The Geography of Real Property Information and Investment: Firm Location, Asset Location and Institutional Ownership
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
Abstract Using a sample of Real Estate Investment Trusts (REITs), we show that institutional investors exploit location‐based information asymmetries by overweighting firms headquartered locally and those with greater economic interests in the investor's home metropolitan statistical area (MSA). This asset allocation strategy is associated with superior portfolio performance. In a difference‐in‐difference‐in‐differences analysis of investor headquarters relocations, we find that investors tend to increase their ownership of REITs that have property holdings in the market to which the investor relocates. Our findings highlight the importance of understanding the relation between information advantages and the geography of firm's operations, as well as the implications on ownership patterns and portfolio construction.
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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) | 0.000 | 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 | 0.001 |
| 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 it