% Author: Simon-Pierre Boucher — contact@spboucher.ai
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% UQO Working Paper No. 9
% A Grand Hedonic Model of the Canadian Housing Market
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\newcommand{\WPnumber}{9}
\newcommand{\WPtitle}{A Grand Hedonic Model of the Canadian Housing Market}
\newcommand{\WPsubtitle}{Decomposing the Value of Structure and Location across
  140{,}931 MLS Listings with High-Dimensional Neighbourhood Fixed Effects}
\newcommand{\WPdate}{May 2026}
\newcommand{\WPversion}{1.2}
\newcommand{\WPkeywords}{Hedonic pricing, Housing markets, Canada, Fixed effects,
  Spatial heterogeneity, Automated valuation}
\newcommand{\WPjel}{R31, R21, C21, C55}

\newcommand{\WPauthor}{Simon-Pierre Boucher}
\newcommand{\WPaffiliation}{D\'epartement des sciences administratives\\
  Universit\'e du Qu\'ebec en Outaouais}
\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
\newcommand{\WPaddress}{Gatineau -- Pavillon Alexandre-Tach\'e\\
  283, boulevard Alexandre-Tach\'e\\ Gatineau, Qu\'ebec, Canada J9A 1L8}

\newcommand{\WPabstract}{%
We estimate a large-scale hedonic price model for the Canadian residential real-estate
market using a cross-section of 140{,}931 active MLS listings drawn from the nine provinces
present in the data. A semi-logarithmic specification decomposes dwelling prices into
structural attributes, dwelling type and ownership form, and 1{,}153 neighbourhood
(Forward Sortation Area) fixed effects estimated by absorbing least squares. Moving from a
purely structural model to the full specification raises the explained share of price
variation from 46\% to 77\%, establishing that location is the dominant price determinant
in Canada: neighbourhood effects alone account for roughly thirty percentage points of
explanatory power. The living-area elasticity is 0.51--0.62, each full bathroom commands a
premium of about 11--15\%, and---conditional on floor space---bedroom counts are
economically negligible. The implicit prices are stable across alternative samples and
trimming rules, and the model delivers strong out-of-sample valuation accuracy (held-out
$R^2=0.76$; median absolute error of 16\%). The neighbourhood effects absorb the bulk of
the spatial dependence in prices---Moran's~$I$ falls from 0.46 to 0.08---and we further
document diminishing returns to floor space, an urban price gradient that decays with
distance to major metros, and quantile and cross-province heterogeneity in the implicit
prices. We map the resulting neighbourhood premia, which span a factor of roughly nine
between the most and least expensive areas.}

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