Short-term rental platforms reduce long-term housing supply for residents
Airbnb and similar platforms remove units from the long-term rental market, reducing supply and raising rents in affected neighborhoods, with the burden falling on lower-income residents.
STR platforms do measurably reduce long-term housing supply and raise rents in high-demand markets, but the effect size is modest, geographically concentrated, and mediated by local regulatory choices. The structural burden is real but unevenly distributed — commercial multi-unit operators drive most of the harm, while occasional hosts contribute little.
The claim
Airbnb and competing short-term rental (STR) platforms have, since their rapid expansion after 2012, converted a significant share of long-term residential housing stock into de facto hotel inventory. The claim is structural: individual landlords and property owners, responding rationally to the revenue premium that short-term rentals offer over long-term leases, remove units from the residential market permanently. This supply withdrawal raises rents for the residents who remain, with the disproportionate burden falling on lower-income renters who are least able to absorb rent increases and least likely to be Airbnb hosts themselves. The problem is concentrated in already high-cost urban neighborhoods and tourist corridors, precisely where affordable housing is most scarce and where the trade-off between tourism infrastructure and residential community is sharpest.
The mechanism
The causal pathway is straightforward in its logic. In markets where short-term rental revenue substantially exceeds long-term rental revenue for an equivalent unit, a rational property owner has a financial incentive to convert. In major US cities circa 2019, the STR premium on a full-unit listing averaged 50–100% above annual long-term lease revenue in high-demand neighborhoods. That premium creates a conversion incentive for three categories of operator: existing landlords converting long-term units; property investors acquiring residential units for STR use rather than as housing; and homeowners with accessory units or second properties shifting them to platforms.
The supply withdrawal mechanism tightens as commercial operators — those operating multiple full-unit listings year-round — concentrate their activity in the most desirable, highest-demand neighborhoods. These are also the neighborhoods where displacement pressure on lower-income and working-class residents is already greatest. Unlike hotel supply, which is constrained by commercial zoning and requires capital investment in commercial-grade construction, STR conversion can happen incrementally, invisibly, and without triggering the land-use review that would apply to formal hotel development. This regulatory arbitrage is central to the structural argument: STRs exploit residential zoning protections while operating as commercial hospitality businesses.
The mechanism breaks down, or attenuates, in several conditions: where the STR premium over long-term rents is modest (lower-demand secondary markets); where adaptive supply responses occur (developers building units intended for STR use in areas where residential conversion is penalized); and where the platform’s business model concentrates on occasional hosting of primary residences rather than commercial multi-unit operation.
The evidence
Barron, Kung, and Proserpio (2021): The most-cited quantitative study of STR effects on housing markets used Airbnb’s own proprietary data on listings and bookings, matched to zip-code level rent and house price data from the American Community Survey and Zillow. Across US zip codes from 2012 to 2016, a 10% increase in Airbnb listings was associated with a 0.42% increase in rents and a 0.76% increase in house prices. While the effect per listing is modest, it accumulates substantially in markets where Airbnb penetration is high — neighborhoods in San Francisco, Manhattan, and New Orleans where STR density reached 1 in 10 or more units showed estimated rent effects in the 3–5% range attributable to STR conversion alone. The identification strategy relies on variation in the timing of Airbnb’s city-by-city expansion and zip-code-level listing density, controlling for local housing market trends. This is stronger than simple cross-sectional correlation but not a clean natural experiment, and the estimated effect size has been subject to methodological challenge.
New York City Local Law 18 as natural experiment: New York City enacted Local Law 18 in 2023, requiring all short-term rental hosts to register with the city, be present during guest stays, and limit rentals to two guests at a time. The law effectively prohibited the full-unit commercial STR model. The result was immediate and large: Inside Airbnb’s tracking showed the number of active New York City Airbnb listings drop from approximately 22,000 before enforcement to approximately 3,400 within months. Hotel prices in the city rose in the subsequent months as tourist accommodation demand shifted to formal hotel stock. The effect on long-term rental supply has been more difficult to measure in real time — the question of whether returned units flow into long-term rental or remain vacant, are sold, or are converted to other uses is empirically contested and will require multi-year data to answer cleanly. But the supply withdrawal was confirmed: approximately 18,000 units had been operating as commercial hotel stock in residential buildings before the law.
Wachsmuth and Weisler (2018) and the commercial operator distinction: Research by Wachsmuth and Weisler in Environment and Planning A established a critical disaggregation that subsequent policy debates have often ignored. In major US and Canadian cities, roughly 30–40% of Airbnb listings — the commercial multi-listing operators — generated 60–70% of total platform revenue and represented essentially all of the full-unit, year-round conversion supply. These operators are distinguishable from the occasional host renting a spare room or listing their primary residence during vacation. The structural harm is concentrated in the commercial segment; occasional hosting has a negligible effect on long-term rental supply because the unit would not have been available for long-term rental regardless. This distinction matters enormously for policy design: a registration requirement that permits occasional hosting but caps commercial operation addresses the supply-withdrawal mechanism directly, while a blanket prohibition penalizes the economically innocuous behavior along with the harmful kind.
Barcelona’s STR license policy: Barcelona presents the most consequential regulatory case study in Europe. The city froze new STR licenses in 2014 and in 2024 announced a policy of not renewing the approximately 10,000 existing licenses when they expire in 2028, effectively phasing out short-term rentals in residential buildings entirely over four years. The city’s justification was explicit: STRs had removed an estimated 7–10% of rental housing stock in central districts and contributed materially to rent increases that displaced working-class residents from historically mixed neighborhoods like the Gothic Quarter, El Born, and Gràcia. Research by Garcia-López et al. (2020) in Regional Science and Urban Economics found that STR density was significantly associated with rent increases and reduced rental availability in Barcelona’s central districts, consistent with the supply-withdrawal mechanism. The 2028 phaseout is projected to return approximately 10,000 units to the long-term market, though critics note that some will be sold rather than rented, and that the broader Catalan housing crisis has drivers — insufficient social housing construction, speculative investment from non-platform buyers — that STR regulation alone cannot address.
Amsterdam’s night cap: Amsterdam imposed a 30-night-per-year limit on STR rentals in 2019, reduced from a prior 60-night limit. Adamiak (2022) found that the cap was associated with a measurable reduction in STR listings and a modest tightening of vacancy in central Amsterdam districts. However, the effect on rents was difficult to isolate from concurrent housing market trends, and platform-level enforcement of night limits has historically been unreliable without active city monitoring.
Neighborhood-level concentration: The aggregate market-level effects mask sharper neighborhood-level impacts. Garcia-López et al. and subsequent studies using Boston, New Orleans, and New York data consistently find that STR conversion is spatially concentrated in neighborhoods with high tourism amenity value — historic cores, waterfront areas, entertainment districts — that overlap substantially with neighborhoods undergoing gentrification pressure for independent reasons. In these areas, the STR premium accelerates displacement dynamics that are already in motion, making attribution to the platform specifically methodologically difficult but contributing to a credible structural account.
Who benefits
Airbnb Inc. and Vrbo (an Expedia Group subsidiary) have a direct financial interest in maximizing the number of active listings, which drives platform revenue through host service fees (typically 3% of booking value) and guest service fees (typically 14–16%). Both companies have invested heavily in lobbying against municipal STR regulation, hiring former government officials as policy staff and funding research favorable to the occasional-host narrative — emphasizing the income supplement provided to individual homeowners while minimizing commercial operator concentration data. Airbnb’s public affairs strategy has consistently framed regulatory opposition as protecting the income of middle-class homeowners rather than defending commercial hospitality operators, though the commercial segment generates the majority of Airbnb’s US revenue.
Commercial STR investors — property management companies operating dozens to hundreds of units on STR platforms — benefit from the current regulatory gap between residential and commercial zoning, which permits hotel-equivalent operations at residential property tax rates and without compliance with commercial building codes, accessibility requirements, or hotel labor law.
Counterintuitively, the hotel industry has been a significant funder of STR regulation campaigns in several cities, including New York (where the Hotel Trades Council, a hotel workers union, was among the leading advocates for Local Law 18). The hotel industry’s interest here is competitive rather than housing-focused: STRs reduce hotel occupancy and pricing power. Analysts of STR regulation must account for this context: some of the most aggressive advocacy for supply-withdrawal framing has come from actors with competitive rather than housing interests, which does not make the underlying evidence wrong but does complicate the political economy.
The counter
The strongest version of the opposing argument is that STR platforms are a symptom of housing undersupply, not a primary cause. In markets where housing supply is sufficiently elastic — where new construction responds to price signals — a portion of STR conversion would be offset by induced new supply. The core constraint in high-cost US cities is not STR conversion but exclusionary zoning, building height restrictions, and permitting delays that prevent supply from responding to demand at any level. On this view, STR regulation redirects policy attention from the primary constraint (supply) to a secondary one (conversion), while leaving the underlying scarcity intact.
There is also a real income dimension on the host side that the structural account can underemphasize. For individual homeowners and primary-residence hosts — a significant fraction of total hosts by count if not by revenue — STR income provides meaningful economic supplementation, particularly for lower-income homeowners in appreciating markets who might otherwise be unable to afford property taxes or maintenance. A policy that categorically prohibits STR activity without distinguishing commercial operators from occasional hosts imposes real costs on this population.
The rent effect size itself is contested. Barron et al.’s 0.42% per 10% listing increase estimate has been critiqued for potential endogeneity — Airbnb expands fastest in markets that were already experiencing rent growth, which could bias upward the estimated effect of listing density on rents. Koster, van Ommeren, and Volkhausen (2021) found smaller effects in some European markets, and several studies using different identification strategies have produced estimates both above and below the Barron et al. baseline.
Finally, the assumption that returned STR units would flow into affordable rental supply is not guaranteed. In high-demand markets, units exiting STR use are as likely to be sold, renovated, or converted to high-end long-term rentals as to provide affordability relief. New York’s early Local Law 18 data show hotel prices rising without a corresponding documented decline in long-term rents — a pattern consistent with the unit-type substitution hypothesis (the returned units are inappropriate or unavailable for lower-income tenants) rather than a simple supply story.
References
Barron, K., Kung, E., & Proserpio, D. (2021). The effect of home-sharing on house prices and rents: Evidence from Airbnb. Marketing Science, 40(1), 23–47. https://doi.org/10.1287/mksc.2020.1227
Wachsmuth, D., & Weisler, A. (2018). Airbnb and the rent gap: Gentrification through the sharing economy. Environment and Planning A: Economy and Space, 50(6), 1147–1170. https://doi.org/10.1177/0308518X18778038
Garcia-López, M. À., Jofre-Monseny, J., Martínez-Mazza, R., & Segú, M. (2020). Do short-term rental platforms affect housing markets? Evidence from Airbnb in Barcelona. Journal of Urban Economics, 119, 103278. https://doi.org/10.1016/j.jue.2020.103278
Koster, H. R. A., van Ommeren, J., & Volkhausen, N. (2021). Short-term rentals and the housing market: Quasi-experimental evidence from Airbnb in Los Angeles. Journal of Urban Economics, 124, 103356. https://doi.org/10.1016/j.jue.2021.103356
Adamiak, C. (2022). Current state and development of Airbnb accommodation offer in 167 countries. Current Issues in Tourism, 25(19), 3131–3149. https://doi.org/10.1080/13683500.2019.1696758
Horn, K., & Merante, M. (2017). Is home sharing driving up rents? Evidence from Airbnb in Boston. Journal of Housing Economics, 38, 14–24. https://doi.org/10.1016/j.jhe.2017.08.002
Sheppard, S., & Udell, A. (2016). Do Airbnb properties affect house prices? Williams College Department of Economics Working Paper.
Inside Airbnb. (2023). New York City data: Active listings before and after Local Law 18. http://insideairbnb.com/new-york-city/
Barcelona City Council. (2024). Plan for the non-renewal of tourist accommodation licenses in residential buildings. Ajuntament de Barcelona. https://ajuntament.barcelona.cat/
Lee, D. (2016). How Airbnb short-term rentals exacerbate Los Angeles’s affordable housing crisis. Harvard Law & Policy Review, 10(1), 229–253.
Premise Assessment
Is the claim as stated true? Four dimensions, each 0–25, sum to 100. The verdict label is derived from this score. Full rubric →
Quality and quantity of direct evidence for or against the claim — RCTs, systematic reviews, natural experiments, large cohort studies.
Multiple peer-reviewed studies (Barron et al. 2021, Garcia-López et al. 2020) and quasi-experimental evidence (NYC Local Law 18) confirm STRs remove units from long-term supply and correlate with rent increases (0.42% per 10% listings). Evidence strongly supports the basic claim, though effect sizes are modest and vary across markets.
Whether the proposed mechanism is valid and established — does the how make sense, or are there fundamental flaws in the causal logic?
The mechanism is well-established: higher STR revenue creates rational incentive for landlords to convert units, documented across multiple markets. The pathway from conversion to supply tightening to rent pressure is empirically confirmed. However, mechanism attenuates with occasional hosts and where adaptive supply responses occur.
Degree of agreement among domain experts and relevant scientific or policy bodies — depth and quality of consensus, not just majority opinion.
Broad housing economics consensus that STRs remove units in high-demand urban markets, with formal policy responses in Barcelona, Amsterdam, Paris, and NYC. However, disagreement exists on effect magnitudes and whether STRs are primary driver versus symptom of undersupply, with some scholars emphasizing zoning constraints.
Whether findings hold across independent studies, populations, and contexts — resistance to p-hacking and publication bias.
Barron et al. findings replicate directionally across multiple US cities (Boston, NYC, New Orleans, Los Angeles) and European cities show consistent qualitative effects. However, effect sizes vary substantially (0.42% vs. smaller European estimates), and replication is not uniform across all contexts studied.
Individual vs. Structural
How much of the outcome is explained by structural forces versus individual agency? Four dimensions, each 0–25. Higher scores indicate stronger structural causation. Full rubric →
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