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Asymmetric Dependence in Real Estate Investment Trusts: An Asset-Pricing Analysis

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Abstract

REITs are often assumed to be defensive assets having a low correlation with market returns. However, this dependence is not symmetric across the joint-return distribution. Disappointment-averse investors with state-dependent preferences attach (dis-)utility to investments exhibiting (lower-tail) upper-tail asymmetric dependence. We find strong empirical evidence that investors price this asymmetric dependence in the cross section of US REIT returns. In particular, we show that REIT stocks with lower-tail asymmetric dependence attract a risk premium averaging 1.3 % p.a. and REIT stocks exhibiting upper-tail asymmetric dependence are traded at discount averaging 5.8 % p.a. We find no evidence that the equity β is positively priced in US REIT returns. Our findings imply that traditional estimators of REIT cost of capital and performance measurement are likely to be substantially misrepresentative.

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Notes

  1. See for example Huang and Wu 2015; Zimmer 2015; Bianchi and Guidolin 2014; Case et al. 2012b; Hoesli and Reka 2013; Rong and Trück 2010; Simon and Ng 2009; Patel and Nimmanunta 2009; Lizieri et al. 2007; Knight et al. 2005; Chatrath et al. 2000; Goldstein 1999; Lizieri and Satchell 1997a, (Lizieri and Satchell 1997b). The existence of AD is not specific to REIT markets only. There also exists a substantial literature on the existence of AD in the US stock market, see (Low et al. 2013; Bollerslev and Todorov 2011; Bali et al. 2009; Hatherley and Alcock 2007; Hong et al. 2007; Ang et al. 2006; Post and Van Vliet 2006; Hartmann et al. 2004; Patton 2004; Ang and Bekaert 2002; Ang and Chen 2002; Longin and Solnik 2001; Erb et al. 1994).

  2. 26 US Code, Section 857 - Taxation of real estate investment trusts and their beneficiaries.

  3. During the 2008-2009 financial crisis, the REIT market experienced a dramatic decline in market value. The FTSE NAREIT U.S. Real Estate Index fell from its index value of 213.68 in January 2007 to 63.41 in March 2009 (Case et al. 2012a).

  4. We unitize β in each data set before the J-statistic is estimated. That is for each set \(\left \{ R_{it},R_{mt} \right \}_{t=1}^{T}\), we get \(\hat {R}_{it} = R_{it} - \beta R_{mt}\), where R i t and R m t is continuously compounded return on asset i and market, and \(\beta = \textup {cov}(R_{it}, R_{mt})/\sigma _{R_{mt}}^{2}\). The first transformation implies that \(\beta _{\hat {R}_{it},R_{mt}} = 0\). This enables us to standardise the data to get identical standard deviation of the CAPM regression residuals and get \(R_{mt}^{S}\) and \(\hat {R}_{it}^{S}\). The final transformation step sets the \(\hat {\beta }\) to 1 by letting \(\tilde {R}_{mt} = R_{mt}^{S}\) and \(\tilde {R}_{it} = \hat {R}_{it}^{S} + R_{mt}^{S}\).

  5. We limit our sample to NYSE only due to differing listing requirements of the remaining stock exchanges in the US. NYSE provides a market for well-established companies, which are expected to have a more stable stock price development. NYSE offers the highest level of disclosure of information of all the stock exchanges in the US, and contains relatively large companies with a more stable performance and operating history.

  6. This t-statistic level of 3.0 is set by Harvey et al. (2014) to account for data mining, correlation among the tests and missing data issues.

  7. US Code 26, Section 856.

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  1. The University of Sydney, Sydney, Australia

    Jamie Alcock & Petra Andrlikova

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  1. Jamie Alcock
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Correspondence to Jamie Alcock.

Appendix: Robustness Tests

Appendix: Robustness Tests

Table 9 The Time Series Average Returns for Double Sorted Portfolios. For a given month, we first sort stocks into β − deciles, and then into J Adj deciles within each characteristic decile in Panel A
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Table 10 Alternative downside risk measure: β − (1992–2013). We measure the in-sample risk premia using the Ang et al. (2006) where cross-sectional regressions are computed every month rolling forward
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Table 11 Equally-weighted In-sample Regressions (1992–2013). We measure the in-sample risk premia using the Ang et al. (2006) where cross-sectional regressions are computed every month rolling forward
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Table 12 Out-of-sample Regression Specifications (1992–2013). We measure the out-of-sample risk premia using the Fama and MacBeth (1973) asset pricing procedure where value-weighted cross-sectional regressions are computed every month rolling forward
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Table 13 In-sample Alternative Regression Specifications (1992–2013). We measure the in-sample risk premia using the Ang et al. (2006) where equally-weighted cross-sectional regressions are computed every month rolling forward
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Table 14 Out-of-sample Alternative Regression Specifications (1992–2013). We measure the out-of-sample risk premia using the Fama and MacBeth (1973) asset pricing procedure where equally-weighted cross-sectional regressions are computed every month rolling forward
Full size table

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Alcock, J., Andrlikova, P. Asymmetric Dependence in Real Estate Investment Trusts: An Asset-Pricing Analysis. J Real Estate Finan Econ 56, 183–216 (2018). https://doi.org/10.1007/s11146-016-9593-9

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