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Price Discovery in Real Estate Markets: A Dynamic Analysis

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Abstract

Although the correlation between the public and private market pricing of real estate has generated considerable research effort, the methods utilized in previous studies have failed to capture the dynamic nature of this correlation. This paper proposes a new statistical method to address this issue. This method, known as the dynamic conditional correlation GARCH model, enables us to study the dynamics of the correlation between the two markets over time and enrich our understanding of the public and private market pricing of real assets. We find that the correlation between NAV returns and REIT returns is dynamic for all REIT types and there is a strong degree of persistence in the series of correlation. Our Granger-causality tests show that price discovery generally takes place in the securitized public market. However, we also find significant variations across property types and individual firms within each type. Our results indicate that constructing an optimal portfolio requires firm level analysis of causality and correlation between REIT returns and NAV returns.

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Notes

  1. The method was developed and extended in Engle (2002) and Engle and Sheppard (2001). Robert Engle was awarded 2003 Nobel Prize in Economics for his contribution to methods of analyzing economic time series with time-varying volatility (ARCH)

  2. To our knowledge, this is the first study that examines the price discovery and correlation of REIT and NAV returns at the firm level

  3. Examples include Pagliari et al. (2005); Clayton and MacKinnon (2001a); Ling and Ryngaert (1997) and Barkham and Geltner (1995)

  4. An application of the dynamic correlation multivariate GARC model to build an optimal portfolio can be found in Case et al. (2008) and Huang and Zhong (2006). Case et al. (2008) construct an optimal portfolio of RETis, stocks, bonds and cash while the portfolio of Huang and Zhong (2006) consists of seven asset classes—U.S. stocks, foreign stocks, U.S. bonds, foreign bonds, commodities, Treasury inflation-protected securities (TIPS), and U.S. REITs

  5. Cherkes et al. (2006) also offer a nice review of the literature on this issue

  6. Engle (2002) compares the DCC model with several other estimators, including moving average methods, and demonstrates the superiority of the DCC model. Two recent studies (Case et al. 2008; Huang and Zhong 2006) show that an optimal portfolio based on the DCC model would outperform an optimal portfolio based on alternative models

  7. In our case, k is equal to 2 and includes REIT and NAV returns

  8. The capitalization rate varies from location to location, across property types, and over time. The capitalization rate used in the NAV estimations reflects the property mix (apartment, office, industrial, etc.), geographic location, and growth prospects of the property holdings of the REIT

  9. The reason for the discrepancy between two NAV estimates is due to noise in the calculations of capitalization rates and net operating income. Cap rates are often obtained from surveys of players in local markets and the net operating income needs to be estimated from the REIT’s financial statements

  10. The four dates in the table are determined by the availability of data. The correlation analysis around September 11, 2001 could not be conducted for HLT (Hilton Hotel), NNN (Commercial Net Lease Reality, Inc.), and EPR (Entertainment Properties Trust) REITs because the NAV data for these firms is not available prior to September 11th

  11. One possible explanation for the changes in the correlations around September 11, 2001 is that the attacks on that day prompted investors to re-estimate both REIT values and NAVs

  12. The five-or-fewer rule refers to the restriction on REITs that the five largest shareholders cannot collectively own more than 50% of the company’s stock

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Authors and Affiliations

  1. Smeal College of Business Administration, Pennsylvania State University, University Park, PA, 16802, USA

    Abdullah Yavas

  2. Whitman School of Management, Syracuse University, Syracuse, NY, 13244, USA

    Yildiray Yildirim

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  1. Abdullah Yavas
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  2. Yildiray Yildirim
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Correspondence to Abdullah Yavas.

Additional information

We are grateful to Dennis Capozza, Brad Case, Jim Clayton, Joseph Pagliari, Dogan Tirtiroglu, Editor and the anonymous referee for their helpful comments and Real Estate Research Institute (RERI) for their financial support.

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Yavas, A., Yildirim, Y. Price Discovery in Real Estate Markets: A Dynamic Analysis. J Real Estate Finan Econ 42, 1–29 (2011). https://doi.org/10.1007/s11146-009-9172-4

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