% Author: Simon-Pierre Boucher — contact@spboucher.ai
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% 07_discussion.tex
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\section{Discussion}\label{sec:discussion}

This section places the empirical findings in the context of the international literature, interprets them for housing policy, discusses the mechanisms that may underlie the observed associations, and addresses the limitations of the analysis.

\subsection{Findings in the Context of the Literature}\label{sec:discussion_context}

How do our magnitudes compare with prior work?  Three benchmarks are instructive.  First, \citet{duso2024airbnb}, using regulatory shocks in Berlin, estimate that each nearby Airbnb listing raises asking rents by roughly 7--13 euros per month; our preferred estimate implies \$6--10 CAD per listing at the median Quebec rent---the same order of magnitude, obtained from an entirely different market and design.  Second, \citet{koster2021short} find that Los Angeles ordinances which halved Airbnb activity lowered local rents by about 2\%; read through our semi-elasticity, removing half of the mean nearby exposure in our sample would predict a rent differential of a comparable magnitude in Airbnb-dense neighbourhoods.  Third, \citet{horn2017airbnb} report that a one-standard-deviation increase in Airbnb density in Boston is associated with 0.4\% higher asking rents; our per-listing coefficient of 0.4\%, combined with the dispersion of exposure in our data, implies a somewhat larger standardised association, consistent with Quebec's rental stock being more concentrated in dense, Airbnb-exposed neighbourhoods.  Our estimates also sit comfortably within the range documented across European markets \citep{garcia2020airbnb, franco2021impact, ayouba2020airbnb}.

Two disagreements with the literature deserve emphasis rather than concealment.  First, unlike most STR pricing studies \citep{wang2017price, ert2016trust, gibbs2018pricing}, we find a superhost \textit{discount} and no significant association between city-level rents and nightly prices (Model~2); we attribute both to composition---Quebec's STR market mixes urban apartments with resort chalets, and the cross-city dimension is dominated by resort communities where nightly prices are high but long-term rents are not.  Second, the compositional prediction of the supply-withdrawal hypothesis---that entire-home listings should matter most \citep{horn2017airbnb, combs2020short}---is not supported in our data: the entire-home share within the buffer is statistically indistinguishable from zero once fixed effects are included (Model~1e).  Either our property-type proxy for entire-home status is too coarse, or in Quebec the intensity of nearby STR activity matters more than its composition.  Both null results are informative for the Canadian debate precisely because they were not guaranteed by the design.

\subsection{Policy Implications for Housing Affordability}

Our finding of a positive association between Airbnb presence and residential rents, while not causal in the strict econometric sense, carries implications for housing affordability policy.  If the association reflects, even in part, a genuine supply-withdrawal mechanism---whereby landlords convert long-term rental units into short-term Airbnb listings---then the cumulative effect on rents in high-tourism neighbourhoods could be substantial.  At our estimated semi-elasticity of 0.3--0.5\% per additional Airbnb listing within 500\,m, a neighbourhood with 20 nearby Airbnb listings would be associated with 6--10\% higher rents relative to an otherwise identical dwelling in an Airbnb-free area, all else equal.

For policymakers in Quebec and Montreal, these estimates suggest that short-term rental activity is a factor---though certainly not the only or dominant factor---in the rental affordability equation.  The magnitude of the association is modest relative to the contribution of dwelling characteristics (bedrooms, bathrooms) and location (city fixed effects), but it is non-trivial in a market where vacancy rates are historically low and modest rent increases impose real burdens on lower-income tenants.

\subsection{Short-Term Rental Regulation}

The province of Quebec requires short-term rental operators to register with the Corporation de l'industrie touristique du Qu\'{e}bec (CITQ) and imposes minimum standards on registered operators.  The City of Montreal has supplemented these provincial requirements with additional restrictions, including limits on the number of nights per year that a primary residence can be rented on a short-term basis and outright prohibitions on non-owner-occupied short-term rentals in certain boroughs.

Our results, while descriptive, provide empirical grounding for the policy rationale underlying these regulations.  The spatial decay of the Airbnb--rent association (Section~\ref{sec:results}) suggests that the effects are highly localised, which supports geographically targeted interventions (e.g., borough-level restrictions) rather than blanket province-wide regulations.  The quantile regression results further suggest that regulatory attention might focus on high-rent neighbourhoods, where the association is strongest and where the risk of supply withdrawal is most acute.

However, regulation involves tradeoffs.  Short-term rentals generate income for hosts, tax revenue for municipalities, and consumer surplus for travellers.  Overly restrictive regulation may push short-term rental activity underground, reduce tourism revenues, and impose compliance costs on casual hosts who rent their primary residence occasionally.  A well-calibrated regulatory framework would balance these considerations by targeting commercial operators---those who manage multiple entire-home listings---while preserving the ability of residents to engage in occasional home-sharing.

\subsection{Professional Hosts and Commercialisation}

The literature has documented the increasing commercialisation of Airbnb, with a growing share of listings operated by professional, multi-listing hosts \citep{ke2017sharing, wachsmuth2018airbnb}.  Unfortunately, our data do not contain host identifiers, precluding direct measurement of multi-listing activity.  The compositional evidence we can bring to bear is, moreover, not supportive of a simple commercialisation channel: the share of entire-home listings within the buffer---the listings most likely to be operated commercially---is not significantly associated with rents once dwelling characteristics and city fixed effects are controlled for (Model~1e in Table~\ref{tab:hedonic_rent}).  In our cross-section, it is the overall intensity of nearby Airbnb activity, rather than its compositional tilt toward entire homes, that co-varies with rents.  One interpretation is that our property-type proxy for entire-home status is too coarse to isolate commercial operations; another is that, within Quebec's market, casual and commercial listings are sufficiently co-located that composition adds little signal beyond the count.

Future research with host-level data could decompose the Airbnb--rent association into contributions from commercial versus casual hosts, providing a sharper evidence base for regulatory targeting.

\subsection{The Tourism--Housing Tradeoff}

At a broader level, the Airbnb--rent relationship exemplifies a fundamental tension in urban policy: the tradeoff between tourism-driven economic activity and residential affordability.  Cities like Montreal derive substantial economic benefits from tourism---employment in hospitality, retail, and cultural sectors; tax revenues; and global visibility---but these benefits are unevenly distributed, while the costs of tourism-driven housing pressure fall disproportionately on renters.  Short-term rental platforms amplify this tension by enabling the conversion of residential housing into tourism infrastructure at the level of individual units, bypassing the traditional regulatory apparatus that governs hotel and commercial accommodation.

Our findings suggest that this tension is empirically present in the Quebec context, though its magnitude is moderate.  The policy challenge is to design regulatory frameworks that capture the benefits of short-term rental activity while mitigating its externalities on the residential rental market---a challenge that requires ongoing empirical monitoring as the platform economy continues to evolve.

\subsection{Limitations}

We reiterate and expand upon the key limitations of our analysis:

\begin{enumerate}[label=(\roman*)]
    \item \textbf{Cross-sectional identification:} Our data represent a single point in time.  We cannot distinguish the causal effect of Airbnb on rents from reverse causality (high rents attracting Airbnb hosts) or confounding by unobserved neighbourhood characteristics.  A credible causal estimate would require panel data---ideally combined with a policy shock that exogenously shifted Airbnb supply in some locations but not others, as in \citet{koster2021short}, \citet{valentin2021regulating}, and \citet{duso2024airbnb}---or a valid instrumental variable for Airbnb penetration in the spirit of \citet{barron2021effect}.  That said, the consistency of our magnitudes with these designs (Section~\ref{sec:discussion_context}) suggests the cross-sectional bias, whatever its direction, is unlikely to be dramatic.

    \item \textbf{No host-level data:} The absence of host identifiers prevents us from identifying multi-listing operators and from distinguishing professional from casual hosts, a distinction that is central to the commercialisation debate \citep{ke2017sharing, combs2020short} and to well-targeted regulation \citep{gurran2017when}.

    \item \textbf{Asking vs.\ transacted rents:} Our dependent variable measures asking rents on Realtor.ca rather than actual contract rents.  If asking rents systematically overstate transacted rents (due to landlord bargaining power or strategic posting), our estimates may be biased, though the direction of this bias is ambiguous.  We note that several benchmark studies share this feature \citep{horn2017airbnb, duso2024airbnb}, which aids comparability even if it does not remove the concern.

    \item \textbf{Exposure measurement error and platform coverage:} Both datasets are scraped from specific platforms and may not capture the universe of short-term or long-term rental listings.  Alternative STR platforms (e.g., VRBO, Booking.com) are not included, scraped snapshots miss delisted or calendar-blocked units, and the Realtor.ca data may underrepresent informal or unposted rentals.  To the extent that measurement error in the buffer counts is classical, it attenuates $\hat{\beta}$ toward zero, making our estimates conservative; non-classical error correlated with neighbourhood type cannot be ruled out.

    \item \textbf{Buffer choice and aggregation:} Any distance-based exposure measure faces a modifiable-areal-unit-style sensitivity: results could, in principle, depend on the radii chosen.  We address this directly by reporting four radii, a ring decomposition, density and logarithmic functional forms, and continuous-decay-consistent patterns (Section~\ref{sec:robustness}), but the discreteness of any buffer set remains a simplification of the underlying spatial process \citep{can1992specification}.

    \item \textbf{External validity:} Our sample is dominated by Montreal and a handful of other Quebec cities and resort areas.  The findings may not generalise to other Canadian provinces or to rural housing markets where short-term rental dynamics differ---a caution reinforced by the cross-market heterogeneity documented in France \citep{ayouba2020airbnb} and Canada \citep{combs2020short}.

    \item \textbf{Static analysis:} Our cross-sectional framework cannot capture dynamic adjustments in the housing market, such as the construction of new supply in response to rising rents \citep{glaeser2005urban, saiz2010geographic} or the exit of Airbnb hosts in response to regulatory pressure.
\end{enumerate}

These limitations motivate the cautious interpretive stance adopted throughout the paper and underscore the need for future research with richer data structures.

\subsection{Future Work}

Four extensions strike us as most valuable, in ascending order of data requirements.  First, replicating the buffer-based design on repeated scrapes of the same platforms would deliver a listing-level panel for Quebec, enabling fixed-effects and event-study designs around the province's evolving registration regime.  Second, Quebec's 2023 tightening of STR registration enforcement offers a natural policy discontinuity analogous to those exploited in Los Angeles \citep{koster2021short}, New Orleans \citep{valentin2021regulating}, and Berlin \citep{duso2024airbnb}; combining it with our exposure measures is the most direct route to causal estimates for Canada.  Third, host-resolved data would permit separating commercial from casual supply, testing the commercialisation channel that our entire-home-share proxy cannot \citep{ke2017sharing, combs2020short}.  Fourth, integrating administrative contract rents (e.g., lease registry data) would resolve the asking-rent measurement concern and allow distributional welfare analysis in the spirit of \citet{li2022market}.
