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Structural changes, market growth and productivity gains of the US real estate investment trusts in the 1990s

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Abstract

The 1990s were tumultuous times for the US Real Estate Investment Trusts (REITs) industry. Significant structural changes occurred during the decade, especially after the 1993 Revenue Reconciliation Act, which tremendously boosted the flow of funds into the system by allowing the participation of institutional investors in REITs. As a result, the industry experienced remarkable asset growth during the decade, with a large number of initial public offerings and substantial increases in market capitalization. Employing the Data Envelopment Analysis-type Malmquist index approach, this paper explores the impact of such environmental changes on productivity growth, efficiency change, and technological progress of REITs. Our results indicate that while efficiency of the REITs significantly increased, their average productivity declined and technology regressed during this decade. It appears that the typical REIT has failed to improve technically, but exerted substantial effort to catch up with the best practice ones relying mainly on aggressive growth strategies. However, our empirical results indicate that they might have overextended themselves as most began to suffer from diseconomies of scale.

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Notes

  1. The Economic Recovery Act of 1981 had created this tax advantage by authorizing property owners to use the vehicle of depreciation of their real estate as a tax shelter for other income. The 1960 act had mandated REITs to hire third parties to provide management and property leasing services. This restriction was lifted by the Tax Reform Act of 1986 by permitting REITs to own, operate and manage most commercial properties. As managers may have different economic interests than owners, it is believed that this law has created substantial agency problems for REITs [see Schulkin 1971], with adverse effects on their investment performance [see Solt and Miller 1985; Hsieh and Sirmans 1991; Howe and Shilling 1990].

  2. The UPREIT structure allows real estate partnerships to become a REIT without imposing a tax liability on the individual partners. After creating an UPREIT, the partners have an option to receive units representing their ownership interests in the new trust. The properties’ original owners can avoid taxes by retaining their partnership interests rather than converting them to shares in the UPREIT [see Kleiman 1993 for more detail]. After the 1993 Act, the REIT shares were considered to be owned by beneficiaries rather than by institution, allowing pension funds to become REIT owners.

  3. Other reforms of the decade were complementary in nature. With the REIT Simplification Act of 1997, the REIT operations have been further liberated by the U.S. Congress. One last impediment remaining in the way of the REITs’ growth potential was in regard to distribution of earnings. The 1960 legislation had designed REITs as “pass-through enterprises” (to prevent double taxation); the law required that 95% of the rents from income property and/or interest income from mortgages had to be distributed to shareholders, further limiting the ability of REITs to retain earnings for future investments. The REIT Modernization Act (RMA) of 1999 finally reduced this dividend distribution requirement back to 90%.REITs initially had to distribute 90% of their taxable income to shareholders. This dividend payout requirement was later increased to 95% in 1980. The RMA of 1999 that became effective in 2001 reduced the dividend payout ratio back to 90%. This policy has provided the REITs with additional sources of income to retire some of their outstanding debt (Topuz et al. 2005).

  4. Since then, four additional REITs have been included in the S&P 500: Apartment Investment and Management, Equity Residential Properties, Plum Creek Timber and Simor Property Group.

  5. As the Malmquist index does not require the specification of a production or a cost function, it is relatively safe against specification errors that may be an issue for stochastic approaches. However, this index is susceptible to mixing of error term with inefficiency as all deviations from the frontier are deemed as inefficiency with no regard to data problems or pure luck.

  6. As REIT performance is not judged relative to some absolute standard, a best practice firm may not be fully efficient. Efficiency measures are relative in nature.

  7. For example for banking firms, the literature provides two main procedures, production approach and intermediation approach, to select appropriate inputs and outputs in DEA-based estimations. Production approach considers banks as firms producing services for customers such as performing transactions and processing documents. Therefore, inputs are measured by physical units, and outputs are measured by the number and type of transactions or documents processed over a given time period. Under the alternative intermediation approach, banks are viewed as the conduit of funds between depositors and borrowers. Banks thus incur labor, capital and loanable funds expenditures to transfer funds from those with surplus of funds to those with shortage of funds.

  8. It is also debatable among academicians as to whether REITs are stock investment or real estate institutions. However, recent studies [e.g., Ghosh et al. (1996); Bers and Springer (1997, 1998a, b) and Anderson et al. (2002)] exceedingly argue that REITs are real estate investments rather than stocks.

  9. Since REITs encountered phenomenal growth during the study period, any downward bias in asset values should be insignificant, i.e., the costs of the assets should be relatively current. Please, refer to Springer (1998) for a detailed discussion about the justification of possible REIT outputs.

  10. In order to control for input/output specification issues further, we have also used at least four other different combinations and our major results were sustained. Due to space issues, these results are not reported here but available from the authors upon request.

  11. The other assets category includes non-operational properties such as land for sale or under development, unconsolidated partnership or joint ventures, and all non-real estate assets.

  12. In order to prevent the adverse impact of inflation on our time-series analysis of productive performance, we adjust our production variables for “purchasing powers loss” using the 1989 prices.

  13. Actually, Anderson et al. (2002) use four inputs: interest expense, operating expense, general and administrative expense, and management fees, whereas Topuz et al. (2005) employ interest expense and operating expenses.

  14. There are 51 (26) such REITs that continuously exist until 1999 (1993).

  15. As a further caveat, the contamination of results with noise in the data resulting from the arrival of new entries could be more serious. As demonstrated by DeYoung and Hasan (1998), as compared to established peers, de novo firms are destined to have erratic behavior and lower efficiency in their early years, which may last as long as 9 years.

  16. The controversial coexistence of both technical regress and efficiency increase is explained theoretically with a graph in the Appendix. The REITs in our sample experienced a 14% downward shift in their CRS frontier and a 19% rise in their CRS efficiency (proximity to this frontier). This indicates that 14% of efficiency increase became possible with the shrinkage of the CRS efficient frontier. This in turn implies that REITs have achieved only 5% of the total trip towards the efficient frontier. If the VRS frontier against which pure efficiency measures are calculated also shrunk by 14%, with the same analogy, the net scale efficiency increase becomes only 3% and net pure technical efficiency increase becomes a decrease of 12%.

  17. As both technologies suggest similar productivity growth, the larger magnitude of technical regress with fixed technology actually implies greater importance of efficiency increase in driving productivity. Since the technological regress is the dominant source of the productivity decline, we reported only two indexes from the fixed frontier results. Other efficiency estimates (effch, pech, and sech) are available upon request from the authors.

  18. It may also be argued that the regulatory reforms may merely change the input mix. Unless REITs use a Leontief production function in which inputs are not substitutable, these regulatory actions merely force the REITs toward a (likely) less efficient input mix.

  19. The poor start in 1990 is attributed to overbuilding in office and apartment sectors, causing rising vacancies and reduced rents. In the retail sector, the blame was on Wal-Mart and other discounters as they marched into the turf of traditional retailers, all of which caused overreaction by investors.

  20. This influx of money attracted the highest number of new comers to the sector in the decade in those years, with 50 IPOs in 1993 and 45 in 1994.

  21. The hot market for REIT stocks in 1997 may have been a prediction of higher property values ahead. The strong involvement of institutional investors was also note-worthy since in 1996–1996, pension funds began setting up new business devoted solely to the management of REIT portfolios in 1996–1997. The trend of public securitization of real estate had become indelibly established by 1997 (Block 2002).

  22. We find 73% by dividing 30% by 41%, and 17% by dividing 11% by 41% in Table 6.

  23. The computation of Malmquist productivity for a multi-output/multi-input firm is clearly more difficult than for single output/single input firm as one needs to aggregate the multiple outputs into a “virtual output”, VO, and multiple inputs into a “virtual input”, VI, where tfp = VO/VI (Coelli et al. 2003).

  24. The 0.5% annual fall in productivity with proxies compares well with the 2% fall with the Malmquist tfpch index. However, with respect to the reference year 1989, the proxy measures imply a severe decline (30%) as compared to 1.5% fall with the Malmquist.

  25. For banking firms, Elyasiani et al. (1994), Berger and Mester (1997) and Isik and Hassan (2003c) also show that information contained in efficiency measures closely corresponds to that contained in standard financial ratios.

  26. We used non-parametric significance tests (based on a binomial distribution) to test if the changes in the numbers in the IRS, CRS or DRS columns are statistically meaningful. The results indicate that both IRS and DRS changes are statistically material.

  27. The dominance of DRS observed here, which confirms the nonexistence of scale economies for REITs, is in line with McIntosh et al. (1991) and Ambrose and Linneman (1998). Furthermore, Topuz et al. (2005) find that the majority (92%) of large REITs experience DRS, suggesting that REITs probably have been experiencing diseconomies of scale with the dramatic and ongoing merger and acquisition activities since 1995. In fact, Bers and Springer (1998a) report that the primary sources of the scale economies are management fees and general administrative expenses, both of which are smaller components of REIT total costs. It may be that little growth in size must have exhausted such small scale economies in the early years of the decade.

  28. Although introduced in 1999, the REIT Modernization Act (RMA) became effective in 2001, which is out of the scope of this study period. Hence, there are two important regulatory reforms after 1993 to consider (Revenue Reconciliation Act of 1993 and REIT Simplification Act of 1997). Thus, one may argue that it is hard to distinguish their differential impact on REITs’ performance. However, we treat 1993 as the year that separates post-deregulation era from pre-deregulation era. Thus, performance difference between these two periods is of main concern for our purpose. Besides, we also test the significance of annual performance difference with respect to the basis year (1989, the representative year of the pre-deregulation era), which could capture the separate effects of these two reforms. We thank our anonymous referee for raising these issues.

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Acknowledgements

The authors wish to thank James E. Payne (the Editor) and an anonymous referee for several helpful comments and suggestions. We would like to acknowledge the valuable support and insightful comments from Ali F. Darrat, Larissa Kyj, M. Kabir Hassan, Iftekhar Hasan, Anita Penathur, Roger M. Shelor and D. H. Bao as well as the conference participants in the Eastern Finance Association and International Financial Management Association annual meetings. The usual disclaimer applies; all errors are our prime responsibility.

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Authors and Affiliations

  1. John Massey School of Business PMB 4083, Southeastern Oklahoma State University, Durant, OK, 74701, USA

    John C. Topuz

  2. Department of Accounting and Finance, Rohrer College of Business, Rowan University, Glassboro, NJ, 08028, USA

    Ihsan Isik

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  1. John C. Topuz
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  2. Ihsan Isik
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Correspondence to John C. Topuz.

Appendix

Appendix

Efficiency measures are usually expressed as a percentage by using distance functions. To illustrate these terms, we use Fig. 1. Assume that a firm uses a single input (x) to produce a single output (y) and production technology is one of constant returns to scale (CRS frontier = R3). Note that technology V1 is one of variable returns to scale (VRS frontier); i.e., technology exhibits IRS to the left of t, DRS to the right of t and CRS at the point c. Technical efficiency for the firm operating at point b will be given by: te = ab/ad, which reflects the ability of a firm to achieve maximal output (d) from a given input (a). Because of imperfect competition, regulatory and market distortions, firms may not be operating at the most scale efficient (CRS) production level. Thus, relaxing the assumption of CRS and constructing a production frontier under variable returns to scale (VRS frontier = V1), one can compute the technical efficiency devoid of scale effects, which is dubbed pte. PTE at point b is measured by pte = ab/ac. Scale efficiency refers to a proportional increase in output generation if the firm can attain the optimum production level (point d), where there are CRS. Thus, se is given by: se = ac/ad. The te score is multiplicative, i.e., it is a product of pte and se scores: te = pte*se. Obviously, if the firm is fully scale efficient (se = 1), the distance cd disappears and te becomes equal to pte.

Fig. 1
figure 1

Definition of Malmquist index measures

Full size image

The Malmquist tfpch index, however, takes the following general form:

$$tfpch = techch \times effch = techch \times \overbrace {pech \times \sec h}^{effch}$$
(E1)

The terms techch stands for technological change, effch for technical efficiency change, pech for pure technical efficiency change and sech for scale efficiency change. To understand this decomposition, consider the example in Fig. 1, where the firm located at point b moved to point f between year t and year t + 1 but the estimated frontiers did not shift upward or downward (the CRS frontier at year t and year t + 1, is represented by R3, and the VRS frontier by V1). The subcomponents of the tfpch index are given below (where c denotes a CRS technology; v denotes a VRS technology):

$$techch\; = \left[ {\frac{{d_t^c \left( {x_{t + 1} ,\,y_{t + 1} } \right)}}{{d_{t + 1}^c \left( {x_{t + 1} ,\,y_{t + 1} } \right)}} \times \frac{{d_t^c \left( {x_t ,\,y_t } \right)}}{{d_{t + 1}^c \left( {x_t ,\,y_t } \right)}}} \right]^{0.5} = \left[ {\frac{{{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}}}{{{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}}} \times \frac{{{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}}}{{{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}}}} \right]^{0.5} $$
(E2)
$$effch\;\; = \frac{{d_{t + 1}^c \left( {x_{t + 1} ,\,y_{t + 1} } \right)}}{{d_t^c \left( {x_t ,\,y_t } \right)}} = \frac{{{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}}}{{{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}}}$$
(E3)

Note that tfpch = techch * effch. By moving from point b to point f, not only does the firm become less productive (tfpch < 1) but also less efficient (effch < 1), i.e., the firm’s output level decreases from b to f, given the same level of input (a), leading to a productivity fall, and the firm’s position falls further behind the efficient frontier (R3), leading to an efficiency decrease. In this case, the only reason for the productivity decline is the increased distance of the firm from the efficient frontier (efficiency decrease) rather than technical regress, as the frontier did not shift upward or inward (techch = 1).

We can further investigate the causes of efficiency decrease by decomposing it into:

$$pech\;\, = \frac{{d_{t + 1}^v \left( {x_{t + 1} ,\,y_{t + 1} } \right)}}{{d_t^v \left( {x_t ,\,y_t } \right)}} = \frac{{{{af} \mathord{\left/ {\vphantom {{af} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}}}{{{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}}}$$
(E4)
$$sech = \frac{{{{d_{t + 1}^c \left( {x_{ \times + 1} ,\,y_{t + 1} } \right)} \mathord{\left/ {\vphantom {{d_{t + 1}^c \left( {x_{ \times + 1} ,\,y_{t + 1} } \right)} {d_{t + 1}^v \left( {x_{t + 1} ,\,y_{t + 1} } \right)}}} \right. \kern-\nulldelimiterspace} {d_{t + 1}^v \left( {x_{t + 1} ,\,y_{t + 1} } \right)}}}}{{{{d{\kern 1pt} _t^c \left( {x_t ,\,y_t } \right)} \mathord{\left/ {\vphantom {{d{\kern 1pt} _t^c \left( {x_t ,\,y_t } \right)} {d_t^v \left( {x_t ,\,y_t } \right)}}} \right. \kern-\nulldelimiterspace} {d_t^v \left( {x_t ,\,y_t } \right)}}}} = \frac{{{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}}}{{{{af} \mathord{\left/ {\vphantom {{af} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}}} \div \frac{{{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}}}{{{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}}}$$
(E5)

The decomposition indicates that the efficiency decrease (effch < 1) for our firm is driven by decreases both in pure technical efficiency (pech < 1) and scale efficiency (sech < 1).

At a glance, our REIT industry analysis produces somewhat a paradoxical result, while the productivity of REITs falls, their efficiency rises. This may indeed occur when (techch < 1) and (effch < 1). However, the extent of regress in technology should be greater than the extent of the fall in efficiency. To see this, review once again the firm located at point b. By moving to point f, we saw that the firm became less productive. Assume that tfpch = 0.95, i.e., the firm now produces 5% less output with the same level of input (a). Also assume that the CRSt frontier (R3) shifted downward to CRSt+1(R1), and techch = 0.90, i.e., technical regress caused leading firms to produce 10% less output from the same amount of input (a). Although both technical regress and efficiency decrease are at play in this example, productivity decline results exclusively from technical regress (10%) as the virtual efficiency improves by 5%. Note that efficiency is measured as the proximity to the frontier. The 5% efficiency decrease is fully offset by the 10% downward shift in the frontier, resulting in net 5% efficiency rise (i.e., the proximity of inefficient firms to the frontier ultimately increases by 5% at the new location, f). For this event, the Malmquist indexes would be: \(tfpch = \left[ {{{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)}}} \right] <1\); \(techch = \left[ {{{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} {\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)*{{\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)}}}}} \right. \kern-\nulldelimiterspace} {\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)*{{\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)}}}}} \right]^{0.5} = 1\);\(effch = \left[ {{{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)}}} \right] <1\); \(pefch = \left[ {{{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right] <1\); \(sech = {{\left[ {{{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right]} \mathord{\left/ {\vphantom {{\left[ {{{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ad}}} \right. \kern-\nulldelimiterspace} {ad}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right]} {\left[ {{{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right]}}} \right. \kern-\nulldelimiterspace} {\left[ {{{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)} \mathord{\left/ {\vphantom {{\left( {{{af} \mathord{\left/ {\vphantom {{af} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{{ab} \mathord{\left/ {\vphantom {{ab} {ac}}} \right. \kern-\nulldelimiterspace} {ac}}} \right)}}} \right]}} <1\)

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Topuz, J.C., Isik, I. Structural changes, market growth and productivity gains of the US real estate investment trusts in the 1990s. J Econ Finance 33, 288–315 (2009). https://doi.org/10.1007/s12197-008-9026-6

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