Available Short Term Rental Data: The Need for More Spatial Research
Bibliographic record
Abstract
This application paper highlights the need and opportunity for research related to short term rental (“STR”) activity. The paper explores the importance of STRs relative to our understanding of the contemporary development of cities and neighborhoods. It does this by surveying the existing research literature on STRs and summarizing recent debates regarding the potential need for STR regulation. Part of this policy-focused discussion centers on Airbnb, the STR industry leader, and the available datasets related to its evolving operations. The paper also explores the insights that can be gained from STR research by presenting a case study of Airbnb’s footprint in Toronto. This regional analysis provides insight into the power of a joint consideration of STR activity together with broader urban-economic indicators, which speaks to the novel research opportunities that analysis of STR data makes possible. In sum, this paper argues that STR research is an appropriate target for the applied geography research community because of the practical need for business and public sector leaders to have a better understanding of the dynamics of this emerging industry, and the opportunity for new insight into urban-economic development more broadly that STR research makes possible.
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 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 | 0.017 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".