Do informed REIT market participants respond to property sector mispricing?
Bibliographic record
Abstract
Sector mispricing represents the deviation of current and long-run sector fundamentals indicating either over- or undervaluation. We focus on the response of informed market participants to property sector mispricing in the context of equity REITs. We argue that REIT market participants such as institutional REIT investors and analysts have an informational advantage due to their access to commercial real estate market data. As a result, they are expected to respond to property sector mispricing. Using a sample of 2,637 firm-quarters of pure play equity REITs over the period of 1993 to 2020, we find that sector mispricing indeed impacts the decision-making of informed REIT market participants. The more overvalued (undervalued) a property sector is, the more institutional investors behave as net sellers (buyers) for REITs with the respective property type specialisation in the next quarter. Similarly, property sector overvaluation (undervaluation) results in lower (higher) net buy recommendations by analysts for REITs in the respective sector in the next quarter. However, our results are driven by smaller REITs and REITs with higher growth options. The sensitivity of institutional REIT investors and analysts to property sector mispricing also varies across different states of trading and recommendations respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".