A Structural Model for Net Rental Income in the U.S. Leasing Industry
Bibliographic record
Abstract
I estimate a theoretically and statistically satisfying model to account for Net Rental Income (NRI) for one of the largest Real Estate Investment Trust companies (REIT) in the U.S. I claim that I have found an accurate method to forecasts the direction and dollar amount of NRI in the apartment industry in The U.S. that can be extended to the remaining branches of the leasing industry. The variables that together account for ninety seven percent of the variation in NRI for this apartment company are, one-period time lag of lease renewals, the Federal Funds interest rate end of month, total gross potential of the company, total concessions, two-period time lag of move-ins, the ratio between total non-farm employment and total construction permits authorized, the inventory of houses in the U.S, one-period time lag of move-outs and this REIT apartment units occupied.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".