[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118737-en":3,"doc-seo-118737-105":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},118737,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Increasing Transparency on Housing Markets through Machine Learning","Increasing transparency on housing markets through machine learning investigates how advanced predictive methods can support clearer insights into real estate market dynamics. The dissertation is structured around deep learning and probabilistic frameworks, with an additional focus on automating residential real estate appraisal processes. Research is supported by defined data, a detailed methodology, and results sections that evaluate robustness and practical implications. The work culminates in conclusions and appendices addressing supplementary materials and references.","INCREASING TRANSPARENCY ON HOUSING MARKETS THROUGH  \nMACHINE LEARNING  \nA dissertation in partial fulfillment of the requirements for the degree of Doktor der Wirtschaftswissenschaften (Dr. rer. pol. )  \nsubmitted to the  \nFACULTY OF BUSINESS, ECONOMICS, MANAGEMENT INFORMATION SYSTEMS  \nUNIVERSITY OF REGENSBURG  \nsubmitted by  \nCathrine Nagl  \nMaster of Science in Real Estate (IRE|BS)  \nAdvisors  \nPROF. DR. WOLFGANG SCHÄFERS  \nPROF. DR. TOBIAS JUST  \nDate of Disputation  \n28. October 2022  \nAcknowledgements  \nFirst and foremost, I would like to express my appreciation to my doctoral supervisor Prof. Dr. Wolfgang Schäfers. Thank you for your great and constant support during the last years, the inspiring discussions, your confidence in my abilities and our successfully completed projects. It was quite a pleasure. In addition, I would also like to thank my second doctoral supervisor, Prof. Dr. Tobias Just, for his willingness for supervision and the valuable comments.  \nI would like to express my sincere thanks to my colleagues for the enjoyable time at the chair. A special thanks goes to my co-authors Moritz Stang and Bastian Krämer. As project and research partners, we spent many hours together with inspiring and often sprawling discussions and the elaboration of ideas. You have redefined the term team spirit on a new level.  \nI also want to appreciate the ongoing support of my friends, which really helped me in challenging times. My special thanks go to Marie Wieck and Stella Zimmermann, for the unforgettable time during the IRE| BS Master studies and for the constant emotional support in the last years.  \nThis project would not have been possible without my parents, Roland and Susanne. I feel beyond blessed to have you always by my side and want to express my gratitude for your outstanding care and support throughout the years. Your relentless belief in me carries me through life. I would also like to thank my sister Paula for your great support and dedicate a special mention to Flora, who has reminded me every day for the past 1.5 years that there is a life outside of work.  \nMy deepest thanks goto my husband Dr. Maximilian Nagl. Without you, I would not have been able to finish this dissertation. One step closer. I thank you for your tireless care and support, your insightful suggestions, and your unconditional love. You have shown me for what and for whom achieving goals in life is worthwhile. For a thousand years.  \nContent  \nList of Figures ........................................................................................................................ iii  \nList of Tables.......................................................................................................................... iv  \n1 Introduction ....................................................................................................................2  \n1.1 Motivation and Background .....................................................................................2  \n1.2 Course of Analysis and Research Questions ..............................................................6  \n1.3 Submissions and Conference Presentations..............................................................8  \n1.4 References .............................................................................................................10  \n2 Sentiment Analysis within a Deep Learning Probabilistic Framework – New Evidence from Residential Real Estate in the United States .................................................................11  \n2.1 Abstract..................................................................................................................11  \n2.2 Introduction ...........................................................................................................12  \n2.3 Literature Review ...................................................................................................13  \n2.4 Data ....................................","cbCaiiHp2q8QunMZ","https://ap.wps.com/l/cbCaiiHp2q8QunMZ","pdf",3437767,1,141,"English","en",105,"# List of Figures\n# List of Tables\n# 1 Introduction\n## 1.1 Motivation and Background\n## 1.2 Course of Analysis and Research Questions\n## 1.3 Submissions and Conference Presentations\n## 1.4 References\n# 2 Sentiment Analysis within a Deep Learning Probabilistic Framework\n## 2.1 Abstract\n## 2.2 Introduction\n## 2.3 Literature Review\n## 2.4 Data\n## 2.5 Methodology\n## 2.6 Results\n## 2.7 Robustness\n## 2.8 Conclusion\n## 2.9 Appendix\n## 2.10 References\n# 3 From Human Business to Machine Learning — Methods for Automating Real Estate Appraisals\n## 3.1 Abstract\n## 3.2 Introduction\n## 3.3 Literature Review\n## 3.4 Data\n## 3.5 Methodology\n## 3.6 Results\n## 3.7 Conclusion\n## 3.8 Appendix","[{\"question\":\"What is the central topic of the dissertation?\",\"answer\":\"The dissertation centers on increasing transparency in housing markets using machine learning techniques, with a focus on modeling market-related signals and improving decision support.\"},{\"question\":\"Which research approaches and learning frameworks are used?\",\"answer\":\"It includes sentiment analysis within a deep learning probabilistic framework and also develops methods to automate real estate appraisals, with separate methodology and results sections.\"},{\"question\":\"How does the work assess reliability of its findings?\",\"answer\":\"The dissertation includes a dedicated robustness section for the sentiment analysis study, along with structured data, methodology, and conclusion sections to evaluate outcomes.\"}]","Increasing Transparency on Housing Markets through Machine Learning | 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