[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127411-en":3,"doc-seo-127411-105":30,"detail-sidebar-cat-0-en-105":92},{"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},127411,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Machine Learning Approach to Price Indices - Applications in Commercial Real Estate","This article presents a model-agnostic methodology for producing property price indices, motivated by integrating non-linear and non-parametric machine learning models into the price-indexing toolkit. The key innovation uses individual out-of-time prediction errors to quantify price changes. Using 29,998 commercial real estate transactions in New York from 2000–2019, the study finds higher prediction accuracy for ML models than linear models. It also highlights increased sensitivity to calibration data, instability on small samples, and potential estimation bias, requiring bias-variance trade-off mitigation.","UvA-DARE (Digital Academic Repository)  \nA Machine Learning Approach to Price Indices: Applications in Commercial Real Estate  \nCalainho, F. D. ; van de Minne, A. M. ; Francke, M. K.  \nDOI  \n10.1007/s11146-022-09893-1  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nPublished in  \nJournal of Real Estate Finance and Economics  \nLicense  \nCC BY  \nLink to publication  \nCitation for published version (APA):  \nCalainho, F. D. , van de Minne, A. M. , & Francke, M. K. (2024) . A Machine Learning Approach to Price Indices: Applications in Commercial Real Estate. Journal of Real Estate Finance and  \nEconomics, 68(4), 624–653 . [https://doi.org/10.1007/s11146-022-09893-1](https://doi.org/10.1007/s11146-022-09893-1)  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:05 Jan 2026  \nA Machine Learning Approach to Price Indices: Applications in Commercial Real Estate  \nFelipe D. Calainho1 · Alex M. van de Minne2 · Marc K. Francke1,3  \nAccepted: 22 February 2022 / Published online: 9 April 2022 © The Author(s) 2022  \nAbstract  \nThis article presents a model agnostic methodology for producing property price indices. The motivation to develop this methodology is to include non-linear and non-parametric models, such as Machine Learning (ML), in the pool of algorithms to produce price indices. The key innovation is the use of individual out-of-time prediction errors to measure price changes. The data used in this study consist of 29,998 commercial real estate transactions in New York, in the period 2000–2019. The results indicate that the prediction accuracy is higher for the ML models compared to linear models. On the other hand, ML algorithms depend more on the data used for calibration; they produce less stable results when applied to small samples and may exhibit estimation bias. Hence, measures to reduce or eliminate bias need to be implemented, taking into consideration the bias and variance trade-off.  \nKeywords Commercial real estate · Price indices · Machine learning  \nJEL Classification R33 · C43 · C51  \n* Felipe D. Calainho[f.dutracalainho@uva.nl](f.dutracalainho@uva.nl)  \n[Alex M. van de Minne](Alex M. van de Minne)  \n[avdminne@uconn.edu](avdminne@uconn.edu)  \n[Marc K. Francke](Marc K. Francke)  \n[m.k.francke@uva.nl](m.k.francke@uva.nl)  \n1 Amsterdam Business School, Universiteit van Amsterdam, Plantage Muidergracht 12 1018 TV, Amsterdam, The Netherlands  \n2 Center for Real Estate and Urban Economic Studies, University of Connecticut, 2100 Hillside Road, Unit1041 Storrs, Connecticut, CT 06269, USA  \n3 Ortec Finance, Naritaweg 51, 1043, BP, Amsterdam, The Netherlands  \nIntroduction  \nHaving reliable real estate price indices is pivotal for several reasons. Firstly, index volatility is an important input in determining the cost of capital of real estate (Geltner et al., 2017) . This is helpful for investors, underwriters and policymakers to determine interest rates. Secondly, it allows one to (re-)appraise real estate portfolios ma","cbCaic7fWebkh8im","https://ap.wps.com/l/cbCaic7fWebkh8im","pdf",2526571,1,31,"English","en",105,"# Introduction\n## Hedonic pricing and index construction\n## Motivation for machine learning approaches","[{\"question\":\"What is the main contribution of the proposed method for price indices?\",\"answer\":\"The methodology is model-agnostic and uses individual out-of-time prediction errors to measure property price changes for index construction.\"},{\"question\":\"What data are used in the study?\",\"answer\":\"The study uses 29,998 commercial real estate transactions in New York over the period 2000–2019.\"},{\"question\":\"How do machine learning models compare with linear models in prediction accuracy?\",\"answer\":\"Results show that ML models achieve higher prediction accuracy than linear models for the task considered in the paper.\"}]","A Machine Learning Approach to Price Indices - Applications in Commercial Real Estate | PDF",1785938741,78,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-machine-learning-approach-to-price-indices-applications-in-commercial-real-estate","",{"@graph":36,"@context":86},[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-machine-learning-approach-to-price-indices-applications-in-commercial-real-estate/127411/",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","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main contribution of the proposed method for price indices?","Question",{"text":76,"@type":77},"The methodology is model-agnostic and uses individual out-of-time prediction errors to measure property price changes for index construction.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data are used in the study?",{"text":81,"@type":77},"The study uses 29,998 commercial real estate transactions in New York over the period 2000–2019.",{"name":83,"@type":74,"acceptedAnswer":84},"How do machine learning models compare with linear models in prediction accuracy?",{"text":85,"@type":77},"Results show that ML models achieve higher prediction accuracy than linear models for the task considered in the paper.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]