[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117982-en":3,"doc-seo-117982-105":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},117982,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Approaches for Modelling Real Estate Systems - Conference Paper","Precise real estate valuation is essential for transactions and investment decisions, yet the 2008 global downturn exposed weaknesses in conventional appraisal practices that often remain subjective or insufficiently objective. This paper examines how machine learning can model complex real estate systems, tracing the shift toward Automated Valuation Models (AVM). It reviews machine-learning-driven improvements and the challenges AVMs face, supported by international research and case studies. Findings emphasize adaptability and accuracy, highlighting machine learning’s role in advancing future property appraisal.","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>Machine Learning Approaches for Modelling Real Estate Systems\u003Cbr>Visar Hoxha\u003Cbr>University for Business and Technology-UBT, [visar.hoxha@ubt-uni.net](visar.hoxha@ubt-uni.net)\u003Cbr>Lorana Berisha\u003Cbr>College ESLG, [lorana.berisha@eukos.org](lorana.berisha@eukos.org)\u003Cbr>Jehona Hoxha\u003Cbr>College ESLG, [jehona.hoxha@eukos.org](jehona.hoxha@eukos.org)\u003Cbr>Follow this and additional works at: [https://knowledgecenter.ubt-uni.net/conference](https://knowledgecenter.ubt-uni.net/conference)\u003Cbr> Part of the Business Commons |  |\n\nRecommended Citation  \nHoxha, Visar; Berisha, Lorana; and Hoxha, Jehona, \"Machine Learning Approaches for Modelling Real Estate Systems\" (2023) . UBT International Conference. 34.  \n[https://knowledgecenter.ubt-uni.net/conference/IC/MBE/34](https://knowledgecenter.ubt-uni.net/conference/IC/MBE/34)  \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).  \nMachine Learning Approaches for Modelling Real Estate  \nSystems  \nVisar Hoxha 1, Lorana Berisha2 and Jehona Hoxha3  \n1Faculty of Real Estate University for Business and Technology  \n[visar.hoxha@ubt-uni.net](visar.hoxha@ubt-uni.net)  \n2Department of Real Estate, College ESLG  \n[lorana.berisha@eukos.org](lorana.berisha@eukos.org)  \n3Department of Real Estate, College ESLG  \n[jehona.hoxha@eukos.org](jehona.hoxha@eukos.org)  \nAbstract. In the intricate domain of real estate, precise property valuation remains paramount for a spectrum of economic endeavors. The 2008 global economic downturn spotlighted the limitations of conventional valuation methods, prompting the exploration of innovative, objective techniques. This paper investigates the incorporation of machine learning in modeling intricate real estate systems. We discuss the evolution from traditional appraisal methods to Automated Valuation Models (AVM) and the subsequent challenges these models face. Drawing from global research and case studies, we highlight the adaptability, accuracy, and potential of machine learning techniques in real estate valuation. Our findings underscore machine learning's transformative role in enhancing property appraisal, offering a forward-looking perspective on the future of real estate valuation.  \n1. INTRODUCTION  \nIn the ever-evolving landscape of real estate, the valuation of properties remains a cornerstone for various economic activities, from individual purchases to large-scale investments. With the global economic downturn of 2008 serving as a backdrop, the traditional methods of property valuation have been put under scrutiny, leading to the exploration of more advanced and unbiased techniques. This paper delves into the integration of machine learning in modeling complex real estate systems, examining its efficacy, challenges, and the potential it holds for the future.  \n2. LITERATURE REVIEW  \nSince 2008, the global economic crisis has led to an economic slowdown resulting in a decline in real estate prices. The appraised value of a property is a key factor for any transaction related to the property, especially when it comes to its sale or mortgage application. Therefore, it's crucial that the price reflects its true value.  \nWhile the physical presence of an appraiser can significantly aid in providing an accurate property valuation, there's still a possibility of bias from interested parties such as buyers, sellers, or even banks, which might lead to subjective evaluations. Hence, there's a clear need for the development of an unbiased and realistic predictive model (Navaro et al., 2020) .  \nThe Int","cbCaievjflF8HW8Z","https://ap.wps.com/l/cbCaievjflF8HW8Z","pdf",162665,1,6,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"Why are traditional real estate valuation methods being reconsidered?\",\"answer\":\"The 2008 global economic downturn highlighted limitations of conventional valuation approaches, motivating the search for more objective and innovative techniques.\"},{\"question\":\"What is the role of Automated Valuation Models (AVM) in this work?\",\"answer\":\"The paper discusses how AVMs evolved as a computer-assisted approach to support mass property valuations, reducing dependence on on-site appraisers.\"},{\"question\":\"Which machine learning techniques are mentioned for real estate appraisal?\",\"answer\":\"The document cites approaches such as neural networks, decision trees, random forests, and fuzzy logic, alongside regression-based methods and data mining techniques.\"}]","Machine Learning Approaches for Modelling Real Estate Systems - 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