A Comparative Analysis of R.E.I.T.s, R.E.O.C.s and P.R.E.O.C.s Using a Stochastic Frontier Approach
Bibliographic record
Abstract
Although the first real estate investment trust (R.E.I.T.) was created in 1960s, according to the latest data of 2018, only 13 out of 28 European countries had such systems on their stock-exchange. Many economists have published detailed studies stating the advantages of R.E.I.T.s, however, the developing part of Europe is still slow to react with legislative initiatives. This article extends the existing research on R.E.I.T. efficiencies and compares them to private real estate operating companies (P.R.E.O.C.s) as well as real estate operating companies (R.E.O.C.s) across the U.S., Canada and the European Union by using a stochastic frontier, panel-data models of translog cost functions while trying to identify whether a significant benefit arises from different corporate structures. The results confirm that out of 666 companies under consideration, all types of real estate (R.E.) firms achieve economies of scale. Furthermore, in the time period of 2014–2016, REITs on average were less reliant on short-term debt, they had a lower debt-to-equity ratio, were more efficient at managing costs in three stochastic translog models and partially in fourth, had a stronger economy of scale effect when their assets size increased, and remained competitively profitable though were outperformed in the profit and revenue area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".