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Economies of Scale and the Operating Efficiency of REITs: A Revisit

Abstract

Building on past research regarding the operating efficiency of Real Estate Investment Trusts (REITs) and recognizing the substantial changes in this industry since the turn of the millennium, we examine REIT efficiency over the period 2001–2015. Using both time-varying stochastic frontier and linear models of costs, revenues, and performance measures, we find evidence showing that the REIT industry is slowly moving away from both cost and revenue efficiency over time; however, size remains positively correlated with efficiency. Despite the rapid expansion of asset sizes and evidence of diminishing efficiencies of scale, larger REITs still enjoy comparative advantages over smaller REITs in both revenue (production) and costs. We also find evidence that post-recession efficiencies exceed pre-recession efficiencies, and we document modest evidence of the “weeding-out” of inefficient enterprises during the market downturn. The results suggest, through a diverse set of measures, that additional efficiency opportunities for REITs may be achievable through continued growth and consolidation.

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Notes

  1. The Great Recession (https://www.federalreservehistory.org/essays/great_recession_of_200709)

  2. According to the REIT Industry Financial Snapshot (https://www.reit.com/data-research/data/industry-snapshot) as of September 2017, equity REIT market capitalization reached an all-time high of $1.043 trillion, with the average REIT having a market cap of $5.5 billion.

  3. The results from our stochastic frontier model for cost efficiency indicate a λ = 1.83. As noted by when computed as mean relative efficiency, this figure is inverted, 1/λ, to give 0.54 on a scale of (0,1). See p. 438 of Meeusen and van den Broeck (1977).

  4. Consistent with Xu and Ooi (2018), Sun, Titman, and Twite (2015) find that valuations of large REITs declined more than those of smaller REITs during the financial crisis and REIT market downturn of 2007–2008.

  5. Feng et al. (2011) provide a thorough review of the literature on equity REIT research.

  6. Some REIT-year observations do not have complete income statement data recorded in S&P Global Market Intelligence. When available, the income statement data was supplemented with Compustat data as a secondary source.

  7. Self-Advised and Self-Managed binary variables are set according to the date the firm elects self-advised or self-managed status.

  8. The monthly and annual index data for the FTSE Nareit U.S. Real Estate Index Series is available at the following: https://www.reit.com/data-research/reit-indexes/annual-index-values-returns. The Index values used are as follows: 3002.97 (December 31, 2000); 8185.75 (December 31, 2006); 5097.46 (December 31, 2008); 14,650.51 (December 31, 2015).

  9. Rogers (1998) utilizes the Aigner et al. (1977) and Meeusen and van den Broeck (1977) stochastic frontier model in examining cost, revenue and profit frontiers of commercial banks.

  10. Badunenko et al. (2008) demonstrate that input prices, which are generally unknown at the firm level, are not necessary to estimate allocative efficiency.

  11. The elasticity measure shows negative elasticities disappearing at a value of ln(Total Assets) of approximately 2.5, corresponding to Total Assets of $12.18 million. While this result is confounded by the time component of the panel data, the small REIT size at which this happens suggests that any REITs on the downslope of the inverse U-shaped cost curve occurred early in the study period. Numerical analysis also suggests that all REIT observations had elasticities less than one (1), suggestive of economies of scale.”

  12. Devaney and Weber (2005) utilize a directional output distance function to construct a risk/return frontier. Their process identifies firm performance as a production process in which each REIT produces a desirable output (return) and an undesirable output (risk) using inputs of managerial effort and financial capital.

  13. These results compare closely to the significant decline in the number of REITs during the Downturn Market (2007–2008) shown in Panel C of Table 2 as well as the significant increase in REIT total assets during the Downturn Market (2007–2008) shown in Panel B of Table 3. Mulherin and Womack (2015) also document evidence of 22 mergers in 2006 alone. To limit our sample to REITs which were merged or otherwise eliminated during the Downturn Market (2007–2008), we require a REIT to report in 2016 to be considered a Survivor or Non-Survivor.

  14. The 20 Non-Survivors have the following SNL Institution Keys: 102919, 102,986, 102,987, 103,005, 103,147, 103,158, 103,168, 103,198, 103,627, 113,002, 4,002,566, 4,076,915, 4,082,048, 4,089,416, 4,089,963, 4,092,926, 4,093,258, 4,093,270, 4,106,641, and 4,110,503.

  15. For completeness, a non-survivor is a REIT which meets condition (A) but fails to meet condition (B) because it was acquired otherwise ceased operations in 2007 or 2008.

  16. When we examine the cost efficiencies for all individual REITs in the sample by year, we find that cost efficiency measures, λ_(i,t), range between a minimum of 0.389 to a maximum of 0.952.

  17. Malhotra et al. (2019) use data from Mergent Online to evaluate economies of scale in REITs over the period 2012–2016. They find that REIT operating expenses increase less than proportionately with increases in total assets.

  18. Models using both Total Asset Value and Market Capitalization to jointly measure the size of a REIT failed due to convergence issues.

  19. When we examine revenue efficiency for each individual REIT in the sample by year, we find that revenue efficiency measures, λ_(i,t), range between a minimum of 0.322 to a maximum of 0.984.

  20. When size is measured by market capitalization the results of the frontier models show that REIT total revenue is increasing at an increasing rate as REITs grow, but again the estimated η constants are negative and imply that the level of revenue efficiency of the REIT industry is decreasing over time.

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Acknowledgments

The data for this project is funded by the Robert W. Warren Chair of Real Estate at Mississippi State University. The authors appreciate comments and suggestions provided by Erik Devos, Zifeng Feng, David Harrison, Bennie Waller, and seminar participants at the 2018 American Real Estate Society Annual Meeting, the 2018 Southern Finance Association meeting, and the 2019 American Real Estate Society Annual Meeting. All errors remain the property of the authors.

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Highfield, M.J., Shen, L. & Springer, T.M. Economies of Scale and the Operating Efficiency of REITs: A Revisit. J Real Estate Finan Econ 62, 108–138 (2021). https://doi.org/10.1007/s11146-019-09741-9

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Keywords

  • REITs
  • Economies of scale
  • Operating efficiency

JEL Classification

  • D22
  • D24
  • G30
  • R30