Public Versus Private Real Estate Equities: A More Refined, Long-Term Comparison
Bibliographic record
Abstract
In this article we compare public and private real estate equities. In so doing, we control for three of the main differences between these investment alternatives: property-type mix, leverage and appraisal smoothing. With these two restated indices, we then run tests to determine in a statistical sense whether the restated means and volatilities of the two series were different from one another. The clear answer is that they were not. The results of the statistical tests combined with the fact that the average difference between the two (restated) return series has substantially narrowed (to approximately 60 basis points) in the more recent (1993–2001) period jointly suggest a seamless real estate market in which public- and private-market vehicles display a long-run synchronicity. This has important implications for portfolio management. First, public- and private-market vehicles ought to be viewed as offering investors a risk/return continuum of real estate investment opportunities. Second, while the “platform” did not matter in terms of observed return characteristics, the platform may matter with regard to liquidity, governance, transparency, control, executive compensation and so forth; an apparent clientele effect hints at these issues being valued differently by large and small investors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".