Converging Towards Normalcy
Bibliographic record
Abstract
Hotel prices continue to converge toward pre-pandemic levels. Gains posted were smaller relative to the previous quarter but higher year over year. Hotels in both gateway and non-gateway cities continue to exhibit positive performance, with hotels in non-gateway cities posting greater gains. Transaction volume continued strong for large and small hotels quarter over quarter and year over year, although the increase in volume was smaller in this instance than was the increase in the prior period. Our moving average trendlines indicate that large hotels are priced to buy, while small hotels are priced at market (priced fairly). Large hotels declined from their statistical high set last quarter, based on our standardized unexpected price (SUP) performance metric. In terms of financing hotels, mortgage financing volume continued to rise, as the cost of financing hotels slightly diminished this quarter. Among factors that have contributed to this situation are the relative risk premium, which has remained stationary this quarter, and a continued decline in the hotel delinquency rate. Hotel deals continue to look profitable, based on our economic value added (EVA) and shareholder value added (SVA) metrics. Looking toward the next quarter, our leading indicators of hotel price performance indicate that we should expect slower or declining price momentum for larger hotels but positive price gains for smaller 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".