Second Quarter 2021: Are We There Yet?
Bibliographic record
Abstract
United States hotel prices have rebounded above their statistical lower bound in all regions except the Mountain states, signaling a return toward their pre-pandemic level—although we are not quite there yet. Prices of hotels in gateway cities rose 6 percent, while hotels in non-gateway cities climbed almost 7 percent on average this quarter. Transaction volume also increased for both large hotels and small hotels, with large hotels rising 79 percent and small hotels, 57 percent on a quarter-over-quarter basis. Our moving average trendlines and standardized unexpected price performance metrics indicate that large hotels (those over $10,000,000) are fairly priced, while small hotels are opportunistic buys at best. The cost of debt financing for hotels declined approximately 17 basis points (bps) this quarter with interest rates currently at 5.55 percent for Class A hotels and 5.75 percent for Class B and C hotels. However, the spread in interest rates between hotels and other commercial real estate (relative risk premium) has widened slightly, from 211 basis points in March to 215 basis points at this writing. The total risk associated with hotel REITs has also increased relative to the total risk for other major types of commercial real estate REITs. This indicates that the capital market still perceives hotels to be relatively riskier, although the delinquency rate on hotel loans (currently at 14.27%) continues to decline from its high of 24.3 percent (June 2020) toward its pre-pandemic level of around 1.51 percent (2019Q4). Our economic value added (EVA) and new shareholder value added (SVA) metrics are negative, indicating that borrowing costs exceed operating performance. As a consequence, any deals done will be based on long-term price appreciation for the deal to pencil. Looking toward the next quarter, our leading indicators of hotel price performance indicates that positive price momentum should continue to exist for both large and 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.025 |
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".