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1\begin{table}[H]2\centering3\caption{OLS fit and Moran's $I$ test on OLS residuals, by segment}4\label{tab:moran}5\begin{threeparttable}6\begin{tabular}{lrcccc}7\toprule8Segment & $N$ & Adjusted $R^2$ & Moran's $I$ & $z$-statistic & $p$-value \\9\midrule10Single-family houses & 6,250 & 0.619 & 0.506 & 51.9 & $<0.001$ \\11Condominiums & 1,031 & 0.669 & 0.551 & 22.0 & $<0.001$ \\12Multiplexes & 3,755 & 0.576 & 0.528 & 40.2 & $<0.001$ \\13Vacant land & 9,239 & 0.154 & 0.580 & 67.2 & $<0.001$ \\14\bottomrule15\end{tabular}16\begin{tablenotes}17\small18\item \textit{Notes:} OLS estimates of equation~\eqref{eq:hedonic} with HC3 standard errors. Moran's $I$ uses the row-standardised inverse-distance $k$-nearest-neighbour matrix ($k = 10$) of equation~\eqref{eq:W}; the $z$-statistic uses the randomisation (kurtosis-adjusted) variance of \citet{cliff1981spatial}; $p$-values are two-sided normal. All entries are parsed from \texttt{results/sqr\_multi\_segment\_output.txt}, the preserved log of the original run of \texttt{scripts/05\_sqr\_multi\_segment.py}, by \texttt{scripts/06\_tables\_figures\_from\_log.py}.19\end{tablenotes}20\end{threeparttable}21\end{table}22