spb/wp1_uqo

1% ============================================================================2\section{Data}3\label{sec:data}4% ============================================================================56\subsection{Data Source and Institutional Context}78The data are drawn from Centris, the centralized listing platform operated by the Qu\'ebec Professional Association of Real Estate Brokers (QPAREB). Centris aggregates virtually all brokered residential listings in the province of Quebec, functioning as the province's de facto multiple listing service (MLS). Unlike local MLS systems in other Canadian provinces, Centris provides province-wide coverage under a single platform, ensuring comprehensive geographic representation. All licensed real estate brokers in Quebec are required to list properties through the Centris system, minimizing selection bias that can arise from voluntary listing platforms.910We collected 42,481 listings across four property types: 18,920 single-family houses, 9,347 condominiums, 4,619 multiplexes (duplexes through sixplexes), and 9,595 vacant land parcels. The data were collected in May 2025 and represent active listings at the time of collection. Each listing includes detailed property characteristics (structural attributes, lot dimensions, amenities), geographic coordinates (latitude and longitude), listing price, and broker-classified neighborhood amenity tags.1112Listing prices rather than transaction prices are used, consistent with the automated valuation model literature \citep{gorjian2025systematic}. While listing prices may exceed transaction prices due to bargaining, the hedonic coefficients remain unbiased under the assumption that the listing-to-sale ratio is uncorrelated with the amenity variables of interest. \citet{knight2002listing} estimate that listing prices in U.S.\ markets exceed sale prices by approximately 3--5\% on average, with the markup relatively stable across housing attributes. \citet{allen2014information} study the Canadian housing market and find that listing price behavior is consistent with strategic pricing, where the listing premium is primarily a function of local market conditions (days on market, inventory levels) rather than property-specific characteristics. To the extent that any listing premium is absorbed by the intercept and regional dummies, our amenity coefficient estimates remain valid.1314\subsection{Variables}1516Table~\ref{tab:variables} provides detailed variable definitions and expected signs based on the prior literature.1718\input{tables/variables}1920Structural variables include bedrooms, bathrooms (full plus half-baths weighted at 0.5), stories, floor area (in square feet, log-transformed), land area (in square feet, log-transformed), parking spaces, and a garage dummy. For the plex segment, we add total unit count. For the land segment, structural variables are unavailable and the model relies on land area, distance, amenities, and regional dummies.2122Locational variables include latitude, longitude, and the log of Euclidean distance to downtown Montreal (45.5017\textdegree N, 73.5673\textdegree W), computed using the flat-Earth approximation for distances within Quebec. Regional dummies classify properties into Montreal (including Laval, South Shore, and North Shore suburbs), Qu\'ebec City (including L\'evis), Gatineau (including Hull and Aylmer), and Regions (the omitted reference category for houses and plex; Gatineau for models where it appears). The Montreal classification includes over 45 municipalities within the Census Metropolitan Area.2324Amenity variables are binary indicators derived from the Centris ``Amenities Nearby'' field: highway, hospital, park, school, public transit, and golf course. These capture proximity to the nearest facility of each type as classified by the listing broker. The binary nature of these variables is a data limitation---continuous distance measures would provide more precise capitalization estimates---but the broker classification ensures consistency across listings and captures the broker's professional judgment about which amenities are relevant to the property's market positioning.2526\subsection{Sample Construction}2728Table~\ref{tab:sample} reports the sample construction.2930\input{tables/sample}3132From the 42,481 raw listings, we retain observations with valid prices, geographic coordinates, and the minimum variable set for each segment. We trim the top and bottom 0.5\% of the log-price distribution and relevant size variables to mitigate outlier influence, following the standard practice in the hedonic literature \citep{sirmans2005composition}. The final analytical samples are: 6,250 houses, 1,031 condos, 3,755 multiplexes, and 9,239 land parcels.3334\subsection{Descriptive Statistics}3536Table~\ref{tab:descriptive} presents descriptive statistics for the single-family house segment, the largest and most attribute-rich sample. The median house is priced at \$579,000 with 3 bedrooms, 2 bathrooms, 1,539 sq.ft.\ of living area, and a lot of 11,249 sq.ft. Nearly 57\% have a garage. Among amenities, school proximity is the most common (69.2\%), followed by parks (60.3\%) and highways (48.5\%). The right-skewed price distribution (mean/median ratio = 1.34) confirms the appropriateness of the log transformation.3738\input{tables/descriptive_houses}3940Table~\ref{tab:descriptive_other} presents summary statistics for the remaining three segments.4142\input{tables/descriptive_other}4344The four segments exhibit distinct characteristics. Condos are concentrated in Montreal (62.4\%) with the highest transit prevalence (58.6\%), reflecting their urban orientation. Multiplexes have the highest mean price (\$935,000), reflecting multi-unit investment value. Land parcels are predominantly rural (78.9\% in ``Regions'') with the lowest amenity prevalence across all categories, consistent with their peripheral locations.4546\subsection{Amenity Correlations}4748Table~\ref{tab:amenity_corr} reports pairwise correlations among amenity indicators for the house segment. The highest correlation (0.38) is between school and park proximity, reflecting the co-location of these amenities in residential neighborhoods. No pairwise correlation exceeds 0.40, suggesting that multicollinearity among amenity variables is not a concern.4950\input{tables/amenity_corr}51