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1% Auto-generated by scripts/18_revision_language_leakage_spatial.py (2026-10-17)2\begin{table}[htbp]3 \centering4 \begin{threeparttable}5 \caption{Random versus Spatial (RTA-Grouped) Cross-Validation}6 \label{tab:rob_spatial}7 \small8 \begin{tabular}{@{}lcccc@{\hskip 0.8cm}cccc@{}}9 \toprule10 & \multicolumn{4}{c}{Random 5-fold} & \multicolumn{4}{c}{Spatial 5-fold (RTA groups)} \\11 \cmidrule(lr){2-5}\cmidrule(lr){6-9}12 Estimator & Base & $+$Text & $+$Vision & MdAPE & Base & $+$Text & $+$Vision & MdAPE \\13 \midrule14 OLS (linear hedonic) & 0.619 & 0.628 & 0.686 & 16.9\% & 0.561 & 0.568 & 0.638 & 18.5\% \\15 Gradient boosting & 0.729 & 0.743 & 0.770 & 14.5\% & 0.690 & 0.703 & 0.734 & 15.9\% \\16 \bottomrule17 \end{tabular}18 \begin{tablenotes}[flushleft]19 \footnotesize20 \item \textit{Notes:} Out-of-sample $R^2$ on $\ln(\text{Price})$ and median absolute percentage error (MdAPE, for the21 full text\,$+$\,vision specification). ``Base'' is the structural $+$ amenity $+$ rooms $+$ listing specification; the22 next two columns add the LLM text layer and the façade vision layer. In the spatial design the 420 forward23 sortation areas (RTA, first three characters of the postal code) are assigned to five folds by \texttt{GroupKFold}, so24 no listing is predicted from a model that has seen a listing of the same RTA; municipality indicators of RTAs held25 out in their entirety are unobserved in training and the model falls back on the reference category. Random-fold26 entries may differ by $\pm0.001$ from Table~\ref{tab:ablation}, which comes from a separate run. $N=$19{,}123.27 \end{tablenotes}28 \end{threeparttable}29\end{table}30