\begin{tabular}{lcccccc}
\toprule
 & \textbf{$\tau=0.10$} & \textbf{$\tau=0.25$} & \textbf{$\tau=0.50$} & \textbf{$\tau=0.75$} & \textbf{$\tau=0.90$} & \textbf{OLS} \\
Dep.\ var: & \textit{log\_rent} & \textit{log\_rent} & \textit{log\_rent} & \textit{log\_rent} & \textit{log\_rent} & \textit{log\_rent} \\
\midrule
const & 6.8249*** & 6.9035*** & 7.0339*** & 7.1237*** & 7.2461*** & 7.0508*** \\
 & (0.0258) & (0.0211) & (0.0209) & (0.0221) & (0.0328) & (0.0197) \\[4pt]
airbnb\_count\_500m & 0.0037*** & 0.0034*** & 0.0035*** & 0.0041*** & 0.0047*** & 0.0038*** \\
 & (0.0003) & (0.0003) & (0.0002) & (0.0002) & (0.0003) & (0.0002) \\[4pt]
bedrooms & 0.1236*** & 0.1034*** & 0.1071*** & 0.1360*** & 0.1418*** & 0.1152*** \\
 & (0.0058) & (0.0043) & (0.0039) & (0.0038) & (0.0055) & (0.0046) \\[4pt]
bathrooms & 0.2411*** & 0.2796*** & 0.2748*** & 0.2669*** & 0.2807*** & 0.2571*** \\
 & (0.0103) & (0.0076) & (0.0072) & (0.0076) & (0.0115) & (0.0125) \\[4pt]
bt\_House & 0.0608*** & 0.1079*** & 0.1382*** & 0.1079*** & 0.1094*** & 0.1201*** \\
 & (0.0139) & (0.0119) & (0.0120) & (0.0131) & (0.0205) & (0.0131) \\[4pt]
bt\_Row / Townhouse & 0.1310*** & 0.0858** & 0.1602*** & 0.2501*** & 0.2274*** & 0.1753*** \\
 & (0.0468) & (0.0383) & (0.0383) & (0.0417) & (0.0625) & (0.0378) \\[4pt]
\midrule
City FE (top 5) & Yes & Yes & Yes & Yes & Yes & Yes \\
Observations & 7925 & 7925 & 7925 & 7925 & 7925 & 7925 \\
(Pseudo-)R$^2$ & 0.2251 & 0.2529 & 0.2976 & 0.3294 & 0.3402 & 0.4849 \\
\bottomrule
\end{tabular}
\parbox{\textwidth}{\footnotesize Standard errors in parentheses. OLS uses HC1 robust SE. $^{***}p<0.01$; $^{**}p<0.05$; $^{*}p<0.10$. City FE limited to the 5 largest cities (others grouped).}
