[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124512-en":3,"doc-seo-124512-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124512,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Toward transparent and accurate housing price appraisal - Hedonic price models versus machine learning algorithms","Hedonic housing-price datasets exhibit strong nonlinearity, motivating flexible automated valuation models in mass appraisal, including artificial intelligence methods. Yet many AI approaches are criticized as “black-box” due to limited interpretability, especially when common importance metrics only show magnitude rather than clear explanatory meaning. This study contrasts traditional hedonic pricing models with machine learning algorithms such as random forest and deep neural networks, and applies SHAP for clearer, evidence-backed interpretation. Results indicate SHAP-enhanced random forest accuracy and interpretable contributions from housing characteristics and local amenities.","Toward transparent and accurate housing price appraisal: Hedonic price models versus machine learning algorithms  \nAn, Sihyun; Song, Yena; Jang, Hanwool; Ahn, Kwangwon  \nPublished in:  \nFinancial Innovation  \nDOI:  \n10.1186/s40854-025-00874-w  \nPublication date:  \n2025  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nAn, S, Song, Y, Jang, H & Ahn, K 2025, 'Toward transparent and accurate housing price appraisal: Hedonic price models versus machine learning algorithms', Financial Innovation, vol. 11, 141.  \n[https://doi.org/10.1186/s40854-025-00874-w](https://doi.org/10.1186/s40854-025-00874-w)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 03. Aug. 2026  \nAn etal. Financial Innovation (2025) 11:141 Financial Innovation  \n[https://doi.org/10.1186/s40854-025-00874-w](https://doi.org/10.1186/s40854-025-00874-w)  \nRESEARCH Open Access  \nToward transparent and accurate housing price appraisal: Hedonic price models versus machine learning algorithms  \nSihyun An1, Yena Song2, Hanwool Jang3 and Kwangwon Ahn4*  \n*Correspondence: [k.ahn@yonsei.ac.kr](k.ahn@yonsei.ac.kr)  \n1 Division of Finance and AI, Hankuk University of Foreign Studies, Yongin, South Korea  \n2 Department of Geography, Chonnam National University, Gwangju, South Korea  \n3 Department of Finance, Accounting and Risk, Glasgow Caledonian University, Glasgow, UK  \n4 Department of Industrial Engineering, Yonsei University, Seoul, South Korea  \nAbstract  \nThe nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately, and artificial intelligence models have been employed in mass appraisal to this end. However, they have been referred to as “blackbox” models owing to difficulties associated with interpretation. In this study, we compared the results of traditional hedonic pricing models with those of machine learning algorithms, e. g., random forest and deep neural network models. Commonly implemented measures, e. g., Gini importance and permutation importance, provide only the magnitude of each explanatory variable’s importance, which results in ambiguous interpretability. To address this issue, we employed the SHapley Additive exPlanation (SHAP) method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models. The results demonstrated that (1) the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2) the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence, and (3) housing characteristics and local amenities are primary contributors in property valuation, which is consistent with the findings of previous studies. Thus, our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.  \nKeywords: Hedonic price model, Importance measure, Machine learning, Housing price appraisal  \nIntroduction  \nReal estate is an essential sector of social and economic systems, and real estate prices fluctuate across time and place, which can affect economic stability (Glaeser et al. 2014; Tchuente and Nyawa 2022). Capturing d","cbCaimmS1GeFZl2R","https://ap.wps.com/l/cbCaimmS1GeFZl2R","pdf",2270891,1,30,"English","en",105,"# Abstract\n# Introduction\n## Motivation: housing price nonlinearity and valuation needs\n## Hedonic price models and interpretability challenges","[{\"question\":\"Why are flexible models needed for housing price appraisal in this study?\",\"answer\":\"Housing prices change across time and place and hedonic datasets show nonlinearity, so flexible valuation models and sophisticated algorithms are required to capture these patterns accurately.\"},{\"question\":\"What limitation affects commonly used importance measures in hedonic pricing models?\",\"answer\":\"Measures such as Gini importance and permutation importance mainly provide the magnitude of each variable’s importance, leading to ambiguous interpretability of model outcomes.\"},{\"question\":\"How does the SHAP method improve interpretability when comparing valuation approaches?\",\"answer\":\"The study uses SHAP and compares it with traditionally explainable measures, showing that SHAP-supported results can be consolidated with statistical evidence to provide more reliable interpretability for housing valuation.\"}]","Toward transparent and accurate housing price appraisal - Hedonic price models versus machine learning algorithms | PDF",1785822836,76,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"toward-transparent-and-accurate-housing-price-appraisal-hedonic-price-models-versus-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/toward-transparent-and-accurate-housing-price-appraisal-hedonic-price-models-versus-machine-learning-algorithms/124512/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are flexible models needed for housing price appraisal in this study?","Question",{"text":75,"@type":76},"Housing prices change across time and place and hedonic datasets show nonlinearity, so flexible valuation models and sophisticated algorithms are required to capture these patterns accurately.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation affects commonly used importance measures in hedonic pricing models?",{"text":80,"@type":76},"Measures such as Gini importance and permutation importance mainly provide the magnitude of each variable’s importance, leading to ambiguous interpretability of model outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the SHAP method improve interpretability when comparing valuation approaches?",{"text":84,"@type":76},"The study uses SHAP and compares it with traditionally explainable measures, showing that SHAP-supported results can be consolidated with statistical evidence to provide more reliable interpretability for housing valuation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]