\begin{tabular}{lccccc}
\toprule
 & \textbf{(R1)} & \textbf{(R2)} & \textbf{(R3)} & \textbf{(R4)} & \textbf{(R5)} \\
Dep.\ var: & \textit{log\_rent} & \textit{log\_rent} & \textit{log\_rent} & \textit{log\_rent} & \textit{log\_rent} \\
\midrule
Airbnb count (500m) & 0.0039*** & 0.0039*** & 0.0032*** &  & 0.0013*** \\
 & (0.0002) & (0.0001) & (0.0003) &  & (0.0003) \\[4pt]
log(1 + Airbnb count 500m) &  &  &  & 0.0484*** &  \\
 &  &  &  & (0.0025) &  \\[4pt]
Airbnb count (500m--1km ring) &  &  &  &  & 0.0018*** \\
 &  &  &  &  & (0.0002) \\[4pt]
Bedrooms & 0.1276*** & 0.1276*** & 0.1271*** & 0.1296*** & 0.1322*** \\
 & (0.0046) & (0.0043) & (0.0076) & (0.0046) & (0.0047) \\[4pt]
Bathrooms & 0.2287*** & 0.2287*** & 0.2103*** & 0.2274*** & 0.2246*** \\
 & (0.0126) & (0.0291) & (0.0202) & (0.0125) & (0.0125) \\[4pt]
Interior size (100 sq ft) &  &  & 0.0044*** &  &  \\
 &  &  & (0.0017) &  &  \\[4pt]
\midrule
Standard errors & HC1 & City cluster & HC1 & HC1 & HC1 \\
Building type \& city FE & Yes & Yes & Yes & Yes & Yes \\
Observations & 7,925 & 7,925 & 3,531 & 7,925 & 7,925 \\
R$^2$ & 0.5697 & 0.5697 & 0.6044 & 0.5726 & 0.5786 \\
\bottomrule
\end{tabular}
\parbox{\textwidth}{\footnotesize Standard errors in parentheses: HC1 except column (R2), which clusters by city. All columns control for bedrooms, bathrooms, building type, and city fixed effects; column (R3) adds interior size (per 100 sq ft) on the subsample reporting it. Column (R5) includes the 500m count and the count in the 500m--1km annulus jointly. $^{***}p<0.01$; $^{**}p<0.05$; $^{*}p<0.10$.}
