% Author: Simon-Pierre Boucher — contact@spboucher.ai
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% appendix_robustness.tex — Additional robustness tables
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\section{Additional Robustness Results}\label{app:robustness}

This appendix presents additional robustness checks that supplement the results reported in Section~\ref{sec:robustness}.

\subsection{Full Regression Output --- Baseline Hedonic Model}

The preferred hedonic rent specification (Model~1c in Table~\ref{tab:hedonic_rent}) includes property controls (bedrooms, bathrooms, building type) and city fixed effects. Full regression output including all control variable coefficients and city fixed effects is available upon request.

\subsection{Correlation Matrix of Airbnb Exposure Measures}

The pairwise correlation matrix of the Airbnb exposure variables at the 500\,m buffer radius documents the degree of collinearity among alternative exposure measures. The various exposure measures---count, density, share of entire-home listings, mean price---are positively correlated, reflecting the spatial concentration of Airbnb activity.

\subsection{Quantile Regression --- Full Coefficient Tables}

The quantile regression results reported in Table~\ref{tab:quantile} focus on the Airbnb exposure coefficient across the rent distribution. Full coefficient tables including control variables at each quantile are available upon request.

\subsection{Machine-Learning Cross-Validation Results}

Table~\ref{tab:ml_performance} in the main text reports hold-out sample performance for all machine-learning models. The gradient boosting and random forest models achieve substantially higher $R^2$ on the test set relative to linear models, suggesting meaningful nonlinearities in the rent--amenity relationship.

\subsection{Additional Subsample Results}

The subsample analysis in Section~\ref{sec:robustness} examines Montreal versus non-Montreal and apartment versus house subsamples. Additional subsample results (e.g., by Airbnb exposure quartile or by borough-level regulation status) are available upon request.