The Financial Performance of Green Reits Revisited
Bibliographic record
Abstract
Executive SummaryThe aim of this paper is to compare the financial performance of “green” and “non-green” U.S. REITs from January 2010 to February 2016 using risk-adjusted performance measures based on multi-factor models. First, we use performance measures (including the generalized Treynor ratio) able to capture the variety of systematic risk sources related to real estate. Second, we implement unbiased estimators to correct for the econometric bias induced by errors-in-variables (EIV) in asset pricing models. Third, to check the robustness of our results, we apply the methodology of Getmansky, Lo, and Makarov (2004) to deal with the problem of illiquidity. With these different adjustments, we analyze the relative performance of green U.S. REITs. Our results show that non-green U.S. REITs tend to perform better during this period than green REITs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".