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1key,first_author,year,title,container,doi_hint,topic,kind2rosen1974hedonic,Rosen,1974,Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition,Journal of Political Economy,10.1086/260169,B,article3hulten1981estimation,Hulten,1981,The estimation of economic depreciation using vintage asset prices,Journal of Econometrics,10.1016/0304-4076(81)90101-9,A,article4malpezzi1987microeconomic,Malpezzi,1987,Microeconomic Estimates of Housing Depreciation,Land Economics,10.2307/3146293,A,article5shilling1991measuring,Shilling,1991,Measuring depreciation in single-family rental and owner-occupied housing,Journal of Housing Economics,,A,article6knight1996depreciation,Knight,1996,"Depreciation, Maintenance, and Housing Prices",Journal of Housing Economics,,A,article7clapp1998residential,Clapp,1998,Residential Hedonic Models: A Rational Expectations Approach to Age Effects,Journal of Urban Economics,10.1006/juec.1997.2065,A,article8harding2007depreciation,Harding,2007,"Depreciation of housing capital, maintenance, and house price inflation: Estimates from a repeat sales model",Journal of Urban Economics,10.1016/j.jue.2006.07.007,A,article9wilhelmsson2008house,Wilhelmsson,2008,House price depreciation rates and level of maintenance,Journal of Housing Economics,10.1016/j.jhe.2007.09.001,A,article10coulson2008estimating,Coulson,2008,"Estimating time, age and vintage effects in housing prices",Journal of Housing Economics,10.1016/j.jhe.2008.03.002,A,article11chau2005adjusting,Chau,2005,Adjusting for Non-Linear Age Effects in the Repeat Sales Index,Journal of Real Estate Finance and Economics,10.1007/s11146-005-1370-0,A,article12francke2017land,Francke,2017,"Land, Structure and Depreciation",Real Estate Economics,10.1111/1540-6229.12146,A,article13bokhari2018characteristics,Bokhari,2018,Characteristics of Depreciation in Commercial and Multifamily Property: An Investment Perspective,Real Estate Economics,10.1111/1540-6229.12156,A,article14yoshida2020economic,Yoshida,2020,The economic depreciation of real estate: Cross-sectional variations and their return implications,Pacific-Basin Finance Journal,10.1016/j.pacfin.2020.101290,A,article15diewert2017alternative,Diewert,2017,Alternative Approaches to Commercial Property Price Indexes for Tokyo,Review of Income and Wealth,10.1111/roiw.12276,A,article16davis2007price,Davis,2007,The price and quantity of residential land in the United States,Journal of Monetary Economics,10.1016/j.jmoneco.2007.06.023,A,article17davis2008price,Davis,2008,The price of residential land in large US cities,Journal of Urban Economics,10.1016/j.jue.2007.02.003,A,article18glaeser2005why,Glaeser,2005,Why Have Housing Prices Gone Up?,American Economic Review,10.1257/000282805774669961,A,article19gyourko2006construction,Gyourko,2006,Construction Costs and the Supply of Housing Structure,Journal of Regional Science,10.1111/j.1467-9787.2006.00472.x,F,article20somerville1999residential,Somerville,1999,Residential Construction Costs and the Supply of New Housing: Endogeneity and Bias in Construction Cost Indexes,Journal of Real Estate Finance and Economics,10.1023/A:1007785312398,F,article21pagourtzi2003real,Pagourtzi,2003,Real estate appraisal: a review of valuation methods,Journal of Property Investment & Finance,10.1108/14635780310483656,A,article22crosby2000valuation,Crosby,2000,"Valuation accuracy, variation and bias in the context of standards and expectations",Journal of Property Investment & Finance,10.1108/14635780010324240,B,article23mcmillen2020assessment,McMillen,2020,Assessment Regressivity and Property Taxation,Journal of Real Estate Finance and Economics,10.1007/s11146-019-09715-x,B,article24paglin1972equity,Paglin,1972,Equity and the Property Tax: A New Conceptual Focus,National Tax Journal,,B,article25malpezzi2003hedonic,Malpezzi,2003,Hedonic Pricing Models: A Selective and Applied Review,Housing Economics and Public Policy,10.1002/9780470690680.ch5,B,chapter26hill2013hedonic,Hill,2013,"Hedonic Price Indexes for Residential Housing: A Survey, Evaluation and Taxonomy",Journal of Economic Surveys,10.1111/j.1467-6419.2012.00731.x,B,article27case2004modeling,Case,2004,Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models,Journal of Real Estate Finance and Economics,,B,article28bourassa2010predicting,Bourassa,2010,Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods,Journal of Real Estate Research,,B,article29zurada2011comparison,Zurada,2011,A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context,Journal of Real Estate Research,,B,article30kok2017big,Kok,2017,Big Data in Real Estate? From Manual Appraisal to Automated Valuation,Journal of Portfolio Management,10.3905/jpm.2017.43.6.202,B,article31mayer2019estimation,Mayer,2019,Estimation and updating methods for hedonic valuation,Journal of European Real Estate Research,10.1108/JERER-08-2018-0035,B,article32pace2020examining,Pace,2020,Examining the Information Content of Residuals from Hedonic and Spatial Models Using Trees and Forests,Journal of Real Estate Finance and Economics,10.1007/s11146-019-09724-w,B,article33krause2020uncertainty,Krause,2020,Uncertainty in automated valuation models: Error- vs model-based approaches,Journal of Property Research,10.1080/09599916.2020.1807587,B,article34bogin2019property,Bogin,2019,Property Renovations and Their Impact on House Price Index Construction,Journal of Real Estate Finance and Economics,10.1007/s11146-017-9641-0,B,article35shen2021information,Shen,2021,Information value of property description: A Machine learning approach,Journal of Urban Economics,10.1016/j.jue.2020.103299,B,article36nowak2017textual,Nowak,2017,Textual Analysis in Real Estate,Journal of Applied Econometrics,10.1002/jae.2550,B,article37baur2023automated,Baur,2023,Automated real estate valuation with machine learning models using property descriptions,Expert Systems with Applications,10.1016/j.eswa.2022.119147,B,article38levitt2008market,Levitt,2008,Market Distortions When Agents Are Better Informed: The Value of Information in Real Estate Transactions,Review of Economics and Statistics,10.1162/rest.90.4.599,B,article39oust2025assessing,Oust,2025,Assessing the explanatory power of dwelling condition in automated valuation models,Journal of Real Estate Research,,B,article40ahmed2016house,Ahmed,2016,House price estimation from visual and textual features,Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI),,C,inproceedings41poursaeed2018vision,Poursaeed,2018,Vision-based real estate price estimation,Machine Vision and Applications,10.1007/s00138-018-0922-2,C,article42bency2017beyond,Bency,2017,Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery,IEEE Winter Conference on Applications of Computer Vision (WACV),10.1109/WACV.2017.42,C,inproceedings43law2019take,Law,2019,Take a Look Around: Using Street View and Satellite Images and Machine Learning to Estimate House Prices,ACM Transactions on Intelligent Systems and Technology,10.1145/3342240,C,article44glaeser2018computer,Glaeser,2018,Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks,NBER Working Paper 25174,10.3386/w25174,C,techreport45naik2014streetscore,Naik,2014,Streetscore -- Predicting the Perceived Safety of One Million Streetscapes,IEEE Conference on Computer Vision and Pattern Recognition Workshops,10.1109/CVPRW.2014.121,C,inproceedings46lindenthal2021machine,Lindenthal,2021,"Machine Learning, Architectural Styles and Property Values",Journal of Real Estate Finance and Economics,10.1007/s11146-021-09845-1,C,article47johnson2020valuing,Johnson,2020,Valuing Curb Appeal,Journal of Real Estate Finance and Economics,10.1007/s11146-019-09713-z,C,article48kang2021understanding,Kang,2021,Understanding house price appreciation using multi-source big geo-data and machine learning,Land Use Policy,10.1016/j.landusepol.2020.104919,C,article49nouriani2022vision,Nouriani,2022,"Vision-based housing price estimation using interior, exterior & satellite images",Intelligent Systems with Applications,10.1016/j.iswa.2022.200081,C,article50koch2018visual,Koch,2018,Visual Estimation of Building Condition with Patch-level ConvNets,Proceedings of the 2018 ACM Workshop on Multimedia for Real Estate Tech,10.1145/3210499.3210526,C,inproceedings51zeppelzauer2018automatic,Zeppelzauer,2018,Automatic Prediction of Building Age from Photographs,Proceedings of the 2018 ACM International Conference on Multimedia Retrieval,10.1145/3206025.3206060,C,inproceedings52despotovic2019prediction,Despotovic,2019,Prediction and analysis of heating energy demand for detached houses by computer vision,Energy and Buildings,10.1016/j.enbuild.2019.03.036,C,article53almaas2026giving,Almaas,2026,Giving Eyes to Automated Valuation Models: Computer Vision Assessment of Interior Home Condition,Journal of Real Estate Finance and Economics,10.1007/s11146-026-10071-w,C,article54jerer2024platforms,,2024,From platforms to price: the impact of condition prediction using computer vision on real estate pricing models,Journal of European Real Estate Research,10.1108/JERER-03-2024-0013,C,article55benefield2011relationship,Benefield,2011,"On the Relationship Between Property Price, Time-on-Market, and Photo Depictions in a Multiple Listing Service",Journal of Real Estate Finance and Economics,10.1007/s11146-009-9219-6,C,article56you2017image,You,2017,Image-Based Appraisal of Real Estate Properties,IEEE Transactions on Multimedia,10.1109/TMM.2017.2710804,C,article57kucklick2023tackling,Kucklick,2023,Tackling the Accuracy-Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,ACM Transactions on Management Information Systems,10.1145/3567430,C,article58geerts2025performance,Geerts,2025,On the Performance of LLMs for Real Estate Appraisal,Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2025),,D,inproceedings59ji2023survey,Ji,2023,Survey of Hallucination in Natural Language Generation,ACM Computing Surveys,10.1145/3571730,D,article60li2023evaluating,Li,2023,Evaluating Object Hallucination in Large Vision-Language Models,Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing,10.18653/v1/2023.emnlp-main.20,D,inproceedings61angelopoulos2023conformal,Angelopoulos,2023,Conformal Prediction: A Gentle Introduction,Foundations and Trends in Machine Learning,10.1561/2200000101,D,article62antol2015vqa,Antol,2015,VQA: Visual Question Answering,IEEE International Conference on Computer Vision (ICCV),10.1109/ICCV.2015.279,D,inproceedings63dagdelen2024structured,Dagdelen,2024,Structured information extraction from scientific text with large language models,Nature Communications,10.1038/s41467-024-45563-x,D,article64mosqueirarey2023human,Mosqueira-Rey,2023,Human-in-the-loop machine learning: a state of the art,Artificial Intelligence Review,10.1007/s10462-022-10246-w,D,article65schennach2016recent,Schennach,2016,Recent Advances in the Measurement Error Literature,Annual Review of Economics,10.1146/annurev-economics-080315-015058,E,article66hausman2001mismeasured,Hausman,2001,Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left,Journal of Economic Perspectives,10.1257/jep.15.4.57,E,article67bound2001measurement,Bound,2001,Measurement Error in Survey Data,Handbook of Econometrics,10.1016/S1573-4412(01)05012-7,E,chapter68rubin1976inference,Rubin,1976,Inference and Missing Data,Biometrika,10.1093/biomet/63.3.581,E,article69heckman1979sample,Heckman,1979,Sample Selection Bias as a Specification Error,Econometrica,10.2307/1912352,E,article70kapoor2023leakage,Kapoor,2023,Leakage and the reproducibility crisis in machine-learning-based science,Patterns,10.1016/j.patter.2023.100804,E,article71howell2021increasing,Howell,2021,"The Increasing Effect of Neighborhood Racial Composition on Housing Values, 1980–2015",Social Problems,10.1093/socpro/spaa033,E,article72mehrabi2021survey,Mehrabi,2021,A Survey on Bias and Fairness in Machine Learning,ACM Computing Surveys,10.1145/3457607,E,article73rudin2019stop,Rudin,2019,Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,Nature Machine Intelligence,10.1038/s42256-019-0048-x,E,article74lipton2018mythos,Lipton,2018,The Mythos of Model Interpretability,Queue,10.1145/3236386.3241340,E,article75kim2004comparison,Kim,2004,"Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning",Building and Environment,10.1016/j.buildenv.2004.02.013,F,article76elfaki2014using,Elfaki,2014,Using Intelligent Techniques in Construction Project Cost Estimation: 10-Year Survey,Advances in Civil Engineering,10.1155/2014/107926,F,article77gunaydin2004neural,Günaydın,2004,A neural network approach for early cost estimation of structural systems of buildings,International Journal of Project Management,10.1016/j.ijproman.2004.04.002,F,article78hwang2009dynamic,Hwang,2009,Dynamic Regression Models for Prediction of Construction Costs,Journal of Construction Engineering and Management,10.1061/(ASCE)CO.1943-7862.0000006,F,article79desrosiers2002landscaping,Des Rosiers,2002,Landscaping and House Values: An Empirical Investigation,Journal of Real Estate Research,,G,article80theriault2003modelling,Thériault,2003,Modelling interactions of location with specific value of housing attributes,Property Management,10.1108/02637470310464472,G,article81desrosiers2001size,Des Rosiers,2001,Size and proximity effects of primary schools on surrounding house values,Journal of Property Research,10.1080/09599910110039905,G,article82dube2014spatial,Dubé,2014,A spatial Difference-in-Differences estimator to evaluate the effect of change in public mass transit systems on house prices,Transportation Research Part B: Methodological,10.1016/j.trb.2014.02.007,G,article83cohen1960coefficient,Cohen,1960,A Coefficient of Agreement for Nominal Scales,Educational and Psychological Measurement,10.1177/001316446002000104,H,article84cohen1968weighted,Cohen,1968,Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit,Psychological Bulletin,10.1037/h0026256,H,article85landis1977measurement,Landis,1977,The Measurement of Observer Agreement for Categorical Data,Biometrics,10.2307/2529310,H,article86hayes2007answering,Hayes,2007,Answering the Call for a Standard Reliability Measure for Coding Data,Communication Methods and Measures,10.1080/19312450709336664,H,article87artstein2008inter,Artstein,2008,Inter-Coder Agreement for Computational Linguistics,Computational Linguistics,10.1162/coli.07-034-R2,H,article88hallgren2012computing,Hallgren,2012,Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial,Tutorials in Quantitative Methods for Psychology,10.20982/tqmp.08.1.p023,H,article89mchugh2012interrater,McHugh,2012,Interrater reliability: the kappa statistic,Biochemia Medica,10.11613/BM.2012.031,H,article90koo2016guideline,Koo,2016,A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,Journal of Chiropractic Medicine,10.1016/j.jcm.2016.02.012,H,article91snow2008cheap,Snow,2008,Cheap and Fast -- But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks,Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing,10.3115/1613715.1613751,H,inproceedings92kermani2021racial,Kermani,2021,Racial Disparities in Housing Returns,NBER Working Paper 29306,10.3386/w29306,E,techreport93seiler2012toward,Seiler,2012,Toward an Understanding of Real Estate Homebuyer Internet Search Behavior: An Application of Ocular Tracking Technology,Journal of Real Estate Research,,C,article94guo2017calibration,Guo,2017,On Calibration of Modern Neural Networks,arXiv:1706.04599,,D,preprint95radford2021learning,Radford,2021,Learning Transferable Visual Models From Natural Language Supervision,arXiv:2103.00020,,D,preprint96lakshminarayanan2017simple,Lakshminarayanan,2017,Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles,arXiv:1612.01474,,D,preprint97gal2016dropout,Gal,2016,Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,arXiv:1506.02142,,D,preprint98kadavath2022language,Kadavath,2022,Language Models (Mostly) Know What They Know,arXiv:2207.05221,,D,preprint99liu2023visual,Liu,2023,Visual Instruction Tuning,Advances in Neural Information Processing Systems 36,,D,inproceedings100alayrac2022flamingo,Alayrac,2022,Flamingo: a Visual Language Model for Few-Shot Learning,Advances in Neural Information Processing Systems 35,,D,inproceedings101koh2021wilds,Koh,2021,WILDS: A Benchmark of in-the-Wild Distribution Shifts,Proceedings of the 38th International Conference on Machine Learning (PMLR 139),,E,inproceedings102zhao2024utilizing,Zhao,2024,Utilizing Large Language Models for Information Extraction from Real Estate Transactions,arXiv:2404.18043,,D,preprint103shehbaz2026structured,Shehbaz,2026,"Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models",arXiv:2607.06012,,D,preprint104nordstrom2024using,,2024,Using Images as Covariates: Measuring Curb Appeal with Deep Learning,arXiv:2403.19915,,C,preprint105goodman1998dwelling,Goodman,1998,Dwelling Age Heteroskedasticity in Repeat Sales House Price Equations,Real Estate Economics,10.1111/1540-6229.00742,A,article106