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1% Auto-generated (revision: full ladder in both panels)2\begin{table}[htbp]3 \centering4 \begin{threeparttable}5 \caption{Predictive Performance: From OLS to Deep Learning}6 \label{tab:models}7 \begin{tabular}{@{}lcccc@{}}8 \toprule9 Estimator & $R^2_{\text{OOS}}$ & Median AE & Within $\pm$15\% & Within $\pm$25\% \\10 \midrule11 \multicolumn{5}{@{}l}{\textit{Panel A: Structural + amenity + LLM-text features}} \\[3pt]12 OLS (linear hedonic) & 0.628 & 17.9\% & 43\% & 64\% \\13 Random Forest & 0.677 & 17.9\% & 43\% & 64\% \\14 Neural network (plain MLP) & 0.711 & 16.1\% & 47\% & 69\% \\15 Neural network (robust ResNet) & 0.741 & 15.2\% & 49\% & 70\% \\16 Gradient boosting (HistGBM) & 0.743 & 15.4\% & 49\% & 70\% \\17 Blend (NN + GBM) & 0.760 & 14.7\% & 51\% & 72\% \\[4pt]18 \multicolumn{5}{@{}l}{\textit{Panel B: \ + Vision (façade) features}} \\[3pt]19 OLS (linear hedonic) & 0.686 & 16.9\% & 45\% & 67\% \\20 Random Forest & 0.704 & 17.2\% & 45\% & 66\% \\21 Neural network (robust ResNet) & 0.747 & 14.8\% & 51\% & 71\% \\22 Gradient boosting (HistGBM) & 0.770 & 14.5\% & 51\% & 71\% \\23 \textbf{Blend (NN + GBM)} & \textbf{0.779} & \textbf{14.0\%} & \textbf{53\%} & \textbf{73\%} \\24 \bottomrule25 \end{tabular}26 \begin{tablenotes}[flushleft]27 \footnotesize28 \item \textit{Notes:} Out-of-sample performance from 5-fold cross-validation with a29 common seed (identical folds across rows). $R^2_{\text{OOS}}$ is on30 $\ln(\text{Price})$; ``Median AE'' is the median absolute percentage error in31 levels (Equation~\ref{eq:cvmetrics}). Panel~A uses structural, amenity, room-detail,32 listing-signal, and LLM text-derived features; Panel~B adds the twelve façade33 vision attributes. City, type, and source enter as one-hot indicators for the34 linear and tree models and as learned embeddings for the neural networks. The tree35 and neural models handle missing living area natively; OLS uses within-type median36 imputation. The best model is the text\,+\,vision blend.37 \end{tablenotes}38 \end{threeparttable}39\end{table}40