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Systematic Mispricing: Evidence from Real Estate Markets

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Abstract

Despite the extensive advancement of knowledge in the field of empirical asset pricing, little is known about how this literature applies to asset classes beyond common stocks and bonds. In this paper we apply recent developments in financial economics, which posit an important role for limited market participation and financial intermediaries, in understanding real estate returns. The risk factors motivated by these theories have significant explanatory power for the cross-section of REITs. However, this relationship is the opposite of what we expected, and the results point to a more complex set of findings that are difficult to reconcile with risk-based explanations. Our results suggest systematic mispricing of real estate assets that is heavily influenced by investor sentiment.

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Notes

  1. Allen and Gale (1994) study the limited stock market participation using a two-period model. Cuoco and Basak (1998) and Guvenen (2009) develop multi-period limited participation models. These models, however, do not incorporate idiosyncratic labor income and borrowing constraints.

  2. The representative-agent models by Campbell and Cochrane (1999) and Basal and Yaron (2004) also explain these stylized facts. However, unlike Guo (2004), these models do not explain the unstable relation between stock market volatility and the dividend yield documented by Schwert (1989). The empirical findings by Muir (2017) also pose challenges to the representative-agent models.

  3. Mankiw and Zeldes (1991) and Vissing-Jorgensen (2002) use stockholders’ consumption to explain the equity premium puzzle.

  4. REITs are required to hold 75% of their assets in real property or loans secured on such assets. Further 75% of REIT annual gross income must be from real estate related sources. Also important is the requirement that REITs distribute 90% of its taxable income, which limits the ability to retain earnings within the organization.

  5. Adrian et al. (2014) construct the broker and dealer leverage using the Flow of Funds data that are subject to regular revision. Tyler Muir posts both the original AEM factor used in Adrian et al. (2014) and an updated AEM factor constructed using a more recent vintage of the Flow of Funds data on his research website https://sites.google.com/site/tylersmuir/home/data-and-code. Unlike the original AEM factor, the updated version has negligible explanatory power for the cross-section of stock portfolio returns. Similarly, Guo and Pai (2020a, Guo and Pai, 2020b) show that revision of National Income and Product Accounts data has significant effects on empirical asset pricing tests. He et al. (2017) construct the equity capital ratio of primary dealers using Compustat data that are not subject to systematic revision.

  6. The specifications LUXCON4 and LUXCON8 follow those used in Lettau et al. (2019).

  7. Guo (2006) first shows that stock market variance is significantly priced in the cross-section of stock returns. See also Kiku et al. (2014) and Campbell et al. (2018).

  8. We thank the anonymous referee for suggesting this implication.

  9. Stambaugh et al. (2012) find that the Baker and Wurgler (2006) sentiment measure forecasts major stock market anomalies.

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Acknowledgements

The authors would like to acknowledge a grant from the Real Estate Research Institute to complete this research. The authors also thank Robert Connolly, Jacob Sagi, Eva Steiner, Buhui Qiu, Chen Xue and seminar and conference participants at the University of Sydney, The Southern Methodist University, The University of Queensland, 2019 AREUEA-ASSA Annual Conference, and 2018 RERI Annual Conference for their helpful comments.

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Authors and Affiliations

  1. UQ Business School, the University of Queensland, St Lucia, QLD, 4072, Australia

    Shaun Bond

  2. Department of Finance, Carl H. Lindner College of Business, the University of Cincinnati, Cincinnati, OH, 45221, USA

    Hui Guo

  3. College of Business Administration, the University of Wisconsin, La Crosse, WI, 54601, USA

    Changyu Yang

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Correspondence to Shaun Bond.

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Appendix

Appendix

REITs Sample

We use the CRSP/Ziman Real Estate Data Series. The CRSP/Ziman database includes all REITs that traded on the three primary exchanges since 1980. We use equity REITs (RTYPE = 2) in the empirical analysis. The number of firms in CRSP/Ziman database ranges from 55 to 199 each year. We also compare our sample with the sample identified by the National Association of Real Investment Trusts (NAREIT). The firms covered in the two databases are very similar.

REITs Portfolio Construction

At the beginning of each month, we sort equity REITs equally into five portfolios based on a firm characteristic. For each portfolio, we first calculate the monthly portfolio return and then convert it into the quarterly portfolio return. We construct portfolio returns using the CRSP and COMPUSTAT data. We use the following six firm characteristics in the empirical analysis.

  • Market Equity (Size) is the share price times the number of shares outstanding. The market equity is calculated at the beginning of each month.

  • Book-to Market (B/M) is the book equity divided by market equity. The B/M is calculated in June each year. The book equity is from the end of last fiscal year. The market equity is from the end of last Calendar year.

  • Momentum (MOM) is measured as the cumulative return in the past t-12 to t-2 month.

  • Investment (I/A) is the annual growth rate in total non-cash asset. We assume that annual investment growth rate is known four months after fiscal year end.

  • Profitability (ROE) is measured as quarterly return on equity, defined as income before extraordinary item dividend by one-quarter-lagged book equity. We assume that quarterly ROE is known on the earnings announcement day (RDQ).

  • Earnings Surprise (SUE) is measured as the standardized unexpected earnings. SUE is calculated as the change in the most recent quarterly earnings per share (EPSPXQ) from its value in the same quarter last year divided by the standard deviation of changes over the previous eight quarters. We assume that earnings surprise known on the earnings announcement day (RDQ).

Financial Factors and Dividend-Price Ratio

TED is the spread between the 3-month Libor and the 3-month Treasury bill. TERM is the spread between the 10-year Treasury bond and the 3-month Treasury bill. DEF is the spread between the Baa-rated and Aaa-rated corporate bonds. DIV is the aggregate quarterly dividend divided by aggregate market cap. Dividend data are from the CRSP event database, which records dividend per share ordered by ex-dividend day (DIVAMT). We include all ordinary dividend (DISTCD first digit = 1) and exclude year-end, extra dividend (DISTCD = 1262) and special dividend (DISTCD = 1272). The dividend price ratio is calculated as the ratio of the sum of dollar amount dividend (dividend per share multiply number of share outstanding) within each quarter to the total market capitalization at the end of each quarter.

Market Factor

MKT is the value weighted excess return of SP500 stocks. REIT_MKT is the value weighted excess return of all equity REITs identified by CRSP/Ziman database. TB is the excess Treasury bond return. CB is the excess corporate bond return. We MKT and the risk-free rate data from Kenneth French at Dartmouth College and obtain TB and CB data from Amit Goyal at the University of Lausanne. We construct REIT_MKT using the CRSP/Ziman database.

Limited Stock Market Participation Factors

Stockholder consumption growth

Following Malloy et al. (2009), we use the Consumer Expenditure Survey (CEX) data to construct stockholder consumption growth factor. CEX interviews 4000 ~ 8000 households each quarter. Each household is interviewed once every three months over four consecutive quarters. About 20% sample households are replaced for each interview. The first interview is a practice interview and the results are not report in the data. The interview data only includes result from interviews two to five.

The sample spans 1982 to 2016 period. CEX data for 1996 and after can be directly download from the Public-Use Microdata (PUMD) on the CEX website. The early sample 1982 to 1995 can be download from the ICPSR website.

We construct stockholder consumption growth (SHCON) in the following steps. First, we classify all types of expenditure into durable, nondurable and service by NIPA definition. All durable items are excluded. For service, we exclude all housing expense except house operation cost, medical and education cost, rental and finance expense for durable products (such as car finance). We also exclude all miscellaneous items because we have insufficient information to correctly classify them. Table A1 shows the UCC (six-digit codes that identify the consumption item) we use for calculating household consumption.

Second, we construct household consumption growth. Interviews are conducted and recorded in each month, and we calculate the quarterly consumption growth rate at a monthly frequency. For example, if a household has the third interview in May 2016, it reports its consumption in February, March, and April 2016. The household then would have the fourth interview in August 2016 and report its consumption in May, June, and July 2016. The household’s consumption growth in July 2016, i.e., the second quarter of 2016, is the difference between the natural logarithm of total consumption reported in the fourth interview and the natural logarithm of total consumption reported in third interview.

Third, we merge the household consumption growth data with household characteristics data. We clean the data by excluding following observations. (1) Households with less than four interviews. (2) Nonurban households (variable: BLS_URBN) and households residing in student housing (variable: CUTENURE). (3) Households with incomplete income response (variable: REPSTAT). (4) The consumption growth rates that are lower than −61% or greater than 161%.

Last, we identify the stockholder in our sample and calculate the stockholder consumption growth rate. In the fifth interview, households will be asked the amount of stock, bonds, mutual fund that they hold today and the amount they hold one year from today. We classify households with either positive holding today or positive holding one year ago as stockholders. SHCON is the average of stockholders’ consumption growth rates.

Luxury consumption

Following Aït-Sahalia, Parker, and Yogo (2004), we use the sales of the high-end luxury goods to construct the luxury consumption growth factor, LUXCON. The high-end luxury goods should not be considered as durable goods for the very rich since fashion is fickle.

We include sales of three luxury retailers, Gucci (GUC), Saks (SKS) and Tiffany (TIFF, TIF since 1986). The quarterly sales data are available from COMPUSTAT over the 1995 to 2004 period for Gucci, the 1991 to 1997 period for Saks, and the 1960 to 2016 period for Tiffany.

COMPUSTAT reports the quarterly sale (turnover) data for all public companies. COMPUSTAT segment reports the annual US sale and annual international sale data for all public companies. Using COMPUSTAT segment data, we can calculate the ratio of US retail to the total sale each year. Then, we multiply this ratio by the quarterly total sale to get quarterly US sale. Because of the seasonality in luxury sales, we use the year-over-year growth rate. For example, the 2016Q4 luxury consumption growth is the percentage change of the 2016Q4 luxury sales over the 2015Q4 luxury sales.

Intermediary Equity Capital Ratio

He, Kelly, and Manela (2017) creates the intermediary equity capital ratio, which is the aggregate equity capital ratio of the New York Fed’s Primary dealer. The intermediary capital ratio is denoted as aggregate value of market equity divided by aggregate market equity plus aggregate book debt. Their factor could be downloaded at http://www.zhiguohe.com/research.html

Intermediary Leverage Ratio

Adrian, Etula, and Muir (2014) constructs intermediary leverage ratio, which is the total financial assets divided by the difference between total financial assets and total liabilities. Adrian, Etula, and Muir use aggregate broker and dealer’s financial assets and liabilities data from Financial Accounts of the United States hosted by the Federal Reserve Board. They are free to download from the website https://www.federalreserve.gov/releases/z1/current/default.htm. Tyler and Muir also posts both the original data used in Adrian, Etula, and Muir (2014) and the updated data on his website https://sites.google.com/site/tylersmuir/home/data-and-code.

Capital Share of Income

We follow Lettau, Ludvigson, and Ma (2019) to construct the capital share of income. The relation between labor share (LS) and capital share (CS) is CS = 1-LS. Lettau, Ludvigson, and Ma calculate the labor share growth rate by taking the log difference of quarterly seasonally adjusted labor share index. The capital share growth rate is the labor share growth rate with the opposite sign. The labor share index are free to download from the FRED database hosted by the St. Louis Fed (http://research.stlouisfed.org/fred2/series/PRS85006173).

Table 13 List of UCC.
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Bond, S., Guo, H. & Yang, C. Systematic Mispricing: Evidence from Real Estate Markets. J Real Estate Finan Econ (2022). https://doi.org/10.1007/s11146-021-09883-9

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