REIT Price-to-Net Asset Value, Performance, and Managerial Entrenchment
Bibliographic record
Abstract
This study examines P/NAV dynamics in the REIT industry, utilizing NAV estimates from SNL Financial and stock returns and distributions from CRSP. The findings reveal that REITs typically trade at a premium to their NAVs around IPOs, transitioning to a discount approximately 30 months later. The analysis also shows a positive association between P/NAV and future excess NAV returns, consistent with Chay and Trzcinka (1999). Furthermore, the results suggest high past excess stock returns indicate managerial ability, creating market expectations reflected in premiums. However, when controlling cross-sectional correlation, this relationship loses statistical significance. Additional findings indicate that REITs with positive excess NAV returns, longer public trading histories, or CEO replacements are likelier to trade at a premium to NAV. In contrast, those led by older CEOs, CEOs with longer tenures, or recent outside appointments tend to trade at a discount. Although the negative relationship between CEO tenure and P/NAV is not statistically significant, it supports Berk and Stanton’s (2007) assertion that entrenched CEOs contribute to NAV discounts.
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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 | 0.001 | 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".