[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120726-en":3,"doc-seo-120726-105":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},120726,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Boosting the Accuracy of Commercial Real Estate Appraisals - An Interpretable Machine Learning Approach","The study evaluates the accuracy and bias of market valuations in U.S. commercial real estate by using properties from the NCREIF Property Index (NPI) between 1997 and 2021. It tests machine learning boosting-tree models to reduce deviations between appraised market values and subsequent transaction prices across 50 covariates. Results show structured variation in deviations that boosting trees capture and explain, improving appraisal accuracy and removing structural bias, with the strongest interpretability for apartments and industrial properties.","Boosting the Accuracy of Commercial Real Estate  \nAppraisals: An Interpretable Machine Learning Approach  \nJuergen Deppner1 · Benedict von Ahlefeldt‑Dehn1 · Eli Beracha2 · Wolfgang Schaefers1  \nAccepted: 16 February 2023 © The Author(s) 2023  \nAbstract  \nIn this article, we examine the accuracy and bias of market valuations in the U.S. commercial real estate sector using properties included in the NCREIF Property Index (NPI) between 1997 and 2021 and assess the potential of machine learning algorithms (i.e., boosting trees) to shrink the deviations between market values and subsequent transaction prices. Under consideration of 50 covariates, we find that these deviations exhibit structured variation that boosting trees can capture and further explain, thereby increasing appraisal accuracy and eliminating structural bias. The understanding of the models is greatest for apartments and industrial properties, followed by office and retail buildings. This study is the first in the literature to extend the application of machine learning in the context of property pricing and valuation from residential use types and commercial multifamily to office, retail, and industrial assets. In addition, this article contributes to the existing literature by providing an indication of the room for improvement in state-of-the-art valuation practices in the U.S. commercial real estate sector that can be exploited by using the guidance of supervised machine learning methods. The contributions of this study are, thus, timely and important to many parties in the real estate sector, including authorities, banks, insurers and pension and sovereign wealth funds.  \nKeywords Commercial real estate · Appraisal · Interpretable machine learning  \n* Juergen Deppner juergen.deppner@irebs.de  \nBenedict von Ahlefeldt-Dehn  \nbenedict.vonahlefeldtdehn@irebs.de  \nEli Beracha  \n[eberacha@fiu.edu](eberacha@fiu.edu)  \nWolfgang Schaefers  \n[wolfgang.schaefers@irebs.de](wolfgang.schaefers@irebs.de)  \n1 University of Regensburg, IRE|BS International Real Estate Business School, Regensburg, Germany  \n2 Florida International University, Hollo School of Real Estate, FL, Miami, USA  \n1 3  \nIntroduction  \nBoth institutional and private investors aim to diversify their portfolios with real estate. A significant share of this is accounted for by investments in commercial real estate sectors, which amount to around $32 trillion globally. The heterogeneity of commercial real estate contributes well to diversification, but it is also accompanied by characteristics such as illiquidity, opacity and unwieldiness that make it difficult to thoroughly understand market dynamics. Consequently, thevaluation of commercial properties involves a great deal of effort that justifies an appraisal industry worth billions of dollars. Studies have repeatedly demonstrated that commercial property appraisals do not always adequately represent market dynamics and can differ significantly from actual sales prices (e.g. , Cole et al. , 1986 ; Webb, 1994 ; Fisher et al. , 1999 ; Matysiak & Wang, 1995 ; Edelstein & Quan, 2006 ; Cannon & Cole, 2011) . Despite the increasing complexity of pricing processes and more rapidly changing markets, the principal methods used by the valuation industry have largely remained unchanged for the past decades. However, this is slowly changing with an increasing availability of data and the emergence of artificial intelligence fostering the use of innovative technologies in the real estate sector.  \nIn recent years, machine learning algorithms have been increasingly considered as a suitable method for the estimation of house prices and rents, with a large corpus of literature pointing to their high accuracy in the residential sector (e.g. , Mullainathan & Spiess, 2017 ; Mayer et al. , 2019 ; Bogin & Shui, 2020 ; Hong et al. , 2020 ; Pace & Hayunga, 2020 ; Lorenz et al. , 2022 ; and Deppner & Cajias, 2022) . In the commercial sector, on the other hand, the scope of analys","cbCaieRiHibuNgMS","https://ap.wps.com/l/cbCaieRiHibuNgMS","pdf",1763136,1,38,"English","en",105,"# Abstract\n# Introduction\n## Market valuation challenges in commercial real estate\n## Prior machine learning work and gaps\n## Study contribution using NCREIF data","[{\"question\":\"What data and time range are used to evaluate commercial real estate appraisal accuracy?\",\"answer\":\"The analysis uses properties included in the NCREIF Property Index (NPI) covering 1997 to 2021.\"},{\"question\":\"How does the study test whether machine learning improves appraisal outcomes?\",\"answer\":\"It applies boosting-tree machine learning models using 50 covariates to reduce deviations between appraised values and subsequent transaction prices.\"},{\"question\":\"Which property types show the greatest model interpretability in the study?\",\"answer\":\"Interpretability is greatest for apartments and industrial properties, followed by office and retail buildings.\"}]","Boosting the Accuracy of Commercial Real Estate Appraisals - An Interpretable Machine Learning Approach | PDF",1785731726,96,{"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},"boosting-the-accuracy-of-commercial-real-estate-appraisals-an-interpretable-machine-learning-approach","",{"@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/boosting-the-accuracy-of-commercial-real-estate-appraisals-an-interpretable-machine-learning-approach/120726/",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","2026-08-03",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},"What data and time range are used to evaluate commercial real estate appraisal accuracy?","Question",{"text":75,"@type":76},"The analysis uses properties included in the NCREIF Property Index (NPI) covering 1997 to 2021.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study test whether machine learning improves appraisal outcomes?",{"text":80,"@type":76},"It applies boosting-tree machine learning models using 50 covariates to reduce deviations between appraised values and subsequent transaction prices.",{"name":82,"@type":73,"acceptedAnswer":83},"Which property types show the greatest model interpretability in the study?",{"text":84,"@type":76},"Interpretability is greatest for apartments and industrial properties, followed by office and retail buildings.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]