% ============================================================================
\section{Data}
\label{sec:data}
% ============================================================================

\subsection{Data Sources}
\label{subsec:data_sources}

We assemble a cross-section of residential listings from two complementary Quebec
platforms, both retrieved on 3~July~2026 via web-scraping actors. The first,
\textbf{Realtor.ca}, is the national Multiple Listing Service (MLS) portal and
provides broker-listed inventory (27{,}302 raw records). The second,
\textbf{DuProprio}, is the dominant for-sale-by-owner platform in Quebec (7{,}693
raw records). Pooling the two sources broadens coverage across price segments and
sale channels---brokered and FSBO listings differ systematically in seller
composition and marketing \citep{hendel2009mls}---and a source indicator absorbs any
systematic level difference between them. The price we observe is the \emph{asking}
price; Section~\ref{subsec:limits} discusses the interpretation of hedonic
coefficients estimated on list rather than transaction prices
\citep{horowitz1992list,knight2002listing}.

Each record is a structured JSON object containing a price, an address, a
property-attribute block, a free-text description, a list of features, and an array
of image URLs. The two sources differ in their auxiliary fields: Realtor.ca records
include room-by-room dimensions but no geographic coordinates, whereas DuProprio
records include coordinates and captioned images but no room block. We construct a
unique key by concatenating the source and the listing identifier. Because the data
form a single-date cross-section, no time dimension is modeled.

\subsection{Sample Construction}
\label{subsec:sample}

We restrict attention to \emph{houses}, defined as single-family dwellings and
townhouses. On Realtor.ca this corresponds to building types \textit{House} and
\textit{Row/Townhouse}; on DuProprio it corresponds to the property types
\textit{Home}, \textit{Bungalow}, \textit{Semi Detached}, \textit{Intergenerational},
\textit{Country Home}, \textit{Cottage}, \textit{Villa}, and \textit{Townhouse}.
Condominiums, income properties (duplex through quintuplex), vacant land, farms,
commercial properties, and mobile homes are excluded. After requiring a non-missing,
positive price, the pooled sample contains 19{,}273 houses.

\subsection{Outlier Cleaning}
\label{subsec:cleaning}

Scraped prices contain parsing artifacts spanning several orders of magnitude (the
raw minimum and maximum are \$899 and \$368~billion). We apply conservative,
transparent bounds rather than model-based trimming: prices are retained in the
\$50{,}000--\$15{,}000{,}000 range; living area in 300--20{,}000~ft$^2$; lot area in
200--5{,}000{,}000~ft$^2$; bedrooms in 0--15 and bathrooms in 0--12. When a single
attribute violates its bound it is set to missing (and later imputed) rather than
deleting the observation, preserving sample size. Finally, we trim the
price-per-square-foot distribution at its 0.5th and 99.5th percentiles
(approximately \$73--\$1{,}276/ft$^2$) among listings with a measured area, removing
residual price- or area-parsing errors. The cleaned sample contains
\textbf{19{,}123 houses}, of which 80.3\% originate from Realtor.ca.

\subsection{Summary Statistics}
\label{subsec:summary_stats}

Table~\ref{tab:summary_stats} reports descriptive statistics for the key variables.
The median list price is \$599{,}900. The distribution of living area is right-skewed,
motivating the logarithmic transformation adopted in Section~\ref{sec:methodology}.
Living area is reported for only a minority of Realtor.ca listings; where it is
missing we reconstruct it from room-level dimensions (Section~\ref{subsec:structural}),
and where neither is available we rely on the area-free specification.

\input{tables/summary_stats}

Table~\ref{tab:ai_summary} previews the machine-generated attributes described in
Section~\ref{sec:methodology}: roughly a quarter of listings are described as recently
renovated, the mean visual condition and curb-appeal scores are 7.4 and 7.2 on a
10-point scale, and 43\% of façades display a visual luxury cue.

\input{tables/ai_summary}