[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118845-en":3,"doc-seo-118845-105":30,"detail-sidebar-cat-0-en-105":84},{"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},118845,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","A Review of Machine Learning Approaches for Real Estate Valuation - Article","Real estate managers need accurate property valuation for current market conditions, yet traditional approaches rely heavily on manual data analysis and subjective experience, which can yield inconsistent results. Given the scale, complexity, and time pressure of valuation decisions, machine learning offers a stronger alternative. This study applies a systematic literature review to identify relevant studies from the last two decades, then builds a DRU (data, reasoning, usefulness) framework to assess performance, summarize the state of research, and derive theoretical and practical directions for future work.","Journal of the Midwest Association for Information Systems (JMWAIS)  \n\n| Volume 2023  Issue 2 | Article 2 |\n| --- | --- |\n| 2023\u003Cbr>A Review of Machine Learning Approaches for Real Estate Valuation\u003Cbr>THOMAS H. ROOT\u003Cbr>DRAKE UNIV, TOM.ROOT@DRAKE.EDU\u003Cbr>Troy J. Strader\u003Cbr>Drake University, [Troy.Strader@drake.edu](Troy.Strader@drake.edu)\u003Cbr>Yu-Hsiang (John) Huang\u003Cbr>Georgia College & State University, [john.huang@gcsu.edu](john.huang@gcsu.edu)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/jmwais](https://aisel.aisnet.org/jmwais) |  |\n\nRecommended Citation  \nROOT, THOMAS H.; Strader, Troy J.; and Huang, Yu-Hsiang (John) (2023) \"A Review of Machine Learning Approaches for Real Estate Valuation,\" Journal of the Midwest Association for Information Systems (JMWAIS): Vol. 2023: Iss. 2, Article 2.  \nDOI: 10.17705/3jmwa.000082  \nAvailable at: [https://aisel.aisnet.org/jmwais/vol2023/iss2/2](https://aisel.aisnet.org/jmwais/vol2023/iss2/2)  \nThis material is brought to you by the AIS Journals at AIS Electronic Library (AISeL) . It has been accepted for inclusion in Journal of the Midwest Association for Information Systems (JMWAIS) by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org).  \nJournal of the Midwest Association for Information Systems  \n\n| Volume2023| Issue2 | Article 2 |\n| --- | --- |\n\nDate: 07-31-2023  \nA Review of Machine Learning Approaches for Real Estate Valuation  \nThomas H. Root  \nDrake University, [tom.root@drake.edu](tom.root@drake.edu)  \nTroy J. Strader  \nDrake University, [troy.strader@drake.edu](troy.strader@drake.edu)  \nYu-Hsiang (John) Huang  \nGeorgia College & State University, [john.huang@gcsu.edu](john.huang@gcsu.edu)  \nAbstract  \nReal estate managers must identify the value for properties in their current market. Traditionally, this involved simple data analysis with adjustments made based on manager’s experience. Given the amount of money currently involved in these decisions, and the complexity and speed at which valuation decisions must be made, machine learning technologies provide a newer alternative for property valuation that could improve upon traditional methods. This study utilizes a systematic literature review methodology to identify published studies from the past two decades where specific machine learning technologies have been applied to the property valuation task. We develop a data, reasoning, usefulness (DRU) framework that provides a set of theoretical and practice-based criteria for a multi-faceted performance assessment for each system. This assessment provides the basis for identifying the current state of research in this domain as well as theoretical and practical implications and directions for future research.  \nKeywords: real estate valuation, property valuation, machine learning, systematic literature review  \nDOI: 10.17705/3jmwa.000082  \nCopyright © 2023 by Thomas H. Root, Troy J. Strader, and Yu-Hsiang (John) Huang  \n1. Introduction  \nReal estate is a major component of the global economy. The correct valuation of real estate plays a key role in the economy and is crucial for active market participants who are buying, selling and developing property, as well as property and non-property owners not actively engaged in the market. Governments use valuation techniques to determine property taxes which generate revenue for roads, schools and other government projects. Rental rates for non-property owners depend upon property valuation. Capital markets use valuations to determine collateral for both commercial and residential lending. Finally, activity in the real estate market creates employment for construction workers, income for retailers, and opportunities for service providers. The correct valuation of property is a crucial component of the complex web of economic activity linked to the real estate markets. The traditional valuation approach tasks market par","cbCaif7xZuC1dYNX","https://ap.wps.com/l/cbCaif7xZuC1dYNX","pdf",511581,1,21,"English","en",105,"# Introduction\n## Background and importance of real estate valuation\n## Traditional valuation methods and limitations\n## Role of mass appraisal\n## Motivation for machine learning\n# Systematic literature review approach\n## Selection of studies from the past two decades\n## DRU (data, reasoning, usefulness) performance assessment framework\n# Theoretical and practical implications\n## Current state of research in the domain\n## Future research directions","[{\"question\":\"What methodology and evaluation framework does the study use?\",\"answer\":\"The study conducts a systematic literature review covering machine learning technologies applied to property valuation over the past two decades. It introduces a DRU (data, reasoning, usefulness) framework to provide criteria for multi-faceted performance assessment.\"}]","A Review of Machine Learning Approaches for Real Estate Valuation - Article | PDF",1785720595,53,{"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":79,"head_meta":81,"extra_data":83,"updated_unix":28},"a-review-of-machine-learning-approaches-for-real-estate-valuation-article","",{"@graph":36,"@context":78},[37,54,69],{"@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/a-review-of-machine-learning-approaches-for-real-estate-valuation-article/118845/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"What methodology and evaluation framework does the study use?","Question",{"text":76,"@type":77},"The study conducts a systematic literature review covering machine learning technologies applied to property valuation over the past two decades. 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