UNDERSTANDING SHORT-TERM RENTAL DATA SOURCES – A VARIETY OF SECOND-BEST SOLUTIONS
Bibliographic record
Abstract
Purpose – This paper aims to identify major supply data sources for short-term rental market research and to provide their advantages and limitations. Methodology – In the paper a grounded approach was used based on a literature review. This review comprised two steps with the first being the query in major databases that was supplemented by academic search engine that resulted in 170 articles. The second step was to investigate the papers’ methodological sections to identify characteristics and limitations of all data sources. Findings – This study identifies three major data sources for the short-term rental market: web scraping with the use of self-made bots, Inside Airbnb and Airdna. A majority (e.g. 74% of papers using Airdna as a source) did not mention any limitations and provide no discussion about the data source, while the remainder gave only superfluous information about possible limitations of its use. Their characteristics and limitations are extensively discussed using a proposed framework that consists of three levels: intermediary, web scraping, and source-specific. Contribution – Very limited number of studies have focused on the short-term rental data sources and this is the first one that discusses advantages and limitation of their use. This paper may be of help to academics or professionals in identifying the right source of data to suit their technical knowledge, financial and technical resources and research areas.
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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 | 0.050 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.021 | 0.041 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".