Airbnb’s Success: Does It Depend on Who Is Measuring?
Bibliographic record
Abstract
Because individual listing data for Airbnb typically are not publicly available, private companies have emerged to estimate the performance of Airbnb listings. The implicit assumption of a growing number of academics, policymakers, and consultants is that Airdna’s performance measures are directly comparable with those of STR. We argue that Airdna’s measures of Occupancy, Average Daily Rate (ADR), and Revenue per Available Room (RevPAR) do not conform to industry standards and exhibit significant bias. We expand available evidence by explicitly quantifying the sources and magnitude of the biases for Airdna’s performance measures for Airbnb listings. Using Airdna’s individual listing data for Virginia between the first quarter of 2015 and the 4th quarter of 2019, we find, on average, Airdna’s performance measures for Occupancy, ADR, and RevPAR were biased upward by 60 percent, 78 percent, and 179 percent, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".