Competitive Hotel Pricing in Uncertain Times
Bibliographic record
Abstract
This analysis of the pricing (ADR), demand (occupancy), and revenue (RevPAR) dynamics in the U.S. hotel industry for the period 2001 through 2007 demonstrates the potentially negative consequences of attempting to maintain market share by offering prices below those of direct competitors. This seven-year study examined the outcomes of pricing behavior on total rooms revenue and occupancy for hotels and their competitors in both bad times (2001-2003) and good (2004-2007). The results are the same in both periods. Hotels that offer average daily rates above those of their direct competitors experienced lower occupancies compared to those other hotels, but recorded higher relative RevPARs. For 67,008 hotel observations, this pattern of demand and revenue behavior was consistent for hotels in all market segments, from luxury to economy. Overall the results suggest that the best way to have better revenue performance than your competitors is to have higher average rates. The findings suggest that lodging demand may be inelastic in local markets, and hotel operators may wish to resist the pressure to undercut competitors when possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".