% ============================================================================
\section{Conclusion}
\label{sec:conclusion}
% ============================================================================

We have estimated a hedonic price model for Quebec houses that augments the standard
set of structural, locational, and amenity attributes with two machine-generated
feature layers: structured specifications extracted from free-text descriptions by a
large language model, and objective visual attributes extracted from façade
photographs by a vision--language model. Schema-constrained decoding renders these
outputs as ordinary, reproducible econometric variables, obtained at a marginal cost
of a fraction of a cent per listing. The central message is methodological:
general-purpose AI models, disciplined by strict output schemas, function as a
low-cost feature-engineering technology that measurably expands the characteristics
set available to hedonic analysis.

The gains are economically meaningful. The two AI layers add nearly four points of
in-sample $R^2$ to an already rich log--linear specification (0.717 to 0.755 with
living area observed), and they raise the out-of-sample accuracy of every estimator
in our ladder, from OLS to gradient boosting and an embedding-based residual neural
network; the best blend attains an out-of-sample $R^2$ of 0.779 with a median
absolute prediction error of 14.0\%. The façade-vision layer alone contributes the
largest increment of any feature block (2.7 points for gradient boosting, 5.8 for
OLS), confirming that image-derived quality is genuinely orthogonal to text and
structured fields. Crucially for hedonic practice, the extracted attributes carry
interpretable implicit prices---curb appeal, renovation level, luxury cues, and
exterior materials all price sensibly and survive municipality-clustered
inference---so the technology augments the classical regression rather than
replacing it with a black box.

Three extensions are natural. First, the same schema-constrained approach scales
from a single façade image to a small panel of interior photographs (kitchen,
bathroom, living space), which our design already accommodates. Second, the
DuProprio coordinates permit continuous spatial features---distance to the urban
core, to water, or to amenities---that would refine the geography beyond discrete
city effects. Third, and most important for external validity, linking listings to
registry transaction prices would establish how much of the AI-measured quality
premium survives bargaining. For appraisal, automated valuation, and property-tax
assessment---applications that consume hedonic models at scale---the practical
implication is immediate: the unstructured majority of a listing's information
content is no longer out of reach.