The Spatial-Temporal Distribution of Airbnb and its’ Potential Impact on the Rental Market: A Case Study of City of Toronto & City of Vancouver
Bibliographic record
Abstract
The digitization of businesses has changed the way we purchase goods and services. This has been amplified in the last year due to the pandemic and social distancing measures. Demand for services such as ridership, quality goods, and short-term rentals has increased relative to traditional services. This has led to the rapid expansion of digital platforms such as Amazon, Uber, and Airbnb. The private short-term accommodation market has raised many public policy questions some of which revolve around how these markets should be regulated and taxed to mediate their impact on the housing and rental market. This study focuses on analyzing the spatiotemporal distribution of Airbnb and its’ potential impact on the long-term rental market in City of Toronto and City of Vancouver in Canada. There are two main findings; first, Airbnb listing are shown to be concentrated in areas closer to the city center. Second, there is a positive correlation between the presence of Airbnb and rental rates. These findings align with previous studies and further contribute to the growing body of research on the topic of platform economy and short-term rentals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".