spb/wp5_uqo

1\begin{tabular}{lcccccc}2\toprule3 & \textbf{(F1)} & \textbf{(F2)} & \textbf{(F3)} & \textbf{(F4)} & \textbf{(F5)} & \textbf{(F6)} \\4Dep.\ var: & \multicolumn{6}{c}{\textit{log rent}} \\5\midrule6Any Airbnb within 500 m (0/1) &  &  &  & -0.0279*** &  &  \\7 &  &  &  & (0.0088) &  &  \\[4pt]8Airbnb count (500 m) & 0.0039*** & 0.0008** &  & 0.0010*** &  & 0.0003 \\9 & (0.0002) & (0.0004) &  & (0.0004) &  & (0.0006) \\[4pt]10Airbnb count (250 m) &  &  & 0.0008 &  &  &  \\11 &  &  & (0.0009) &  &  &  \\[4pt]12log(1 + Airbnb count, 500 m) &  &  &  &  & 0.0022 &  \\13 &  &  &  &  & (0.0053) &  \\[4pt]14Bedrooms & 0.1276*** & 0.1501*** & 0.1502*** & 0.1500*** & 0.1502*** & 0.1479*** \\15 & (0.0046) & (0.0047) & (0.0047) & (0.0047) & (0.0047) & (0.0083) \\[4pt]16Bathrooms & 0.2287*** & 0.1980*** & 0.1978*** & 0.1979*** & 0.1978*** & 0.1892*** \\17 & (0.0125) & (0.0120) & (0.0120) & (0.0120) & (0.0120) & (0.0200) \\[4pt]18Interior size (100 sq ft) &  &  &  &  &  & 0.0040** \\19 &  &  &  &  &  & (0.0016) \\[4pt]20\midrule21Fixed effects & City & FSA & FSA & FSA & FSA & FSA \\22FE groups & 187 & 289 & 289 & 289 & 289 & 222 \\23Observations & 7,860 & 7,883 & 7,883 & 7,883 & 7,883 & 3,485 \\24R$^2$ & 0.5636 & 0.6306 & 0.6303 & 0.6313 & 0.6303 & 0.6505 \\25Within R$^2$ & 0.4703 & 0.5189 & 0.5185 & 0.5197 & 0.5184 & 0.5326 \\26\bottomrule27\end{tabular}28\parbox{\textwidth}{\footnotesize Heteroskedasticity-robust (HC1) standard errors in parentheses. All columns control for bedrooms, bathrooms and building type; fixed effects are absorbed (within transformation) and singleton groups are dropped. FSA = forward sortation area (first three characters of the postal code). Column (F3) uses the 250\,m count; column (F6) adds interior size on the subsample reporting it. $^{***}p<0.01$; $^{**}p<0.05$; $^{*}p<0.10$.}29