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Analyzing the Impacts of Property Age on REITs and the Reasons Why REITs Own Older Properties

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Abstract

This paper first documents the impacts of property age on the operational efficiency, portfolio risk and market valuation of REITs. Based on the findings, we examine three possible explanations why REITs own older properties. Using a comprehensive property-level data set of U.S. equity REITs from 1995 to 2020, we construct a firm-level property age measure based on the age of individual properties held by REITs. Controlling for important firm characteristics, we find that REITs holding more older properties exhibit lower operational efficiency, lower firm value and higher firm risk than their counterparts. Moreover, while the stockholders do not enjoy higher stock returns, we find evidence that managers of REITs that carry more aged properties in their portfolios earn significantly higher compensation. This is consistent with agency cost associated with managerial opportunism. Furthermore, REITs that own more properties in the same locations as their headquareters and those with higher geographic concentration of properties tend to hold older properties. This finding is consistent with the hypothesis based on geographic locations of older properties. We, however, do not find evidence supporting the growth hypothesis associated with core-plus or value-add investment strategies.

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Notes

  1. See https://www.eia.gov/consumption/commercial/.

  2. In the U.S., since asset value in accrual income accounting is based on historical cost, and depreciation is allowed by the tax laws to reduce taxable income of the owners, it is important to estimate annual depreciation rate of properties so that a depreciation schedule can be determined for tax purpose.

  3. For example, Malpezzi et al. (1987) find that the annual depreciation rates for residential housing range from 0.38–2.40%. In another study using housing data from the Netherlands, Franche and Van de Minne (2017) observe an annual depreciation of 1.5% for the first 20 years and 1% for the first 50 years. Meanwhile, Hulten and Wykoff (1981) estimate the annual depreciation rate of commercial properties to be roughly 3% for building structures. Fisher et al. (2005) show that multifamily properties have a nominal depreciation rate of 5.25% per year based on building structure.

  4. Physical obsolescence refers to wear and tear on the building structure and its components over time (Francke & Van de Minne, 2017).

  5. Functional obsolescence occurs when the structure becomes less suited to its intended use or relatively less desirable for its users/tenants due to technological advancements or changes in preferences, such as demand for fiber-optic or need for sustainable energy-efficient design by space occupiers (Bokhari & Geltner, 2018).

  6. Economic obsolescence refers to the phenomenon of the highest and best use of the site evolving away from the use or intensity of the current structure. An example is that changing to commercial or residential use would be more profitable than industrial use of the current building (see Bokhari & Geltner, 2018).

  7. Levkovich et al. (2018) also observe that depreciation rates of commercial real estate in the Netherlands vary considerably across different property segments. In general, properties constructed between 1960–1990 experience negative depreciation effects, but pre-WWII office and industrial properties can experience an increase in value due to vintage effect.

  8. See NAREIT website on “REITs by the Numbers” at https://www.reit.com/data-research/data/reits-numbers.

  9. Previous research shows that US REITs typically sell a small percentage of their properties each year. For example, Eichholtz and Yönder (2015) document a typical US REIT sells about 5% of its properties based on the dollar value of properties between 2003 and 2010. Feng et al. (2022) find most of REITs adopt a long-term investment strategy by holding most of their properties over a long time period. These findings suggest that the firm-level property age of REITs is unlikely to change significantly in the short term since it is an aggregated measure of the property age of REIT portfolios. Meanwhile, we acknowledge that a self-selection bias can exist as managers may choose to hold older properties. We address this selection bias using Heckman sample selection method. The results remain largely similar.

  10. E.g., see a list for REIT headquarters by location: https://www.reitsacrossamerica.com/us-reits-headquartered-state.

  11. In particular, older CEOs tend to be more conservative by adopting low growth strategy (Child, 1974), resisting technology adoption (Kitchell, 1997), spending less on R&D (Barker & Mueller, 2002), making few acquisitions (Yim, 2013) and taking less risks (Serfling, 2014; Andreou et al., 2017; Belenzon et al., 2019). In a recent study, Zhang and Ooi (2022) track the property acquisitions of 150 REIT CEOs and find that younger CEOs tend to engage more frequently and aggressively on property acquisitions to signal to the market their ability to make deals.

  12. See article, “Older condos plagued by high maintenance costs”, at https://www.marketwatch.com/story/older-condos-plagued-by-high-maintenance-costs-2014-06-12.

  13. See a TD Insurance article titled “How property insurance is calculated”, at https://www.tdinsurance.com/products-services/home-insurance/tips-advice/premium-calculations.

  14. Older buildings are generally less energy efficient as compared to newer buildings because they usually adhere to outdated standards. See https://www.forbes.com/sites/pikeresearch/2016/04/13/energy-efficient-building/?sh=41a4e8d548f8.

  15. Kenneth R. French’s Data Library: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

  16. See https://www.eia.gov/consumption/commercial/data/2018/pdf/CBECS_2018_Building_Characteristics_Flipbook.pdf.

  17. Specifically, we run the following time-series regression on each REIT during the entire sample period.

    \({R}_{i,t}={\beta }_{0}+{\beta }_{1}{R}_{m,t}+{\beta }_{2}{SMB}_{t}+{\beta }_{3}{HML}_{t}+{\beta }_{4}{RMW}_{t}+{\beta }_{5}{CMA}_{t}+{\varepsilon }_{t}\)   where \({R}_{i,t}\) is the excess stock return of REIT \(i\), \({R}_{m,t}\) is the risk-free stock return of the market, \({SMB}_{t}\) (Small minus Big), \({HML}_{t}\) (High minus Low), \({MOM}_{t}\) (Momentum), and \({RMW}_{t}\) (Robust minus Weak), and \({CMA}_{t}\) (Conservative minus Aggressive) are the return to zero investment factor-mimicking portfolios designed to capture size, book-to-market effects, momentum, profitability. and investment risk in year \(t\). Then, we use the market return, the annual \(SMB\), \(HML\), \(MOM\), \(RMW\), and \(CMA\) risk factors, and the estimated factor loadings, \({\beta }_{s}\), of the factor models to obtain the estimated expected return, \({\widehat{R}}_{i,t}\).

  18. Leskinen et al. (2020) provide a literature review of green certification in commercial properties.

  19. The number of observations for this regression is lower than the other regressions because information on repairs and maintenance costs is not available for some of the REITs.

  20. Previous literature shows that the volatility of REIT returns is quite different from other assets classes (e.g., Cotter & Stevenson, 2006; Fei et al., 2010). Also, REIT extreme risks are higher than non-REIT firms (Zhou & Anderson, 2012). REIT pricing is influenced by their idiosyncratic risk (e.g., Ooi et al., 2009; Chiang et al., 2009; Cakici et al., 2014) but not distress risk (Shen, 2021).

  21. The U.S. geographic regions determined by National Council of Real Estate Investment Fiduciaries (NCREIF) are: (1) NE (Northeast): including ME, VT, NH, NY, CT, RI, MA, PA, NJ, DE; (2) ME (Mideast): including MD, WV, VA, KY, NC, SC, DC; (3) SE (Southeast): including TN, GA, FL, AL, MS; (4) EN (East North Central): including MI, IL, OH, IN, WI; (5) WN (West North Central): including MN, IA, MO, KS, NE,S D, ND; (6) SW (Southwest): including TX, OK, AR, LA, (7) MT (Mountain): MT, ID, WY, UT, CO, NM, AZ, NV; and (8) PC (Pacific): including WA, OR, CA, AK, HI.

  22. See some recent asset pricing studies in the REIT literature (e.g., Ling et al. (2019), Beracha et al. (2019b), Chen et al. (2020), Milcheva et al. (2021), Shen (2021), Shen et al. (2021), and Zhu and Lizieri (2022)) for similar tests.

  23. For robustness, we also examine the equal-weighted monthly excess stock return of portfolios. We find quantitatively similar results, which are omitted from the paper for brevity.

  24. For robustness, we also adopt the capital asset pricing model (CAPM) model, Fama and French (1993) five-factor model and the Fama and French (2015) five-factor model. We find quantitatively similar results on the trivial coefficients and statistical insignificance of the alphas using these models. Those results are omitted from the paper for brevity.

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Authors and Affiliations

  1. University of Texas at El Paso, El Paso, TX, USA

    Zifeng Feng

  2. National University of Singapore, Singapore, Singapore

    Joseph Ooi

  3. Florida International University, Miami, FL, USA

    Zhonghua Wu

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  1. Zifeng Feng
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Correspondence to Zifeng Feng.

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Appendix: Definition of Variables

Appendix: Definition of Variables

Variable

Definition

Operating expenses

Total expense minus real estate depreciation, amortization divided by total revenues.

Repairs & maintenance

Repairs and maintenance expenses divided by the book value of assets.

Occupancy rate

The physical occupancy rate, as reported by S&P Global Market Intelligence

Return volatility

The standard deviation of daily stock returns at each firm year.

Market beta

The annual beta of the stock in each firm based on CAPM at each firm-year.

Excess return

Stock return minus risk-free rate

Expected return

The expected return is estimated based on Fama and French (2015) five-factor model. We use the market return, the annual risk factors, and the estimated factor loadings,\(\alpha , {\beta }_{1}, {\beta }_{2},\)\({\beta }_{3}\),\({\beta }_{4}\), and\({\beta }_{5}\), of the factor models estimated using the full sample to obtain the estimated expected return,\({\widehat{R}}_{i,t}\).

Market-to-book equity ratio

The ratio of the market capitalization of the REIT to its total equity.

NAV premium (discount)

The ratio of share price and net asset value per share as of the fiscal year-end munis one. Net asset value per share is calculated as total assets minus total debt and then scaled by common shares outstanding.

Top 5 executive compensation

The average total compensation (tdc1 in ExecuComp) of the top five executives in a firm.

Geographic concentration

The Herfindahl Index of REITs' assets invested in different MSA, based on book values. Properties without net book value recorded in S&P Global Market Intelligence are excluded.

Headquarter property percentage

The percentage of a REIT's property is located at the headquarters’ MSA to its total property value, based on net book value. Properties without net book value recorded in S&P Global Market Intelligence are excluded.

 

Funds from operations divided by the book value of assets.

Weighted average rate

The weighted average interest rate of debts.

Year listed

The number of years since the IPO or REIT status was established if the IPO year is missing.

Leverage ratio

The ratio of total debt to total equity.

Real estate investment growth

Real estate investment growth rate, as reported by the S&P Global Market Intelligence.

Cash stock

Cash and cash equivalents scaled the book value of assets.

Property type concentration

The Herfindahl Index of REITs, calculated using their assets invested in different real estate property types, based on net book value.

Northeastern concentration

The ratio of real estate assets of a REIT invested in the northeastern region as defined by NCREIF, based on net book value. The Northeast region includes M.E., VT, NH, NY, CT, RI, MA, PA, NJ, DE.

M&A dummy

A binary variable indicating whether the REIT is involved in mergers and acquisitions activities as a buyer or a seller in a given year.

Institutional ownership

The ratio of the shares owned by institutions divided by the total common share outstanding.

Green property share

The percentage of a REIT's property is green (i.e., energy star or LEED certified) to its total property value, based on net book value. Properties without net book value recorded in S&P Global Market Intelligence are excluded.

Capex / Assets

Capital expenditures divided by the book value of assets.

Real estate property type

REIT's type of real estate property, determined by the tenant's uses of the property, as reported by S&P Global Market Intelligence.

Property age

It is calculated as the mean of the ages of properties a REIT holds. Properties without year-built information recorded in S&P Global Market Intelligence are excluded.

Property age bin

binary variables indicating whether the property age of REITs is less than 20 years (Prop Age Bin: < 20 Years), between 20–40 years (Prop Age Bin: 20–40 Years), and greater than 40 years (Prop Age Bin: >40 Years), respectively.

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Feng, Z., Ooi, J. & Wu, Z. Analyzing the Impacts of Property Age on REITs and the Reasons Why REITs Own Older Properties. J Real Estate Finan Econ (2023). https://doi.org/10.1007/s11146-023-09961-0

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