Ruckus in the Rentals, Seeking New Arrangements: Remedying the Impact of Home-Sharing on Urban Noise
Bibliographic record
Abstract
Drawing underutilized residential assets into the tourism economy, home-sharing platforms create tremendous economic value for their owners and users. They also, however, generate negative externalities. This study examines the impact of home-sharing property use on an externality that has been increasingly understood to have adverse consequences for human health and well-being—noise. Based on empirical analyses using a large sample of transactions from a popular platform, we show that property use increases noise, which adversely affects neighbors. More interestingly, we show that the increase in noise is mitigated when property use is spatially or temporally concentrated. Our analyses reveal that a high concentration of property use can enhance the effectiveness of deterrence created through enforcement action against noise complaints. We further conduct empirically informed simulation experiments and propose a nudging algorithm that helps platforms mitigate noise externalities while also fulfilling user preferences and maintaining revenue growth. Contributing to the burgeoning literature on platform externalities, our work highlights the need for further research on potential complementarities between platform and regulatory governance and provides platforms with an alternative approach to the reputationally expensive guest penalties for addressing noise externalities.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".