What Airbnb Host Listings Influence Peer-to-Peer Tourist Accommodation Price?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recognizing that the pricing strategy of the newly emerging online shared accommodation industry would be different from that of the traditional hotel industry, this study attempted to identify the variables that are the main determinants of the peer-to-peer tourist accommodation price. Using a data set of Airbnb accommodation listings for Toronto, the study established a relationship between room pricing and various listing variables and identified a reduced number of listing attributes that influence the room price significantly. Focusing on a reduced number of important variables, Airbnb hosts can not only increase average profit but would also give tourists a better rental experience. Along with traditional multiple regressions approach, the study also applied two different approaches and found that the analysis of hedonic pricing using nonlinear and nonparametric approaches is quite promising.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it