Center for Research in Security Prices (CRSP) over the period of January 1980 through December 2010. The weekly data are constructed by us in the same way as we did for REITs.

  7. 7.

    The OLS-CUSUM procedure tests for structural breaks based on the cumulated sum (CUSUM) of the OLS residuals within a time series regression framework. The rationale is that CUSUM tends to drift off following a structural break. So if CUSUM is found to become too large (i.e. crossing some predefined critical lines), structural breaks could be said to occur. Thanks to Zeileis et al. (2007), this procedure can now be easily implemented in R using the ‘Strucchange’ package.

  8. 8.

    As a robustness check, we experiment with different break dates which are either several months before or after July 1, 2008. The estimation results are similar to those reported here. To save space, they are not presented but are available upon request.

  9. 9.

    We thank an anonymous reviewer for bringing up this suggestion.

  10. 10.

    According to its annual reports, Kimco held its IPO in November 1991.


  1. Ambrose, B. W., & Linneman, P. (2001). REIT organizational structure and operating characteristics. Journal of Real Estate Research, 21, 141–162.

  2. Anderson, R., Clayton, J., Mackinnon, G., & Rajneesh, S. (2005). REIT returns and pricing: the small cap value stock factor. Journal of Property Research, 22, 267–286.

  3. Anderson, R. I., Guirguis, H, & Bony, V. (2010). The impact of switching regimes and monetary shocks: An empirical analysis of REITs. Working paper, University of Denver.

  4. Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.

  5. Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18, 1–22.

  6. Barnes, M., & Hughes, A. W. (2002). A quantile regression analysis of the cross section of stock market returns. Working Paper, Federal Reserve Bank of Boston.

  7. Bekaert, G., & Wu, G. (2000). Asymmetric volatility and risk in equity markets. Review of Financial Studies, 13, 1–42.

  8. Bikhchandani, S., & Sharma, S. (2001). Herd behavior in financial markets. IMF Staff Papers, 47, 279–310.

  9. Buchinsky, M. (1995). Estimating the asymptotic covariance matrix for quantile regression models: a Monte Carlo study. Journal of Econometrics, 68, 303–338.

  10. Buchinsky, M. (1998). Recent advances in quantile regression models: a practical guideline for empirical research. Journal of Human Resources, 33, 88–126.

  11. Case, B., Yang, Y., & Yildirim, Y. (2010). Dynamic correlations among asset classes: REIT and stock returns, forthcoming. Journal of Real Estate Finance and Economics.

  12. Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An examination of herd behavior in equity markets: an international perspective. Journal of Banking and Finance, 24, 1651–1679.

  13. Chiang, T. C., Li, J., & Tan, L. (2010). Empirical investigation of herding behavior in Chinese stock markets: evidence from quantile regression analysis. Global Finance Journal, 21, 111–124.

  14. Christie, W. G., & Huang, R. D. (1995). Following the pied piper: do individual returns herd around the market? Financial Analysts Journal, 51, 31–37.

  15. Clayton, J., & MacKinno, G. (2003). The relative importance of stock, bond and real estate factors in explaining REIT returns. Journal of Real Estate Finance and Economics, 27, 39–60.

  16. Clement, M. B., & Tse, S. Y. (2005). Financial analyst characteristics and herding behavior in forecasting. Journal of Finance, 60, 307–341.

  17. Cotter, J., & Stevenson, S. (2008). Modeling long memory in REITs. Real Estate Economics, 36, 533–554.

  18. Demirer, R., & Kutan, A. M. (2006). Does herding behavior exist in Chinese stock markets? Journal of International Financial Markets, Institutions and Money, 16, 123–142.

  19. Devenow, A., & Welch, I. (1996). Rational herding in financial economics. European Economic Review, 40, 603–615.

  20. Duffee, G. R. (2000). Asymmetric cross-sectional dispersion in stock returns: Evidence and implications. Berkeley: Working Paper U.C.

  21. Falzon, R. (2002). Stock market rotations and REIT valuation. Prudential Real Estate Investors, October 1–10.

  22. Gleason, C. A., & Lee, C. M. C. (2003). Analyst forecast revisions and market price discovery. The Accounting Review, 78, 193–225.

  23. Gleason, K. C., Mathur, I., & Peterson, M. A. (2004). Analysis of intraday herding behavior among the sector ETFs. Journal of Empirical Finance, 11, 681–694.

  24. Goyal, A., & Santa-Clara, P. (2003). Idiosyncratic risk matters! Journal of Finance, 58, 975–1008.

  25. Graham, J. R. (1999). Herding among investment newsletters: theory and evidence. Journal of Finance, 54, 237–268.

  26. Hwang, S., & Salmon, M. (2004). Market stress and herding. Journal of Empirical Finance, 11, 585–616.

  27. Koenker, R. W. (2005). Quantile regression. Cambridge University Press.

  28. Koenker, R. W., & Bassett, G., Jr. (1978). Regression quantiles. Econometrica, 46, 33–50.

  29. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1992). The impact of institutional trading on stock prices. Journal of Financial Economics, 32, 23–44.

  30. Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. Journal of Finance, 56, 649–676.

  31. Ploberger, W., & Kramer, W. (1992). The CUSUM test with OLS residuals. Econometrica, 60, 275–285.

  32. Sa-Aadu, J., Shilling, J., & Tiwari, A. (2010). On the portfolio properties of real estate in good times and bad times. Real Estate Economics, 38, 529–565.

  33. Tan, L., Chiang, T. C., Mason, J., & Nelling, E. (2008). Herding behavior in Chinese stock markets: an examination of A and B shares. Pacific-Basin Finance Journal, 16, 61–77.

  34. Trueman, B. (1994). Analyst forecasts and herding behavior. Review of Financial Studies, 7, 97–124.

  35. Welch, I. (2000). Herding among security analysts. Journal of Financial Economics, 58, 369–396.

  36. Wermers, R. (1999). Mutual fund herding and the impact on stock prices. Journal of Finance, 54, 581–622.

  37. Zeileis, A., Shah, A., & Patnaik, I. (2007). Exchange rate regime analysis using structural change methods. Working Paper, Department of Statistics and Mathematics, WU Vienna University of Economics and Business

  38. Zhou, J. (2011). Multiscale analysis of international linkages of REIT returns and volatilities. forthcoming. Journal of Real Estate Finance and Economics.

  39. Zhou, J., & Anderson, R. I. (2010). Extreme risk measures for international REIT markets. forthcoming. Journal of Real Estate Finance and Economics.

Download references


We thank the Editor and two anonymous referees for their valuable comments and suggestions. We also thank Joshua Harris for his research assistance. All errors remain our own.

Author information

Correspondence to Jian Zhou.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Zhou, J., Anderson, R.I. An Empirical Investigation of Herding Behavior in the U.S. REIT Market. J Real Estate Finan Econ 47, 83–108 (2013).

Download citation


  • Herding
  • REITs
  • Quantile regression
  • Asymmetry