spb/wp5_uqo Public

1% Author: Simon-Pierre Boucher — contact@spboucher.ai2% =============================================================================3% 08_conclusion.tex4% =============================================================================5\section{Conclusion}\label{sec:conclusion}67This paper has investigated the relationship between Airbnb short-term rental activity and residential rents in Quebec, Canada, using cross-sectional microdata comprising approximately 5{,}000 Airbnb listings and 8{,}300 residential rental listings.  Employing a hedonic pricing framework augmented with spatial analysis, quantile regressions, and machine-learning benchmarks, we have documented a consistent positive association between nearby Airbnb presence and monthly rents.89Our key findings are as follows.  First, each additional Airbnb listing within 500 metres of a rental unit is associated with an approximate 0.4\% increase in monthly rent, conditional on dwelling characteristics and city fixed effects---an estimate that is stable across spatial and trimmed-sample specifications (roughly 0.3--0.5\%).  Second, this association exhibits spatial decay, with the per-listing coefficient largest at narrow buffer radii and attenuating at wider distances.  Third, quantile regressions reveal that the association, while significant throughout the distribution, is strongest at the upper tail, suggesting that high-rent segments of the market are most strongly linked to Airbnb activity.  Fourth, a complementary hedonic model of Airbnb nightly prices indicates that short-term rental pricing is driven primarily by listing characteristics; city-level mean rents carry no significant premium, offering no support for a simple opportunity-cost pricing channel in our cross-section.  Fifth, machine-learning models confirm the predictive relevance of Airbnb exposure variables and the adequacy of the linear hedonic specification for inference.1011The principal contribution of this paper is to provide the first granular, listing-level econometric analysis of the Airbnb--rent nexus in Quebec---extending a Canadian evidence base that was previously descriptive \citep{combs2020short, grisdale2021displacement}---leveraging precise geographic coordinates for exact distance-based spatial matching.  By applying multiple econometric and machine-learning methods to a single dataset, we demonstrate the consistency of the finding across methodological frameworks and provide a rich set of robustness checks that characterise the sensitivity of the estimates to alternative specifications.  Notably, our per-listing magnitude is of the same order as those recovered from regulatory quasi-experiments in Berlin and Los Angeles \citep{duso2024airbnb, koster2021short}, which we read as mutual corroboration between designs.1213We are candid about the limitations of our analysis.  The cross-sectional nature of the data precludes causal identification: the positive association between Airbnb density and rents may reflect reverse causality, omitted neighbourhood characteristics, or spatial sorting, rather than---or in addition to---a genuine supply-withdrawal effect.  Establishing causality in this domain requires panel data combined with plausibly exogenous variation in Airbnb supply, such as a regulatory discontinuity or a natural experiment.  Our results should therefore be interpreted as well-controlled conditional correlations that are consistent with the supply-withdrawal hypothesis but do not definitively confirm it.1415Several directions for future research emerge from this analysis.  First, the construction of panel data---tracking the entry and exit of Airbnb listings and the evolution of rents over time at the neighbourhood level---would enable difference-in-differences or event-study designs that can more credibly isolate the causal effect.  Second, the exploitation of regulatory shocks---such as the tightening of Montreal's short-term rental regulations or the introduction of provincial registration requirements---would provide natural-experiment variation for causal inference.  Third, the integration of host-level data would allow researchers to distinguish the effects of commercial multi-listing operators from those of casual home-sharers, sharpening the policy relevance of the analysis.  Fourth, extending the geographic scope to include other Canadian cities would improve external validity and enable cross-city comparisons of regulatory effectiveness.1617In sum, our findings add to a growing body of evidence---now spanning correlational, quasi-experimental, and structural designs \citep{barron2021effect, koster2021short, li2022market}---that short-term rental platforms are associated with higher residential rents, at least in localities with significant tourist appeal.  While the estimated magnitudes are modest at the per-listing level, their cumulative significance in high-tourism neighbourhoods---combined with the documented concentration of effects at the upper end of the rent distribution---underscores the importance of evidence-based regulatory frameworks that balance the economic benefits of the platform economy with the imperative of housing affordability.18