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Cited by 2Housing Market and EconomicsSharing Economy and PlatformsHousing, Finance, and Neoliberalism

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index; none of the 16 sources its claims cite is checked for support yet.

Housing versus home sharing: the causal impact of short-term rental regulation on residential rents

Open access
Wachsmuth, David, St-Hilaire, Cloé·2026·Regional Studies
DOI 10.1080/00343404.2026.2670360
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Abstract

What is the relationship between short-term rental (STR) activity, STR regulations and residential rents? Using the staggered introduction of principal-residence restrictions in Canadian urban areas, we estimate the causal impact of STRs on rents in a time-varying difference-in-differences model. We find that principal-residence regulations that reduce STR activity cause rents to decline in subsequent years, demonstrating that STR activity increases rents. Additional analyses show that these effects propagate across neighbouring jurisdictions, consistent with adjustments in metropolitan housing markets rather than purely localised policy impacts. Further descriptive analysis decomposes this impact into separate supply, demand and rental price stickiness channels.

Overview

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TL;DR

Using staggered municipal principal-residence restrictions across Canadian urban areas, this paper finds that short-term rental (STR) activity causally increases residential rents: regulations that reduce STR activity cause rents to decline. The effects operate mainly through commercial STR supply and rental price stickiness, with weaker evidence for a home-sharing demand channel.

Key findings

  • Principal-residence restrictions reduce rents: treated neighbourhoods’ log average rents are 0.096 SD lower than the no-regulation counterfactual (ATT = −0.096, SE 0.021, p < 0.001). The same models show declines in FREH listings (−0.251 SD) and non-FREH listings (−0.207 SD), while STR prices are statistically indistinguishable from zero (−0.034, SE 0.035).
  • Effects grow over time and spill over: in the year after regulations, rents were on average CA$24 lower (1.7%); by 2023, rents in regulated neighbourhoods were CA$55 (3.3%) lower, and rents in adjacent unregulated areas were CA$40 (3.0%) lower. The authors estimate this saved Canadian renters CA$192.4 million in monthly rent payments in 2023.
  • Spillover and residualisation analyses support metropolitan-scale adjustment: untreated neighbourhoods adjacent to treated areas show negative rent effects after about two years (ATT = −0.060, p < 0.1) and declines in STR listings; after removing region-by-year variation, the rent ATT remains negative and significant (ATT = −0.019, SE 0.003, p < 0.001).
  • The RE-ESF decomposition finds strong support for the supply channel (FREH_change, p < 0.01) and weak support for the price-stickiness channel (price_change, p < 0.1); the demand channel (non_FREH_change) has the hypothesised direction but a 90% confidence interval crossing zero. Pre-pandemic, STR variation accounted for an average 2.6% of the annual increase in total rent paid; in 2020 this dropped to −5.5%.

Methods

  • Time-varying difference-in-differences using Callaway and Sant’Anna’s (2021) group-time ATT estimator, with staggered municipal STR principal-residence restrictions as treatment. Treatment is assigned if a restriction was in place by 1 January of the year; rents are measured in October and STR variables in September.
  • Data cover Canadian urban areas: CMHC Rental Market Survey rents and vacancy rates (2015–2023), AirDNA STR listings and nightly prices (2017–2022), Canadian Census controls, and a hand-compiled STR regulation database. The main dataset includes 798 neighbourhoods in British Columbia, Ontario, New Brunswick and Quebec; 38 cities implemented restrictions, covering 309 neighbourhoods.
  • Supplementary random-effects eigenvector spatial filtering (RE-ESF) regression decomposes effects into supply (FREH listings), demand (non-FREH listings) and price-stickiness (nightly STR price) channels. Parallel trends are tested via exposure-length ATTs; spatial spillovers and residualised rent models are used as robustness checks.

Limitations

  • The paper states that no single clustering level is unambiguously correct because treatment arises from municipal, sub-municipal and partial-jurisdiction regulations; robustness checks use unclustered and municipality-clustered standard errors.
  • The RE-ESF pathway analysis is described as exploratory and vulnerable to endogeneity on its own; the authors rely on the DiD for the overall causal claim. The non-FREH demand channel is not statistically significant, and STR prices are treated as exogenously determined by tourism demand, hotel competition and housing quality—a simplifying assumption the authors defend but note is not strictly true.
  • Evident from the text: the analysis uses only Airbnb/AirDNA data, excluding other STR platforms, so STR revenue is underestimated; rent data cover purpose-built rentals only, and the CA$8.8 billion total rent figure assumes all tenants’ average rent equals that of purpose-built rental tenants. The authors also note that long-run effects through new residential investment are not captured in the short-run model.

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

Integrity signals

Registry

Crossref · 10.1080/00343404.2026.2670360

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Image 1
CC-BY-NC-ND · From the PDF
Image 2
CC-BY-NC-ND · From the PDF
Image 3
CC-BY-NC-ND · From the PDF
Figure 1 (A) Average monthly rents for all purpose-built rentals in Canada, 2015-23; (B) active listings and frequently rented entire-home (FREH) listings by region of Canada (monthly average); and (C) active short-term rentals (STRs) as a share of all dwellings by community size in Canada (per community monthly average).
Figure 1 (A) Average monthly rents for all purpose-built rentals in Canada, 2015-23; (B) active listings and frequently rented entire-home (FREH) listings by region of Canada (monthly average); and (C) active short-term rentals (STRs) as a share of all dwellings by community size in Canada (per community monthly average).CC-BY-NC-ND · From the PDF
Figure 2 Directed acyclic graph (DAG) showing hypothesised causal pathways between short-term rental (STR) activity, STR regulation and rent.
Figure 2 Directed acyclic graph (DAG) showing hypothesised causal pathways between short-term rental (STR) activity, STR regulation and rent.CC-BY-NC-ND · From the PDF
Figure 3 Bivariate relationships between rent_log , FREH_log , non_FREH_log and price_log .
Figure 3 Bivariate relationships between rent_log , FREH_log , non_FREH_log and price_log .CC-BY-NC-ND · From the PDF
Figure 4 Average treatment effect of the treated (ATT) of short-term rental (STR) principal-residence regulations on ren­ t_log by length of exposure.
Figure 4 Average treatment effect of the treated (ATT) of short-term rental (STR) principal-residence regulations on ren­ t_log by length of exposure.CC-BY-NC-ND · From the PDF
Figure 5 Average treatment effects by time period for the main rent_log , FREH_log and non_FREH_log models, and variants with Vancouver treatment date delayed by one year.
Figure 5 Average treatment effects by time period for the main rent_log , FREH_log and non_FREH_log models, and variants with Vancouver treatment date delayed by one year.CC-BY-NC-ND · From the PDF
Figure 6 Average treatment effect in untreated areas adjacent to treated areas of STR principal-residence regulations on rent_log by length of exposure.
Figure 6 Average treatment effect in untreated areas adjacent to treated areas of STR principal-residence regulations on rent_log by length of exposure.CC-BY-NC-ND · From the PDF
Figure 7 Year-to-year and cumulative estimated effects of short-term rentals (STRs) on change in total rent paid in Cana­ dian urban areas.
Figure 7 Year-to-year and cumulative estimated effects of short-term rentals (STRs) on change in total rent paid in Cana­ dian urban areas.CC-BY-NC-ND · From the PDF

Verification

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Self-check

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Citations

Cites

Research works printed but not linked: 1

  1. Sheppard, S., & Udell, A. (2016). Do Airbnb properties affect house prices? Williams College Department of Economics Working Papers, 3(1), 43.

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Metadata

Authors
Wachsmuth, D., & St-Hilaire, C.
Affiliations
  • McGill University (CA)
  • University of Waterloo (CA)
Corresponding author
Wachsmuth, David
ORCID
Venue
Regional Studies
Publisher
Informa UK Limited
Published
2026-06-10
Volume
60
Issue
1
ISSN
  • 0034-3404 (ISSN-L)
  • 1360-0591
DOI
10.1080/00343404.2026.2670360
OpenAlex
W7164149651
Cited by
2
References
44
Citations by year
2026: 2
FWCI
8.53
Type
journal article
Language
en
Open access
Licence
Locations
Regional Studies (publishedVersion, cc-by-nc-nd)
Funders
Social Sciences and Humanities Research Council (435-2019-0720)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Housing Market and Economics
  • Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
  • Social Sciences › Economics, Econometrics and Finance › Finance › Housing, Finance, and Neoliberalism
Keywords
short-term rentals, home sharing, residential rents, rent regulation, housing market, metropolitan housing markets, Canadian cities

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