Bibliographic record
Abstract
The price of large hotels fell by .25 percent, while that of smaller hotels increased 3.3 percent this quarter. On a regional basis, the MidAtlantic had the best quarterly gains, with the Pacific region also doing well, while the Midwest suffered price declines. Hotels in both gateway and non-gateway cities continue to post positive performance, with greater gains for hotels in non-gateway cities. Transaction volume declined this quarter (from the previous quarter), although it was still stronger relative to the same quarter last year. Our moving average trendlines indicate that both large and small hotels are priced to buy. Large hotels continue to decline, while smaller hotels are rising, based on our standardized unexpected price (SUP) performance metric. In terms of financing hotels, mortgage-financing volume continued to rise, although the cost of financing hotels rose sharply this quarter. The relative risk premium has remained stationary this quarter, although the hotel delinquency rate has declined along with the riskiness of hotels compared to other major types of commercial real estate. Hotel deals continue to look profitable based on our economic value added (EVA) and shareholder value added (SVA) metrics, although they are nearing breakeven. Looking toward the next quarter, our leading indicators of hotel price performance indicate that in the near term we should expect slower or declining price momentum for both large and small hotels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.542 | 0.293 |
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".