Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 0Sharing Economy and PlatformsHousing Market and EconomicsHousing, Finance, and Neoliberalism

The DOI matches its Crossref record; 1 of the 2 references it cites were found in a registry or an index, and 1 are not checked yet; none of the 26 sources its claims cite is checked for support yet.

The Unintended Consequences of Short-Term Rental Regulation: Evidence from New York City's Local Law 18

Open access
Dong, Yuzi·2026·Advances in Economics, Management and Political Sciences

Abstract

The rapid expansion of short-term rental platforms such as Airbnb has sparked intense debates about its impact on urban housing affordability. The rapid development of the short-term rental market in New York has brought great economic pressure to the local homeless residents. This paper uses the implementation of Local Law 18 in New York City in September 2023 as a natural experiment to investigate whether short-term rental regulations can effectively achieve stated goal of improving long-term residents' housing affordability. Through the analysis of internal data from Airbnb and housing market statistics, this study found that although the regulation successfully reduced approximately 90% of short-term rental supply, the expected improvement in long-term housing affordability was not achieved. The research results indicate that the housing affordability crisis in major cities is mainly driven by fundamental supply constraints rather than short-term rental platforms. This paper provides empirical evidence on the effectiveness of regulation and proposes evidence-based policy alternatives.

Checked 2026-11-13 · 1 reference not checked yet

Overview

CoChat Summary

TL;DR

Using New York City’s Local Law 18 as a natural experiment, this paper finds that the regulation cut legal short-term rental listings by over 90% but did not improve long-term housing affordability. It concludes that the city’s affordability crisis is driven mainly by fundamental housing supply constraints rather than short-term rental platforms.

Key findings

  • Local Law 18 reduced short-term rental listings from approximately 38,500 to 3,000, a decline of about 92%.
  • Despite that drop, Manhattan median rent rose from $4,200 to $4,700 (+12%) by July 2025, while Manhattan’s vacancy rate remained near 2.45% (versus 2.50% pre-law), far below the 5% threshold the paper calls a housing-crisis warning line.
  • Short-term rentals made up only about 2% of NYC rental housing; even reclassifying all of them would raise Manhattan’s vacancy rate only from 2.45% to approximately 2.55%, so their removal had negligible effect on long-term rental supply.
  • Hotel prices rose after the regulation: the average nightly rate increased from $265 to $283 (+7%), with busy-season rates reaching $417, while annual tourist arrivals rose from 63M to 68M (+8%).

Methods

  • Natural experiment/case study of New York City’s Local Law 18, implemented in September 2023, comparing pre- and post-regulation housing market outcomes.
  • Uses “Airbnb internal” data and government/housing market statistics, including Inside Airbnb, CoStar, and NYC Tourism Board data, to analyze short-term rental supply, rents, vacancy, hotel prices, and tourism.
  • Also reports a fixed-effects panel regression on around 16,000 whole residences from July 2023 to January 2024, with average daily rate as the dependent variable and the number of competing listings as the main independent variable.

Limitations

  • The paper states that reliance on publicly available “Airbnb internal” and government data may have measurement limitations; Airbnb has not released official data, and web scraping may miss or double-count listings.
  • The paper states that the post-regulation period is short (about two years), so long-term effects on investment, new construction, and community composition may not yet be observable; it also notes the NYC-only focus and unique local characteristics limit generalizability, and that Airbnb-hotel substitution was not fully considered.
  • Evident from the text: the main housing-market analysis is largely a descriptive pre/post comparison, so causal interpretation depends on the natural-experiment framing.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.54254/2754-1169/2026.34798

Cites

1 of 2 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 Valid · 2 Not valid · 21 Not checked · 10 Own results and interpretations

9 more claims

Create a free account to see all 33 claims and every source verdict.

See all claims — free account
  • Probably not a substantial claim. 11
  • It may state two separate claims as one. 13
  • The quote only partly states this claim. 1

Create a free account to see these claims.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

2 of 2

Citations

Cites

Show all 1 references

Research works printed but not linked: 6

  1. Voltes-Dorta, A. (2024). Price competition among short-term Airbnb listings in New York City following Local Law 18. Tourism Economics, 30(8), 2047-2051. Retrieved from https: //doi.org/10.1177/13548166241234567 · DOI
  2. Martí-Ochoa, J., Martín Fuentes, E., Ferrer Rosell, B. (2024). Review of the scientific literature on Airbnb: Past, present and future. Journal of Tourism Analysis, 31(2), 75-112. Retrieved from https: //doi.org/10.1108/jta-2024- 0012 · DOI
  3. O'Connor, P., Pung, J. M. (2025). Exploring the impact of short-term rental regulations on housing availability and affordability: A case study and research agenda. In Information and Communication Technologies in Tourism 2025 (pp. 275-285). Springer. Retrieved from https: //doi.org/10.1007/978-3-031-12345-6_25 · DOI
  4. Hidalgo, A., Velazquez, F. J. (2026). The economic impacts of short-term rentals and regulations: A literature review. Documentos de Trabajo (FEDEA), (2), 1-55.
  5. Benítez-Aurioles, B. (2021). A proposal to regulate the peer-to-peer market for tourist accommodation. International Journal of Tourism Research, 23(1), 70-78. Retrieved from https: //doi.org/10.1002/jtr.1234 · DOI
  6. Zhang, C. (2025). Essays in leveraging machine learning and big data analytics in Airbnb for forecasting and pricing (Doctoral dissertation, King's College London). King's College London Research Portal. Retrieved from https: //kclpure.kcl.ac.uk/portal/en/studentTheses/essays-in-leveraging-machine-learning-and-big-data-analytics-in- a/

Cited by

Still being checked.

Metadata

Authors
Dong, Y.
Affiliations
Beijing Luhe International Academy
Venue
Advances in Economics, Management and Political Sciences
Published
2026-06-23
Volume
276
Issue
1
Pages
142-148
ISSN
  • 2754-1169 (ISSN-L)
  • 2754-1177
OpenAlex
W7165652512
Cited by
0
References
1
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
Advances in Economics Management and Political Sciences (publishedVersion, cc-by)
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Housing Market and Economics
  • Social Sciences › Economics, Econometrics and Finance › Finance › Housing, Finance, and Neoliberalism
Keywords
short-term rental regulation, Airbnb, housing affordability, housing supply constraints, long-term residents, unintended consequences of regulation, urban housing policy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-11-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-11-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The Unintended Consequences of Short-Term Rental Regulation: Evidence from New York City's Local Law 18. https://app.cochat.ai/papers/10.54254/2754-1169/2026.34798
The paper
Dong, Y. (2026). The Unintended Consequences of Short-Term Rental Regulation: Evidence from New York City's Local Law 18. Advances in Economics, Management and Political Sciences. https://doi.org/10.54254/2754-1169/2026.34798

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.