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% UQO Working Paper — Hedonic House Pricing with LLM & Vision Features
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\newcommand{\WPnumber}{14}
\newcommand{\WPtitle}{Hedonic House Pricing with Language-Model and Vision Features}
\newcommand{\WPsubtitle}{Evidence from Quebec Real-Estate Listings}
\newcommand{\WPdate}{October 2026}
\newcommand{\WPversion}{2.1}
\newcommand{\WPabstract}{%
  Hedonic house-price models are bounded by the characteristics an analyst can
  measure, yet much of a home's value is recorded only in the free-text description
  and photographs of its listing. We treat general-purpose AI models as a
  feature-engineering technology for hedonic analysis: a large language model
  converts each description into structured property specifications, and a
  vision--language model converts the façade photograph into visual attributes, both
  under strict JSON schemas that make the outputs auditable econometric variables.
  Using 19{,}123 Quebec house listings, the two AI layers raise the $R^2$ of an
  already rich log-linear hedonic specification with municipality fixed effects from
  0.717 to 0.755, 3.0 of the 3.8 points coming from the façade photograph, and they
  raise the out-of-sample $R^2$ of every estimator from OLS to gradient boosting and
  neural networks (best ensemble: $R^2$ of 0.779, median absolute error 14.0\%). The
  vision layer adds more to the linear model than switching from OLS to a random
  forest does. The extracted attributes carry economically sensible implicit
  prices---2.3\% per point of curb appeal, 3--4\% for brick or stone façades---that
  survive municipality-clustered inference, a cluster bootstrap, postal-code fixed
  effects, and a control for the language of the description, which itself carries a
  7\% conditional premium for English listings. We also document that
  for-sale-by-owner descriptions embed the asking price, so that the text layer's
  gain is credible only on the brokered platform, and that spatially grouped
  cross-validation lowers all accuracy levels by three to five points while
  preserving the AI increments.
}
\newcommand{\WPkeywords}{hedonic pricing, house prices, large language models, computer vision, text as data, automated valuation, Quebec}
\newcommand{\WPjel}{R31, C21, C55, R21}

% --- Author ---
\newcommand{\WPauthor}{Simon-Pierre Boucher}
\newcommand{\WPaffiliation}{%
  Département des sciences administratives\\
  Université du Québec en Outaouais}
\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
\newcommand{\WPaddress}{%
  Gatineau -- Pavillon Alexandre-Taché\\
  283, boulevard Alexandre-Taché\\
  Gatineau, Québec, Canada J9A 1L8}

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\input{sections/methodology}
\input{sections/results}
\input{sections/benchmark}
\input{sections/robustness}
\input{sections/conclusion}

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\section*{Declarations}

\paragraph{Funding.} This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.

\paragraph{Declaration of competing interest.} The author declares that he has no
known competing financial interests or personal relationships that could have
appeared to influence the work reported in this paper.

\paragraph{Data availability.} The listing data were collected from publicly
accessible web platforms (Realtor.ca and DuProprio) on 3~July~2026. The full
data-construction code, the extraction schemas, and the derived analysis dataset
(excluding copyrighted listing text and images), together with the raw JSON outputs
of the two AI models, are available from the author upon reasonable request. The
robustness analyses added in version 2.1 are produced by a single script
(\path{scripts/18_revision_language_leakage_spatial.py}) from the archived
extractions, without new API calls.

\paragraph{Use of AI models.} The large language model \texttt{gpt-4o-mini} and the
vision--language model \texttt{claude-haiku-4-5} are the object of study of this
paper and were used exclusively as documented in Section~\ref{sec:methodology} to
extract structured features from listing text and photographs. They were not used to
generate the analysis or the conclusions of the paper. An AI coding assistant was
used in the October 2026 revision, under the author's direction, to check the
manuscript for internal consistency and to run the additional robustness analyses;
all results were verified by the author.

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