Long Small Hotels, Short Large Hotels
Bibliographic record
Abstract
The price of hotels showed particular strength in the Mid-Atlantic, Pacific, and West South Central regions, while other regions experienced positive growth albeit at a slower rate relative to the previous period. Hotels in non-gateway cities posted higher quarterly gains relative to those in gateway cities, increasing 2 percent compared to a drop of 4 percent for gateway hotels. With regard to yearly prices, hotels in non-gateway cities increased 21 percent as compared to a rise of 1 percent in gateway cities. The transaction volume on all hotel transactions (both large hotels and small hotels combined) mimicked the previous quarter’s sales volume. The prices of small hotels appear undervalued (while those of large hotels appear overvalued), based on both 3-year and 5-year moving averages. Mortgage volume for hotels fell slightly for the most recent quarter, while the the cost of hotel debt financing has continued to rise quarter to quarter, as well as year over year. In short, lenders are requiring relatively more compensation for hotel loans relative to the 10-year risk-free rate due to increased perceived risk. The rise in borrowing cost will dampen enthusiasm for undervalued hotel properties, since our EVA and SVA metrics continue to indicate that the cost of borrowing exceeds the return for hotels. Looking toward the next quarter, our near-term leading indicators of hotel price performance indicate that we should expect slower or declining price momentum for large hotels but not necessarily for small hotels.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".