% ============================================================================
\section{Related Literature}
\label{sec:literature}
% ============================================================================

This paper connects four strands of research.

The first is the classical \textbf{hedonic pricing} literature. Building on
\citet{lancaster1966new}, \citet{rosen1974hedonic} formalized the market for
differentiated goods as a market for their underlying characteristics, providing the
theoretical foundation for interpreting regression coefficients as implicit prices.
The empirical housing literature that followed is vast; \citet{sirmans2005composition}
survey the characteristics most commonly included in hedonic house-price models and
document substantial heterogeneity in specification, \citet{malpezzi2003hedonic}
provides a critical review of functional-form and identification choices, and
\citet{hill2013hedonic} surveys the use of hedonic methods for house-price
measurement. A recurring theme in this literature is omitted-variable bias from
unmeasured quality: the state of repair, the standard of finishes, and the visual
quality of a dwelling are priced by the market but rarely recorded in the structured
fields available to the econometrician. Our contribution speaks directly to this
measurement problem. Our log--linear specification, with logged continuous attributes
and a dependent variable in logs, follows the mainstream of this literature.

The second strand concerns \textbf{spatial structure, functional form, and
prediction}. \citet{can1992spatial} and \citet{anselin1988spatial} emphasize that
unobserved locational amenities induce spatial dependence that, if ignored, biases
coefficient estimates; we absorb the first-order component of this problem through a
rich set of municipality fixed effects and cluster inference at the same level.
On the prediction side, \citet{bin2004prediction} compares parametric and
semi-parametric hedonic predictors, \citet{mullainathan2017machine} frame
machine-learning methods as estimators of the conditional expectation
$\mathbb{E}[P\mid\mathbf{z}]$ well suited to prediction tasks, and
\citet{kok2017bigdata} document the rise of automated valuation models in industry.
This literature generally finds that flexible learners beat linear hedonics on
accuracy at some cost in interpretability; our estimator ladder
(Section~\ref{sec:benchmark}) reproduces that pattern on a common feature set, and
our focus is on how much of the gap is closed not by a better \emph{estimator} but by
better \emph{measurement}.

The third strand is the use of \textbf{unstructured listing content}---text and
images---in real-estate valuation. \citet{gentzkow2019text} survey text-as-data
methods in economics. In real estate specifically, \citet{nowak2017textual} show that
words in MLS remarks predict transaction prices and reduce hedonic pricing errors,
and \citet{shen2021information} extract quality signals from property descriptions
with supervised machine learning, finding that the text carries information about
unobserved quality that is capitalized into prices. On the image side,
\citet{ahmed2016house} combine visual and textual features for price estimation;
\citet{poursaeed2018vision} show that learned measures of interior and exterior
quality carry independent predictive power; \citet{glaeser2018vision} use street-level
imagery of Boston houses to measure exterior quality and study its interaction with
incentives; \citet{johnson2020curb} construct a curb-appeal index from Google Street
View images and estimate its capitalization into sale prices; and
\citet{lindenthal2021architecture} classify architectural styles from photographs and
document style premia. Relative to this work, our approach differs in the
\emph{technology of measurement}: rather than training bespoke supervised models on
labeled data---which requires labels, task-specific engineering, and yields opaque
scores---we use general-purpose large language and vision--language models with
schema-constrained decoding to produce a fixed set of interpretable, human-readable
attributes (e.g., ``renovated kitchen,'' ``brick façade,'' a 1--10 curb-appeal score)
at a marginal cost of a fraction of a cent per listing, and we embed those attributes
in a transparent hedonic regression whose coefficients can be read as implicit
prices. The models never observe the price, so the extracted attributes cannot
mechanically encode the outcome.

The fourth strand is the \textbf{microstructure of listing markets}, which frames two
institutional features of our data. Our sample pools a brokered MLS platform
(Realtor.ca) with a for-sale-by-owner platform (DuProprio); \citet{hendel2009mls}
compare exactly these two market forms and document differences in seller
composition, marketing, and outcomes, motivating our platform indicator and
platform-specific robustness checks. And because we observe asking rather than
transaction prices, the interpretation of our dependent variable rests on the
list-price literature: the asking price is a strategic choice that anchors search and
bargaining \citep{horowitz1992list,knight2002listing,han2015microstructure}. We
return to this limitation in Section~\ref{sec:robustness}.