Barcelona’s peer-to-peer tourist accommodation market in turbulent times
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
Purpose The purpose of this paper is to assess the impact of sociopolitical instability on the peer-to-peer market for tourist accommodation. Design/methodology/approach The author studies for the case of Barcelona the impacts of the events occurring in the past months of 2017, which consisted of a terrorist attack and the calling for a referendum on the independence of Catalonia, by fitting a fixed effects regression model to a data panel of Airbnb listings, using New York and Paris as a control group. Findings The results show that, after controlling for individual and time effects, listing reviews and revenues fall in the last quarter of 2017 and do not recover until the second quarter of the next year, in spite of a notable effort to decrease prices in the same period. They also indicate that peer-to-peer hosts react fast to demand shocks and as those from traditional markets. Originality/value This is the first study to evaluate the impact of terrorism or political uncertainty in the peer-to-peer market and the first to evaluate their combined effect in any market.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it