[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117137-en":3,"doc-seo-117137-105":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":4,"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},117137,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Revolutionizing Real Estate Mortgage Scoring - The Superiority of Machine Learning Over Traditional Statistical Methods - Conference Paper","This paper investigates how machine learning improves real estate mortgage scoring compared with traditional statistical methods. It highlights that conventional approaches often depend on extensive data preprocessing, including detection and imputation of missing or outlying values, variable discretization, and manual intervention. In contrast, machine learning supports more streamlined workflows, enabling faster mortgage application processing and helping reduce modeling biases. The results emphasize machine learning’s capability to transform financial-sector decisioning and underwriting practices through efficient, data-driven analysis.","University for Business and Technology in Kosovo  \nUBT Knowledge Center  \n\n| UBT International Conference | 2023 UBT International Conference |\n| --- | --- |\n| Oct 28th, 8:00 AM-Oct 29th, 6:00 PM\u003Cbr>Revolutionizing Real Estate Mortgage Scoring: The Superiority of Machine Learning Over Traditional Statistical Methods\u003Cbr>Visar Hoxha\u003Cbr>University for Business and Technology-UBT, [visar.hoxha@ubt-uni.net](visar.hoxha@ubt-uni.net)\u003Cbr>Blerta Demjaha\u003Cbr>[blerta.demjaha@eukos.org](blerta.demjaha@eukos.org)\u003Cbr>Veli Lecaj\u003Cbr>University for Business and Technology-UBT, [veli.lecaj@ubt-uni.net](veli.lecaj@ubt-uni.net)\u003Cbr>Hazer Dana\u003Cbr>University for Business and Technology, [hazer.dana@ubt-uni.net](hazer.dana@ubt-uni.net)\u003Cbr>Fuat Pallaska\u003Cbr>University for Business and Technology-UBT, [fuat.pallaska@ubt-uni.net](fuat.pallaska@ubt-uni.net)\u003Cbr>Follow this and additional works at: [https://knowledgecenter.ubt-uni.net/conference](https://knowledgecenter.ubt-uni.net/conference)\u003Cbr> Part of the Law Commons |  |\n\nRecommended Citation  \nHoxha, Visar; Demjaha, Blerta; Lecaj, Veli; Dana, Hazer; and Pallaska, Fuat, \"Revolutionizing Real Estate Mortgage Scoring: The Superiority of Machine Learning Over Traditional Statistical Methods\" (2023) . UBT International Conference. 6.  \n[https://knowledgecenter.ubt-uni.net/conference/IC/LAW/6](https://knowledgecenter.ubt-uni.net/conference/IC/LAW/6)  \nThis Event is brought to you for free and open access by the Publication and Journals at UBT Knowledge Center. It has been accepted for inclusion in UBT International Conference by an authorized administrator of UBT Knowledge Center. For more information, please contact [knowledge.center@ubt-uni.net](knowledge.center@ubt-uni.net).  \nRevolutionizing Real Estate Mortgage Scoring: The Superiority of Machine Learning  \nOver Traditional Statistical Methods  \nVisar Hoxha1, Blerta Demjaha2 Veli Lecaj1 Hazer Dana1 Fuat Pallaska1  \n1 Faculty of Real Estate, UBT  \n[Visar.hoxha@ubt-uni.net](Visar.hoxha@ubt-uni.net)  \n2 Real Estate Department, College ESLG  \n[blerta.demjaha@eukos.org](blerta.demjaha@eukos.org)  \n1 Faculty of Real Estate, UBT  \n[Veli.lecaj@ubt-uni.net](Veli.lecaj@ubt-uni.net)  \n1 Faculty of Real Estate, UBT  \n[hazer.dana@ubt-uni.net](hazer.dana@ubt-uni.net)  \n1 Faculty of Real Estate, UBT  \n[fuat.pallaska@ubt-uni.net](fuat.pallaska@ubt-uni.net)  \nAbstract: This paper explores the advantages of machine learning over traditional statistical methods in the context of real estate mortgage scoring. While traditional methods require extensive data preprocessing, machine learning offers a more streamlined and efficient approach. The financial industry is recognizing these benefits, with machine learning enabling faster mortgage application processing and reduced modeling biases. The findings underscore the potential of machine learning to revolutionize the financial sector.  \nKeywords: Machine Learning, Real Estate Mortgage, Financial Industry, Data Preprocessing, Traditional Statistical Methods.  \n1. Introduction  \nThe evolution of machine learning (ML) has revolutionized various sectors, including the financial industry. Traditional statistical methods, while effective, often require extensive data preprocessing and manual intervention. Machine learning, with its ability to autonomously process and analyze data, offers a promising alternative. This paper delves into the advantages of machine learning over traditional statistical methods, particularly in the context of real estate mortgage scoring within large banks.  \n2. Literature review  \nBeyond the question of predictive performance, machine learning methods have an undeniable advantage over the usual parametric scoring approaches since they allow significant productivity gains. In particular, machine learning algorithms make it possible to reduce the time devoted to the data management and preprocessing stages before the modeling stage in a strict sense (Milunovich, 2019) 1. Of course, this does not mean tha","cbCaicKVM3d5mXx3","https://ap.wps.com/l/cbCaicKVM3d5mXx3","pdf",157549,1,5,"English","en",105,"# Abstract\n# Introduction\n# Literature review","[{\"question\":\"What problem does traditional statistical mortgage scoring require significant effort to solve?\",\"answer\":\"Traditional methods require extensive data preprocessing, such as handling missing or outlying values and discretizing variables, often with manual intervention.\"},{\"question\":\"How does machine learning streamline mortgage scoring compared with traditional approaches?\",\"answer\":\"Machine learning enables more autonomous data processing and analysis, reducing time spent on data management and preprocessing before modeling.\"},{\"question\":\"What benefits of machine learning are emphasized for the mortgage scoring process?\",\"answer\":\"The document highlights faster mortgage application processing and reduced modeling biases, supporting faster and more reliable financial decisioning.\"}]","Revolutionizing Real Estate Mortgage Scoring - The Superiority of Machine Learning Over Traditional Statistical Methods - Conference Paper | PDF",1785674058,13,{"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},"revolutionizing-real-estate-mortgage-scoring-the-superiority-of-machine-learning-over-traditional-statistical-methods-conference-paper","",{"@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/revolutionizing-real-estate-mortgage-scoring-the-superiority-of-machine-learning-over-traditional-statistical-methods-conference-paper/117137/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does traditional statistical mortgage scoring require significant effort to solve?","Question",{"text":75,"@type":76},"Traditional methods require extensive data preprocessing, such as handling missing or outlying values and discretizing variables, often with manual intervention.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning streamline mortgage scoring compared with traditional approaches?",{"text":80,"@type":76},"Machine learning enables more autonomous data processing and analysis, reducing time spent on data management and preprocessing before modeling.",{"name":82,"@type":73,"acceptedAnswer":83},"What benefits of machine learning are emphasized for the mortgage scoring process?",{"text":84,"@type":76},"The document highlights faster mortgage application processing and reduced modeling biases, supporting faster and more reliable financial decisioning.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"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":21,"slug":137},19,"General","general"]