spb/wp5_uqo Public

1% Author: Simon-Pierre Boucher — contact@spboucher.ai2% =============================================================================3% 01_introduction.tex4% =============================================================================5\section{Introduction}\label{sec:introduction}67The rapid expansion of short-term rental platforms has fundamentally transformed urban housing markets around the world.  Since its founding in 2008, Airbnb has grown from a modest home-sharing service into a global hospitality platform with millions of listings across virtually every major city, an archetypal case of disruptive innovation in tourism accommodation \citep{guttentag2015airbnb} and of the broader rise of peer-to-peer markets \citep{einav2016peer, sundararajan2016sharing}.  By enabling property owners to rent their dwellings---or portions thereof---to short-term visitors at nightly rates that often exceed what a long-term tenant would pay on a monthly basis, the platform has created powerful economic incentives to divert housing units from the residential rental market.  The same flexible peer supply that generates large welfare gains in the accommodation market \citep{zervas2017rise, farronato2022welfare} is, from the housing market's perspective, a competing use for the residential stock.89This diversion mechanism sits at the heart of a growing policy concern: that the proliferation of Airbnb listings may contribute to rising residential rents and reduced housing affordability, particularly in cities with significant tourist appeal.  A now-substantial empirical literature supports the concern: studies exploiting cross-market variation \citep{barron2021effect, horn2017airbnb}, regulatory discontinuities \citep{koster2021short, valentin2021regulating, duso2024airbnb}, and within-city diffusion \citep{garcia2020airbnb, franco2021impact} consistently find that STR activity raises rents and house prices, with effects that are modest per listing but spatially concentrated.  The economic logic is straightforward.  When landlords can earn higher returns by listing a unit on Airbnb than by renting it to a long-term tenant, the effective supply of long-term rental housing contracts.  On the demand side, Airbnb may attract additional visitors to a neighbourhood, increasing foot traffic, consumption amenities, and---through general equilibrium effects---the desirability of the area for both tourists and residents.  The net effect on rents is theoretically ambiguous: supply withdrawal pushes rents upward, while the sign and magnitude of demand-side amenity effects depend on the local context, the composition of Airbnb listings, and the degree of market segmentation between short-term and long-term rentals.1011The province of Quebec, and the city of Montreal in particular, provides a compelling setting in which to study these dynamics.  Montreal is Canada's second-largest city and one of North America's foremost tourist destinations, attracting over 11 million visitors annually prior to the COVID-19 pandemic.  Its dense, walkable neighbourhoods, vibrant cultural scene, and historic architecture make it especially attractive for short-term rental guests, and national analyses identify it as one of Canada's largest and most commercialised STR markets \citep{combs2020short}.  At the same time, Quebec has experienced significant rental market pressures in recent years.  Vacancy rates in the Montreal census metropolitan area have fallen to historically low levels, and median rents have risen substantially.  Policymakers at the provincial and municipal levels have responded with a mix of regulatory interventions, including CITQ registration requirements for short-term rental operators and zoning restrictions in Montreal \citep{nieuwland2020regulating}.  Yet the econometric evidence on the housing-market effects of Airbnb in the Quebec context remains remarkably thin: existing Canadian work is either national and descriptive \citep{combs2020short} or focused on Toronto's political economy of displacement \citep{grisdale2021displacement}.  No study, to our knowledge, has estimated the Airbnb--rent relationship at the individual listing level for Quebec.1213Estimating the effect of short-term rental platforms on residential rents poses significant empirical challenges.  The most fundamental is endogeneity: Airbnb listings are not randomly assigned to locations.  Hosts choose to list in neighbourhoods where rents---and tourist demand---are already high, creating a positive correlation between Airbnb density and rent levels that may reflect reverse causality or omitted neighbourhood characteristics rather than a genuine causal effect.  In the ideal research design, one would exploit plausible exogenous variation in Airbnb supply---for example, a regulatory shock that differentially affected neighbourhoods---combined with panel data to control for time-invariant unobservables.  Our data, however, are cross-sectional: they comprise a single snapshot of Airbnb and rental listings in Quebec, scraped in 2026.  We therefore adopt a transparent hedonic pricing framework that quantifies the conditional association between Airbnb presence and rents, controlling for a rich set of dwelling characteristics and location fixed effects, while being explicit about the limitations of causal interpretation.1415Our empirical strategy proceeds in several stages.  First, we estimate a hedonic rent model in which the logarithm of monthly rent is regressed on the count of Airbnb listings within a spatial buffer (our preferred radius is 500 metres), controlling for the number of bedrooms, bathrooms, building type, and city fixed effects.  This specification yields a semi-elasticity interpretation: the coefficient on Airbnb count measures the percentage change in rent associated with one additional nearby Airbnb listing, holding observable dwelling characteristics constant.  Second, we estimate a complementary hedonic model of Airbnb nightly prices, in which city-level mean rents serve as a control, allowing us to examine the bidirectional pricing relationship between the two market segments.  Third, we aggregate the data to the city level to examine cross-city variation in Airbnb penetration and mean rents.  Fourth, we incorporate spatial lags and vary the buffer radius (250\,m, 500\,m, 1\,km, 2\,km) to assess the spatial decay of the association.  Fifth, quantile regressions at the 10th, 25th, 50th, 75th, and 90th percentiles reveal how the Airbnb--rent association varies across the conditional rent distribution.  Sixth, we benchmark our parametric estimates against machine-learning methods---LASSO, elastic net, random forest, and gradient boosting---to assess predictive importance and model robustness.1617To preview the central result: each additional Airbnb listing within 500 metres of a rental unit is associated with approximately 0.4\% higher monthly rent, conditional on dwelling characteristics, building type, and city fixed effects.  The association decays monotonically with distance, is present throughout the conditional rent distribution but strongest at its upper tail, and survives spatial-econometric treatment, city-clustered inference, functional-form changes, sample trims, and leave-one-city-out exclusions.  At the median rent of roughly \$1{,}950 per month, the point estimate corresponds to \$6--10 per additional nearby listing---strikingly close, in order of magnitude, to the 7--13 euros per listing that \citet{duso2024airbnb} recover from Berlin's regulatory shocks, and consistent with the per-listing effects implied by the Los Angeles ordinance evidence of \citet{koster2021short}.  That a transparent cross-sectional design recovers magnitudes in line with the quasi-experimental literature is, we argue, informative in both directions: it lends external credibility to our estimates and out-of-sample support to the causal designs.1819This paper makes three contributions.  First, it provides the first granular, listing-level econometric analysis of the Airbnb--rent nexus in Quebec---and, to our knowledge, in any Canadian market---drawing on microdata with precise geographic coordinates that allow exact distance-based matching between Airbnb and rental listings.  Prior Canadian evidence is descriptive \citep{combs2020short} or qualitative \citep{grisdale2021displacement}; we complement it with formal estimation.  Second, it applies a multi-method approach---hedonic regressions \citep{rosen1974hedonic}, spatial econometrics estimated by generalised moments \citep{kelejian1998generalized, kelejian1999generalized}, quantile regressions \citep{koenker1978regression}, and machine-learning benchmarks \citep{mullainathan2017machine}---to a single dataset, enabling direct comparison of results across methodological frameworks and, in particular, delivering the first distributional (quantile-level) characterisation of the STR--rent association for a Canadian market.  Third, it contributes to the ongoing policy debate about short-term rental regulation in Canadian cities by providing empirically grounded estimates of the magnitude and spatial reach of the Airbnb--rent association---quantities that bear directly on the geographic targeting of Quebec's registration-based regime---even as it highlights the limitations inherent in cross-sectional identification.2021The remainder of the paper is organised as follows.  Section~\ref{sec:literature} reviews the related literature on short-term rentals and housing markets.  Section~\ref{sec:data} describes the data sources, cleaning procedures, and spatial merge strategy.  Section~\ref{sec:methodology} presents the econometric framework.  Section~\ref{sec:results} reports the main empirical results.  Section~\ref{sec:robustness} provides robustness checks and sensitivity analyses.  Section~\ref{sec:discussion} discusses policy implications and limitations.  Section~\ref{sec:conclusion} concludes.22