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While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model- specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre- factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders.</jats:p>", "DOI": "10.2139/ssrn.4706470", "type": "posted-content", "created": {"date-parts": [[2024, 2, 13]], "date-time": "2024-02-13T09:42:40Z", "timestamp": 1707817360000}, "source": "Crossref", "is-referenced-by-count": 2, "title": ["Towards a Better Uncertainty Quantification in Automated Valuation Models"], "prefix": "10.2139", "author": [{"ORCID": "https://orcid.org/0009-0007-2481-3355", "authenticated-orcid": true, "given": "Arne Johan", "family": "Pollestad", "sequence": "first", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"given": "Arild", "family": "Brandrud Næss", "sequence": "additional", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"ORCID": "https://orcid.org/0000-0002-0601-5268", "authenticated-orcid": true, "given": "Are", "family": "Oust", "sequence": "additional", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}], "member": "78", "reference": [{"key": "ref1", "doi-asserted-by": "crossref", "first-page": "243", "DOI": "10.1016/j.inffus.2021.05.008", "article-title": "A review of uncertainty quantification in deep learning: Techniques, applications and challenges", "volume": "76", "author": "M Abdar", "year": "2021", "journal-title": "Information Fusion"}, {"key": "ref2", "author": "A N Angelopoulos", "year": "2022", "journal-title": "A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification"}, {"key": "ref3", "author": "A Baum", "year": "2021", "journal-title": "The future of automated real estate valuations (AVMs)"}, {"issue": "1", "key": "ref4", "doi-asserted-by": "crossref", "first-page": "40", "DOI": "10.1007/s11146-019-09712-0", "article-title": "Appraisal Accuracy and Automated Valuation Models in Rural Areas", "volume": "60", "author": "A N Bogin", "year": "2020", "journal-title": "The Journal of Real Estate Finance and Economics"}, {"key": "ref5", "doi-asserted-by": "crossref", "first-page": "123", "DOI": "10.1007/BF00058655", "article-title": "Bagging predictors", "volume": "24", "author": "L Breiman", "year": "1996", "journal-title": "Machine Learning"}, {"key": "ref6", "first-page": "49", "volume": "24", "author": "L Breiman", "year": "1996", "journal-title": "Stacked regressions. 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BE2-Bootstrap 605"}], "deposited": {"date-parts": [[2026, 8, 16]], "date-time": "2026-08-16T15:41:18Z", "timestamp": 1786894878000}, "score": 39.71148, "resource": {"primary": {"URL": "https://www.ssrn.com/abstract=4706470"}}, "issued": {"date-parts": [[2024]]}, "references-count": 44, "URL": "https://doi.org/10.2139/ssrn.4706470", "published": {"date-parts": [[2024]]}, "subtype": "preprint"}, {"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. D., Cain, C. L., & Johnson, K. H. (2011). On the relationship between property price, time-on-market, and photo depictions in a multiple listing service. The Journal of Real Estate Finance and Economics, 43(3), 401–422. https://doi.org/10.1007/s11146-009-9219-6", "journal-title": "The Journal of Real Estate Finance and Economics"}, {"issue": "2", "key": "10071_CR2", "doi-asserted-by": "publisher", "first-page": "139", "DOI": "10.53383/100319", "volume": "24", "author": "K Birkeland", "year": "2021", "unstructured": "Birkeland, K., D’Silva, A. D., Füss, R. A., & Oust, A. (2021). The predictability of house prices: Human against machine. International Real Estate Review, 24(2), 139–183.", "journal-title": "International Real Estate Review"}, {"key": "10071_CR3", "doi-asserted-by": "crossref", "unstructured": "Bjørgve, E., Oust, A., Pollestad, A. J., Sandnes, C., & Sønstebø, O. J. (2025). Comparing housing valuation techniques and stacked generalization: exploiting explainable AI. The Journal of Real Estate Finance and Economics. Forthcoming.", "DOI": "10.1007/s11146-025-10030-x"}, {"issue": "1–2", "key": "10071_CR4", "doi-asserted-by": "publisher", "first-page": "40", "DOI": "10.1007/s11146-019-09712-0", "volume": "60", "author": "AN Bogin", "year": "2020", "unstructured": "Bogin, A. N., & Shui, J. (2020). Appraisal accuracy and automated valuation models in rural areas. The Journal of Real Estate Finance and Economics, 60(1–2), 40–52. https://doi.org/10.1007/s11146-019-09712-0", "journal-title": "The Journal of Real Estate Finance and Economics"}, {"issue": "1", "key": "10071_CR5", "doi-asserted-by": "publisher", "first-page": "7", "DOI": "10.1111/1540-6229.12137", "volume": "45", "author": "E Buitelaar", "year": "2017", "unstructured": "Buitelaar, E., & Schilder, F. (2017). The economics of style: measuring the price effect of neo-Traditional architecture in housing. 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We also extract balcony size using text recognition. Then we utilize two models for AVM estimation – hedonic regression (linear OLS) and the non-linear XGBoost model – before measuring feature importance using SHAP.</jats:p>\n </jats:sec>\n <jats:sec>\n <jats:title>Findings</jats:title>\n <jats:p>Our experiments show that including balcony size as a feature in AVMs enhances model performance. We also find that balcony size has a positive but diminishing impact on property price.</jats:p>\n </jats:sec>\n <jats:sec>\n <jats:title>Research limitations/implications</jats:title>\n <jats:p>Demonstrating that image segmentation can be used for valuation in AVMs opens up the possibility to value numerous other aspects of dwelling floor plans.</jats:p>\n </jats:sec>\n <jats:sec>\n <jats:title>Practical implications</jats:title>\n <jats:p>The use of floor plans in AVMs can provide a more objective valuation of single apartment floor plan aspects, giving architects and developers better insights into how homes should be designed.</jats:p>\n </jats:sec>\n <jats:sec>\n <jats:title>Originality/value</jats:title>\n <jats:p>To our knowledge, this is the first attempt to extract features for use in AVMs from floor plans.</jats:p>\n </jats:sec>", "DOI": "10.1108/jerer-08-2024-0062", "type": "journal-article", "created": {"date-parts": [[2025, 1, 23]], "date-time": "2025-01-23T10:40:46Z", 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