Public Versus Private Real Estate Equities: A More Refined, Long-Term Comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it