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% UQO Working Paper No. 20 — From Listing Images to Property Valuation
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% METADATA
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\newcommand{\WPnumber}{20}
\newcommand{\WPtitle}{From Listing Images to Property Valuation: A Multimodal AI Framework for Automated Building Characterization and the Cost Approach}
\newcommand{\WPsubtitle}{Evidence from Québec single-family listings matched to the assessment roll}
\newcommand{\WPdate}{October 2026}
\newcommand{\WPversion}{1.0}
\newcommand{\WPabstract}{%
The cost approach to value requires a specification of the improvements---materials, finish grade, condition, renovations---that assessment rolls do not record and that appraisers obtain by inspection. We ask whether that specification can be extracted from the photographs and text of a for-sale listing by general-purpose vision--language models, and what a transparent cost indication built on it is worth. We formalise an extraction-to-valuation framework with five evidence classes (administrative, visual, proxy, text-claimed, latent), deterministic cross-image aggregation, text--image--record reconciliation, a fully sourced cost schedule and uncertainty propagation, and we apply it to \nSample{} Québec house listings matched within 15~m to their roll unit (\nImagesSample{} photographs, three extractor tiers, \nRefsVerified{} verified references). Visible exterior attributes are extracted with high agreement against broker fields (roof covering \accroofcovering\%, garage type \accgaragetypeattacheddetached\%, cladding \acccladdingprimarybrokersidings\%) but storeys agree with the roll only loosely ($\kappa=\kappastoreysvisible$), and the most cost-relevant interior evidence is simply not shown: within the first ten photographs, \shownBasement\% of listings show a basement and \shownBathroom\% a bathroom, a missing-not-at-random structure that no extractor can overcome. Relative to a roll-only baseline, image and text attributes raise the median cost indication and move its ratio to the asking price from \priceRatiosonnetBZeroB{} to \priceRatiosonnetBThreeB{} (median absolute deviation \priceMdapesonnetBZeroB\% to \priceMdapesonnetBThreeB\%), mainly through condition-adjusted depreciation; the gain is real but modest, and the level of the indication depends more on the published unit-cost anchor than on anything seen in the images. A calibration against assessor building values on five metropolitan areas transfers to a geographic hold-out and implies a hard cost near \$300 per square foot, consistent with 2026 Québec industry guides and well above a Montréal hard-cost band. We release the ontology, prompts, schedule and code, and we are explicit about what the data cannot establish: no expert labels exist, latent defects are unobservable, and the cost indication is a screening tool, not an appraisal.%
}
\newcommand{\WPkeywords}{cost approach, automated valuation, vision--language models, listing photographs, depreciation, assessment roll, Québec}
\newcommand{\WPjel}{R31, R32, C45, C55, D83}

% --- 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}

\hypersetup{pdftitle={From Listing Images to Property Valuation: A Multimodal AI Framework for Automated Building Characterization and the Cost Approach},pdfauthor={Simon-Pierre Boucher}}

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\begin{document}

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\input{sections/introduction}
\input{sections/setting}
\input{sections/literature}
\input{sections/framework}
\input{sections/data}
\input{sections/methodology}
\input{sections/results_extraction}
\input{sections/results_cost}
\input{sections/robustness}
\input{sections/discussion}
\input{sections/limitations}
\input{sections/conclusion}

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