% Author: Simon-Pierre Boucher — contact@spboucher.ai
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\section{Literature Review}\label{sec:literature}

This section reviews the empirical and methodological literatures on which the paper builds.  We organise the review thematically rather than chronologically: we first survey the evidence on short-term rental (STR) platforms and housing costs, moving from the early correlational studies to the more recent quasi-experimental designs (Section~\ref{sec:lit_str}); we then turn to the Canadian and Quebec evidence (Section~\ref{sec:lit_canada}), the broader economics of peer-to-peer accommodation markets (Section~\ref{sec:lit_p2p}), and the pricing of STR listings themselves (Section~\ref{sec:lit_strpricing}).  The remaining subsections cover the methodological foundations of the paper: hedonic pricing theory and practice (Section~\ref{sec:lit_hedonic}), spatial econometrics (Section~\ref{sec:lit_spatial}), distributional analysis via quantile regression (Section~\ref{sec:lit_quantile}), and machine learning in housing economics (Section~\ref{sec:lit_ml}).  Section~\ref{sec:lit_regulation} reviews the tourism-gentrification and regulation literature that frames the policy discussion, and Section~\ref{sec:lit_gap} states the gap this paper fills.

\subsection{Short-Term Rentals and Housing Costs}\label{sec:lit_str}

Housing costs respond to shifts in effective supply, and markets with inelastic supply capitalise demand shocks into prices and rents \citep{glaeser2005urban, saiz2010geographic, gyourko2005superstar}.  STR platforms create precisely such a shift at the margin: by raising the return to allocating a dwelling to visitors rather than tenants, they can withdraw units from the long-term rental stock.  The empirical literature testing this mechanism has developed in two waves.

\paragraph{Correlational and panel evidence.}  The seminal large-scale study is \citet{barron2021effect}, who exploit zipcode-level variation in Airbnb penetration across the United States, instrumenting Airbnb growth with the interaction of Google Trends search interest and a measure of touristiness.  They estimate that a 1\% increase in Airbnb listings raises rents by 0.018\% and house prices by 0.026\%, and attribute the effect to the reallocation of units from long-term to short-term supply.  \citet{horn2017airbnb} use listing-level asking rents in Boston and find that a one-standard-deviation increase in Airbnb density is associated with rents approximately 0.4\% higher, an effect concentrated where Airbnb listings are entire homes.  \citet{sheppard2016airbnb} document property-value capitalisation in New York City, and \citet{wachsmuth2018airbnb} show that the ``rent gap'' opened by STR revenue potential is systematically related to gentrification pressure.  In a similar spirit, \citet{ayouba2020airbnb} apply spatial panel methods to eight French cities and find significant rent effects of Airbnb density in Lyon, Montpellier, and Paris but not elsewhere---early evidence that the association is heterogeneous across markets.  Using proprietary platform data and a structural model of landlord choice, \citet{li2022market} show that Airbnb entry shifts landlords at the margin between long-term and short-term supply, with the incidence falling on long-term tenants in high-demand markets.

\paragraph{Quasi-experimental evidence.}  A second wave exploits policy discontinuities and regulatory shocks.  \citet{garcia2020airbnb} use the staggered, spatially uneven diffusion of Airbnb across Barcelona neighbourhoods and estimate that Airbnb activity raised rents by 1.9\% and transaction prices by 4.6--7\% in high-activity areas.  \citet{koster2021short} exploit Home-Sharing Ordinances adopted by some Los Angeles-area cities but not others in a spatial regression-discontinuity design: ordinances reduced Airbnb listings by roughly 50\% and lowered house prices and rents by around 2\% in treated areas, implying a substantial positive effect of STR activity in the absence of regulation.  \citet{valentin2021regulating} finds that New Orleans' licensing reform capitalised into property values where STR use was legalised.  \citet{duso2024airbnb} use two Berlin regulatory shocks and estimate that each nearby Airbnb listing raises asking rents by roughly 7--13 euros per month, while \citet{franco2021impact} document positive effects of Airbnb density on both prices and rents across Portuguese municipalities, strongest in tourist-intensive locations.  For London, \citet{shabrina2022airbnb} link neighbourhood-level Airbnb ``misuse'' (de facto commercial operation) to higher local rents.

Taken together, this literature finds positive, modest-per-listing, spatially concentrated associations between STR activity and residential housing costs, with quasi-experimental designs generally confirming---and sometimes exceeding---the magnitudes suggested by earlier correlational work.  Our estimates are best read against this benchmark: a cross-sectional design cannot adjudicate causality, but it can establish whether the conditional correlations in a previously unstudied market are consistent with the causal magnitudes established elsewhere.

\subsection{Canadian and Quebec Evidence}\label{sec:lit_canada}

Evidence for Canada is comparatively scarce and largely descriptive.  \citet{combs2020short} provide the first national analysis of Canadian STR markets, documenting rapid but spatially uneven growth, strong revenue concentration among commercial multi-listing operators, and an estimate of roughly 31{,}000 housing units removed from Canadian long-term rental markets by frequently rented entire-home listings in 2018---with Montreal among the three dominant markets.  \citet{grisdale2021displacement} traces the political economy of STR-driven displacement in Toronto, linking commercial Airbnb operation to rental-supply loss in central neighbourhoods.  Beyond descriptive market measurement and critical urban geography, however, there is little listing-level econometric evidence for Canadian markets, and none, to our knowledge, for Quebec---a striking gap given that the province combines one of Canada's largest STR markets, distinctive provincial registration requirements, and acute rental-market tightness.  This paper addresses that gap.

\subsection{The Economics of Peer-to-Peer Accommodation}\label{sec:lit_p2p}

A complementary literature studies STR platforms as peer-to-peer markets.  \citet{einav2016peer} analyse how platforms reduce transaction costs and enable trade in underutilised assets, and \citet{sundararajan2016sharing} surveys the rise of ``crowd-based capitalism.''  \citet{guttentag2015airbnb} frames Airbnb as a disruptive innovation in tourism accommodation.  On market impacts, \citet{zervas2017rise} estimate that Airbnb entry reduced hotel revenues in the most affected Texas segments by 8--10\%, and \citet{farronato2022welfare} quantify the welfare effects of peer entry in the accommodation industry, showing that flexible peer supply expands capacity precisely when demand peaks, generating consumer surplus while eroding hotel profits.  This literature matters for our purposes because it establishes the economic incentive at the heart of the housing channel: peer hosts respond elastically to short-term rental returns, and the same responsiveness that creates accommodation-market surplus is what reallocates dwellings away from long-term tenants when STR returns exceed residential rents.

\subsection{Pricing of Short-Term Rental Listings}\label{sec:lit_strpricing}

A distinct strand estimates hedonic models of Airbnb nightly prices, which informs our Model~2.  \citet{wang2017price} study some 180{,}000 listings in 33 cities and find that entire-home status, capacity, and host attributes (notably superhost status) are dominant price determinants.  \citet{gibbs2018pricing} estimate hedonic price equations for five Canadian Airbnb markets---the study closest to our Model~2 in geographic scope---and document significant premia for entire homes and location.  \citet{ert2016trust} show that host reputation and trust signals are capitalised into Airbnb prices.  Notably, these studies generally find positive superhost premia, whereas our within-city estimates yield a superhost \textit{discount}; we return to this disagreement in Sections~\ref{sec:results} and~\ref{sec:discussion} and interpret it as a composition effect specific to Quebec's mix of urban and resort listings.

\subsection{Hedonic Pricing: Theory and Practice}\label{sec:lit_hedonic}

The hedonic framework descends from \citet{lancaster1966new}, who recast consumer demand as demand for characteristics, and \citet{rosen1974hedonic}, who formalised the market equilibrium in which the prices of differentiated products reveal implicit characteristic prices.  In housing applications, the hedonic price of a locational attribute---here, exposure to nearby STR activity---is identified from cross-sectional price variation conditional on structural characteristics \citep{palmquist2005property}.  Surveys of five decades of empirical practice document both the workhorse status and the fragility of hedonic estimates: \citet{sirmans2005composition} catalogue the composition of hundreds of published models; \citet{malpezzi2003hedonic} reviews functional-form and specification choices; and \citet{kuminoff2010which} show in large-scale simulations that spatial fixed effects and flexible functional forms are the most effective safeguards against omitted-variable bias in cross-sectional hedonics---a prescription our specification follows (city fixed effects; count, density, and logarithmic exposure forms; extensive robustness analysis).  \citet{parmeter2010applied} discuss the identification threats that remain.

\subsection{Spatial Econometrics in Housing Research}\label{sec:lit_spatial}

Housing prices are spatially dependent: nearby dwellings share unobserved amenities, and pricing spillovers propagate through comparable-based valuation.  Ignoring this dependence invalidates inference and can bias coefficients when spatially correlated unobservables are also correlated with regressors \citep{dubin1988estimation, can1992specification}.  The canonical modelling responses are the spatial autoregressive (SAR) and spatial error (SEM) models \citep{anselin1988spatial}, with feasible estimation by spatial two-stage least squares and generalised moments developed by \citet{kelejian1998generalized} and \citet{kelejian1999generalized}---the estimators we employ---and comprehensive treatment in \citet{lesage2009introduction}.  Our buffer-count exposure variable is itself a spatially weighted aggregate, so the spatial models play a dual role in this paper: they absorb residual spatial dependence, and they provide a conservative check on whether the Airbnb--rent association merely proxies for spatially correlated unobservables.

\subsection{Distributional Effects and Quantile Regression}\label{sec:lit_quantile}

Mean regression can mask heterogeneity across the conditional price distribution.  Quantile regression \citep{koenker1978regression, koenker2001quantile} recovers characteristic prices at arbitrary quantiles, and housing applications consistently find that implicit prices differ across the distribution: \citet{zietz2008determinants} show that buyers of higher-priced homes value certain attributes differently from buyers of lower-priced homes, and \citet{mcmillen2008changes} decomposes changes in the house-price distribution into characteristics and coefficient effects.  In the STR context, distributional heterogeneity is economically meaningful: if STR pressure is concentrated where rents are already high, the affordability consequences differ sharply from those of a uniform shift.  Our Model~5 provides, to our knowledge, the first quantile-level evidence on the Airbnb--rent association for a Canadian market.

\subsection{Machine Learning in Housing Economics}\label{sec:lit_ml}

Machine-learning methods enter modern hedonic practice in two roles: as prediction benchmarks and as data-driven specification checks \citep{mullainathan2017machine, athey2019machine}.  We use the LASSO \citep{tibshirani1996regression} and the elastic net \citep{zou2005regularization} for regularised variable selection, and random forests \citep{breiman2001random} and gradient boosting \citep{friedman2001greedy} as flexible nonlinear benchmarks, with SHAP values \citep{lundberg2017unified} for feature attribution; all models are implemented in scikit-learn \citep{pedregosa2011scikit}.  The comparison between OLS and the flexible learners serves an econometric purpose articulated by \citet{mullainathan2017machine}: if a linear hedonic attains predictive accuracy close to the flexible frontier, linearity is an adequate approximation for inference; where it falls short, the gap signals nonlinearities---in our data, primarily geographic---that fixed effects and buffer construction must absorb.

\subsection{Tourism Gentrification and Short-Term Rental Regulation}\label{sec:lit_regulation}

Finally, a critical urban literature situates STRs within longer-running processes of tourism-driven neighbourhood change.  \citet{gotham2005tourism} coined ``tourism gentrification'' to describe the transformation of New Orleans' Vieux Carr\'e; \citet{cocola2016holiday} identifies holiday rentals as a distinct gentrification battlefront in which transient visitors replace residents; and \citet{wachsmuth2018airbnb} connect this process explicitly to platform-enabled rent gaps.  The commercialisation of nominally peer-to-peer platforms is central to this critique: \citet{ke2017sharing} documents that professional multi-listing hosts account for a disproportionate share of Airbnb supply and revenue.  On the policy side, \citet{gurran2017when} ask how urban planners should respond to Airbnb; \citet{nieuwland2020regulating} compare regulatory regimes across cities, from outright prohibition to registration and cap systems; and \citet{agyeman2020airbnb} examine the equity dimensions of STR regulation.  In Quebec, provincial law requires STR operators to register with the Corporation de l'industrie touristique du Qu\'ebec (CITQ), and Montreal restricts commercial STRs to designated zones---a regime whose spatial targeting our decay results speak to directly.

\subsection{Positioning and Contribution}\label{sec:lit_gap}

Three gaps emerge from this review.  First, despite a mature international literature spanning correlational and quasi-experimental designs, there is no listing-level econometric evidence on the STR--rent relationship for Quebec, and Canadian evidence more broadly stops at descriptive market measurement \citep{combs2020short} and critical urban geography \citep{grisdale2021displacement}.  Second, few studies examine the \textit{distributional} incidence of the association---most report mean effects---although the affordability debate turns on who bears the pressure.  Third, the pricing side and the rent side of the market are rarely analysed jointly in the same data, despite the opportunity-cost logic that connects them.  This paper addresses all three gaps: it provides the first listing-level, multi-method quantification of the Airbnb--rent association in Quebec, characterises the association across the full conditional rent distribution, and estimates both sides of the market with mutually consistent variable constructions.  We are equally deliberate about what the paper does not do: with a single cross-section we do not identify causal effects, and we position our estimates as conditional correlations to be read against---and, as it turns out, found consistent with---the quasi-experimental magnitudes surveyed above.
