How Fast Do New Hotels Ramp Up Performance?
Bibliographic record
Abstract
Using an event study methodology and data from 3,494 new entrants in the U.S. lodging industry, this paper examines how quickly new hotels ramp up their performance after opening. For the years 2006 through 2009, new entrants entered with average daily rates (ADRs) above incumbents, and took seven quarters (1.75 years) to ramp up occupancies to the levels of comparable incumbent hotels. These averages include performance behavior of brand-managed, franchisee-managed, and unaffiliated independent hotel new ventures compared with incumbent hotels in similar geographic markets, locations, and price segments. Overall, new hotels reached comparable revenue per available room (RevPAR) performance by the second quarter of the second year of operation. RevPAR ramp-up was earlier for brand-managed hotels (first quarter of the second year), an outcome primarily attributable to higher occupancies and lower initial ADRs. Independent hotels took substantially longer than other new entrants to reach the RevPAR performance of existing hotels. Based on the faster ramp-up of new branded properties, the chief implication is that hotel developers should consider affiliating with a brand for quicker stabilization and short-term gain. The speed of hotels’ ramp-up also calls into question the conventional view that new hotels represent a relatively risky investment.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".