Use of dynamic pricing strategies by Airbnb hosts
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a comprehensive analysis of dynamic pricing by Airbnb hosts. Design/methodology/approach This study uses attribute and sales information from 39,837 Airbnb listings and hotel data from 1,025 hotels across five markets to test different hypotheses which explore the extent to which Airbnb hosts use dynamic pricing and how their pricing strategies compare to those of hotels. Findings Airbnb is a unique and complex platform in terms of dynamic pricing where hosts make limited use of dynamic pricing strategies, especially as compared to hotels. Notwithstanding their limited use, hosts who own listings in high-demand leisure markets, manage entire places, manage more listings and have more experience vary prices the most. Practical implications This study identified a great need for Airbnb to encourage dynamic pricing among its hosts, but also warned of the potential perils of dynamic pricing in the sharing economy context. The findings also demonstrated challenges for hotel managers interested in actionable information related to Airbnb as a competitor. Originality/value This is the first Airbnb study to use a comprehensive set of data over a continuous period in multiple markets to look at a number of listing and host factors and determine their relation with dynamic pricing strategies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".