{"status": "ok", "message-type": "work", "message-version": "1.0.0", "message": {"indexed": {"date-parts": [[2026, 7, 8]], "date-time": "2026-07-08T06:20:13Z", "timestamp": 1783491613723, "version": "3.55.0"}, "reference-count": 41, "publisher": "Springer Science and Business Media LLC", "license": [{"start": {"date-parts": [[2026, 7, 8]], "date-time": "2026-07-08T00:00:00Z", "timestamp": 1783468800000}, "content-version": "tdm", "delay-in-days": 0, "URL": "https://creativecommons.org/licenses/by/4.0"}, {"start": {"date-parts": [[2026, 7, 8]], "date-time": "2026-07-08T00:00:00Z", "timestamp": 1783468800000}, "content-version": "vor", "delay-in-days": 0, "URL": "https://creativecommons.org/licenses/by/4.0"}], "funder": [{"name": "NTNU Norwegian University of Science and Technology"}], "content-domain": {"domain": ["link.springer.com"], "crossmark-restriction": false}, "short-container-title": ["J Real Estate Finan Econ"], "abstract": "<jats:title>Abstract</jats:title>\n                  <jats:p>Housing condition is a key determinant of property value, yet it is often omitted from modern automated valuation models (AVMs). This study develops a computer vision framework to extract room-specific condition signals from listing images and examines their economic relevance for housing valuation. Using 15,702 condominium transactions from Oslo, Norway (2022–2023), matched with 393,914 images, we classify images by room type, estimate condition on a 1–5 scale, and incorporate these measures into hedonic linear regression and XGBoost AVMs. The results show that computer-vision-based condition improves predictive performance, with gains comparable to those from human-labeled condition. Moreover, housing condition is positively associated with sales prices, with stronger effects for kitchens and bathrooms and in lower-priced homes. Explainable AI analysis further indicates that the models rely on intuitive and economically meaningful visual patterns.</jats:p>", "DOI": "10.1007/s11146-026-10071-w", "type": "journal-article", "created": {"date-parts": [[2026, 7, 8]], "date-time": "2026-07-08T06:06:08Z", "timestamp": 1783490768000}, "update-policy": "https://doi.org/10.1007/springer_crossmark_policy", "source": "Crossref", "is-referenced-by-count": 0, "title": ["Giving Eyes to Automated Valuation Models: Computer Vision Assessment of Interior Home Condition"], "prefix": "10.1007", "author": [{"ORCID": "https://orcid.org/0009-0006-7875-8569", "authenticated-orcid": false, "given": "Vebjørn", "family": "Almaas", "sequence": "first", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"ORCID": "https://orcid.org/0000-0002-0601-5268", "authenticated-orcid": false, "given": "Are", "family": "Oust", "sequence": "additional", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"ORCID": "https://orcid.org/0009-0007-2481-3355", "authenticated-orcid": false, "given": "Arne Johan", "family": "Pollestad", "sequence": "additional", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}], "member": "297", "published-online": {"date-parts": [[2026, 7, 8]]}, "reference": [{"issue": "3", "key": "10071_CR1", "doi-asserted-by": "publisher", "first-page": "401", "DOI": "10.1007/s11146-009-9219-6", "volume": "43", "author": "JD Benefield", "year": "2011", "unstructured": "Benefield, J. 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