[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118096-en":3,"doc-seo-118096-105":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},118096,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine learning and residential real estate - Three applications - Thesis","Machine learning techniques are applied to residential real estate, focusing on residential property valuation. The thesis integrates empirical studies across three topics: automated valuation models (AVM), residential property price indices (RPPI), and land development analysis framed as a real option. The AVM implementation aims to reduce human intervention despite missing values, then supports compiling an index that tracks market value trends and explains time effects. Results show improved AVM accuracy and effective missing-value handling; the ML-based index serves as a strong alternative to Australia’s ABS RPPI. For land development, construction-cost uncertainty raises land value while higher construction costs delay development, whereas higher residential building prices encourage it.","School of Accounting, Economics and Finance  \nMachine learning and residential real estate: Three applications  \nZhuoran (Thomas) Zhang  \n0000-0002-2110-6221  \nThis thesis is presented for the Collaborative Degree of Doctor of Philosophy  \nof  \nCurtin University  \nand  \nUniversity of Aberdeen  \nApril, 2024  \nDeclaration  \nTo the best of my knowledge and belief, this thesis contains no material previously published by any other person except where due acknowledgment has been made. This thesis contains no material which has been accepted for the award of any other degree or diploma in any university.  \nSignature:  \nDate: 3rd April, 2024  \nAbstract  \nThe applications of machine learning techniques have become popular recently in residential real estate, specifically, the valuation of residential properties. This thesis is a composite of empirical studies for three topics, automated valuation model (AVM), residential property price index (RPPI), and the analysis of land development. Firstly, machine learning techniques are applied to develop the implementations of AVMs, whose purpose is to provide a price estimate of a particular property at a specified time. The main objective is to minimize human intervention in price estimation when the presence of missing values remains a major challenge in the process. Then, the proposed AVM implementation is applied for compiling the residential property price index, which tracks the trend of market values, cooperating with the classic indexing approaches. The main objective is to investigate whether more accurate price predictions lead to a better price index and examine how well the machine learning techniques explain the time effects. Thirdly, land development is a“real option” that provides the landowner a right to decide whether and when to develop the vacant land by spending amount of money. The analysis of land development is to examine the real option, including the valuation of the option and the optimal timing to exercise the option. The analysis is conducted using machine learning techniques with the factors on both the investment output (residential buildings) side and the investment cost (construction cost)  \nside, such as the growths and uncertainties of property prices and construction costs.  \nThe empirical results show that the proposed AVM implementation in topic one can predict more accurately, meanwhile, the missing values in the data are well handled simultaneously. This is consistent with the results of recent studies. In addition, the residential property price index compiled using the machine learning AVM implementation is a competent alternative to the RPPI published by the Australian Bureau of Statistics. For land development, the uncertainty of construction costs consistently presents a positive effect on the land value, a risk premium is added on the land price. Meanwhile, the higher price of residential buildings encourages the landowners to develop. On the contrary, the higher construction cost delays the development project.  \nKeywords: Machine learning techniques, residential real estate, automated valuation model (AVM), Residential property price index, Land development  \nAcknowledgements  \nThe completion of this Ph.D. thesis is only possible with the encouragement and support from my supervisors, my family, and my friends. I would first like to express my sincere gratitude to my supervisory team, Professor Felix Chan, Associate Professor Greg Costello, and Dr. Rainer Schulz, for their continuous guidance, motivation, dedication, and patience during the completion of [my Ph.D. study](my Ph.D. study), especially during the COVID-19 pandemic. Not only their contributions to my Ph.D. study are significant, but also their guidance on my academic ethics and life attitude are valuable.  \nMy deep appreciation also goes to my parents, Yimin Zhang and Yuyan Yang for always encouraging and believing in me during my Ph.D. journey. Their support provides me with strength","cbCaikEfPTjOtAte","https://ap.wps.com/l/cbCaikEfPTjOtAte","pdf",2454950,1,273,"English","en",105,"# 1 Introduction\n## 1.1 Research background\n## 1.2 Research motivation\n## 1.3 Research objectives\n## 1.4 Research contributions\n## 1.5 Structure of the thesis\n# 2 Literature review\n## 2.1 Introduction\n## 2.2 Valuation for residential property\n## 2.3 Residential property price indices\n## 2.4 Land valuation and development\n## 2.5 Research gaps in literature\n## 2.6 Concluding remarks\n# 3 Data\n## 3.1 Introduction\n## 3.2 Transaction-level data\n## 3.3 Data description\n## 3.4 Data preparation\n## 3.5 Concluding remarks\n# 4 Methodology\n## 4.1 Introduction","[{\"question\":\"What are the three research topics in this thesis?\",\"answer\":\"The thesis covers automated valuation models (AVM), a residential property price index (RPPI) built using the AVM, and land development analysis using a real option framework.\"},{\"question\":\"How does the thesis address the missing values problem in residential data?\",\"answer\":\"The proposed AVM implementation is designed to minimize human intervention in price estimation and to handle missing values effectively during the modeling process.\"},{\"question\":\"How do construction costs and uncertainty affect land development decisions?\",\"answer\":\"Construction-cost uncertainty has a positive effect on land value (a risk premium), while higher construction costs delay the development project.\"}]","Machine learning and residential real estate - 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