Higher Interest Rates Hinder Hotel Price Momentum
Bibliographic record
Abstract
The price performance of hotels by region was mixed in the third quarter of 2023. The Midwest, Mid-Atlantic, Mountain, and West South Central posted positive quarterly and year-over-year results. In contrast, New England and the South Atlantic regions struggled, and the Pacific region recorded only slight year-over-year gains. Hotels in non-gateway cities continued to outperform those in gateway cities. Transaction volume fell year-over- year and quarter-over-quarter. However, volume is up this quarter for large hotels and hotels in gateway cities. Based on moving averages, a “hold” signal is indicated for large hotels, with a “buy” signal for small properties. That said, the situation calls for keeping your gunpowder dry, given that the standardized prices of both large and small hotels have softened. Hotel interest rates for both Class A and Class B and C hotels rose about 3.6 percent this quarter and approximately 4 percent year over year, even as credit spreads tightened and the delinquency rate on hotel loans fell this quarter. Looking at commercial property categories, the delinquency rate on hotels is now lower than both retail and office properties. As in the prior period, the borrowing costs still exceed the return on hotels. Expect to see an uptick in the price of large hotels in the next quarter, while prices for small hotels falter, based on our leading indicators of hotel price performance. This is volume 12, number 3 of the hotel indices series.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.021 |
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".