% Author: Simon-Pierre Boucher — contact@spboucher.ai
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\section{Conclusion}
\label{sec:conclusion}
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Using a nationwide cross-section of 140{,}931 MLS listings and 1{,}153 absorbed
neighbourhood fixed effects, we estimate a grand hedonic model of the Canadian
residential housing market. The exercise yields three robust conclusions. First,
location dominates: resolving geography from the provincial to the neighbourhood scale
raises explained price variation from 57\% to 77\%, and location as a whole accounts for
roughly thirty percentage points of $R^2$---more than every structural attribute
combined, and a listing-level counterpart to the large land shares documented in
aggregate data \citep{davis2007price,knoll2017no}. Second, the structural implicit
prices behave as hedonic theory predicts and are strikingly stable across samples:
living area carries an elasticity near 0.55, each full bathroom adds about 11--15\%, and
bedroom counts are economically negligible once floor space is held
fixed---magnitudes squarely within the range catalogued by decades of hedonic work
\citep{sirmans2005composition}. Third, the neighbourhood premia are vast---a factor of
roughly nine separates the most and least expensive FSAs---and the model translates into
a credible valuation tool, predicting held-out prices with an out-of-sample $R^2$ of
0.76 and a median error of 16\%. These conclusions survive an extensive battery of
checks: the implicit prices are stable across subsamples and across the price
distribution, the neighbourhood effects absorb 82\% of the spatial autocorrelation in
residuals, floor space displays diminishing returns, value decays with distance to major
metros as the monocentric tradition predicts, and the structural prices transfer across
provinces in leave-one-province-out cross-validation.

The reading we propose is deliberately modest and, we believe, well supported: in
Canada, the single largest priced component of a home is its neighbourhood. That fact
is simultaneously a measurement result (for assessment, taxation and index
construction), a validation result (a transparent fixed-effects design removes the
spatial dependence that motivates far more elaborate machinery), and a policy result
(the premium dispersion that supply constraints and agglomeration generate is the
dominant term in the decomposition). Section~\ref{sec:discussion} details the
limitations---list prices, unobserved quality, noisy lot data, a single
cross-section---and the research agenda they imply. Even within those bounds, the model
provides a transparent, reproducible benchmark for automated valuation, market
monitoring and the welfare analysis of local amenities across the Canadian residential
market.

\vspace{0.6em}
\noindent\textbf{Reproducibility.} Data engineering was performed in \texttt{DuckDB} and
\texttt{pandas}; estimation used \texttt{statsmodels} (cluster-robust OLS) and
\texttt{linearmodels} (absorbing least squares); figures were produced in
\texttt{matplotlib}. All tables and figures are generated programmatically from the
source database by the numbered scripts in the accompanying repository.