[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126261-en":3,"doc-seo-126261-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126261,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Automated Valuation Model for Residential Properties using Machine Learning Approaches - A Case Study of Greater Sydney, Australia","The thesis investigates automated valuation models (AVMs) for residential properties using machine learning approaches, focusing on Greater Sydney, Australia. It develops and compares valuation methods grounded in hedonic price theory, locational externality theory, and established econometric and machine learning model families. The study integrates sales price with macroeconomic, structural, locational, neighbourhood, and point-of-interest or census and crime-related data. Algorithms such as ordinary least squares, geographically weighted regression, and random forest are used to estimate values and assess their modelling performance.","Automated Valuation Model for Residential Properties using Machine Learning Approaches: A Case Study of Greater Sydney, Australia  \nAuthor:  \nLee, Hojun  \nPublication Date:  \n2025  \nDOI:  \n[https://doi.org/10.26190/unsworks/31081](https://doi.org/10.26190/unsworks/31081)  \nLicense:  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nLink to license to see what you are allowed to do with this resource.  \nDownloaded from [http://hdl.handle. net/1959.4/104792](http://hdl.handle. net/1959.4/104792) in [https://](https://)[ ](https://)[unsworks. unsw.edu.au](unsworks. unsw.edu.au) on 2025-07-18  \nAutomated Valuation Model for Residential Properties using Machine Learning Approaches: A Case Study of Greater Sydney,  \nAustralia  \nHojun Lee  \nA thesis in fulfilment of the requirements for the degree of Doctor of Philosophy  \nSchool of Urban Planning  \nFaculty of Art, Design and Architecture  \nDECLARATIONS  \nPUBLICATIONS STATEMENT  \nTable of Contents  \nTable of Contents .......................................................................................................... i  \n[List of Figures .............................................................................................................. vi](List of Figures .............................................................................................................. vi)  \n[List of Tables ...............................](List of Tables ...............................)................................................................................ ix  \nList of Thesis Publications by Candidate...................................................................... xi  \nPeer-Reviewed Published Journal Papers ............................................................ xi  \nUnder Review Journal Papers .............................................................................. xi  \nConference Papers / Presentations ...................................................................... xi  \nAcknowledgement .......................................................................................................xii  \nAbstract ......................................................................................................................xiv  \nAbbreviations .............................................................................................................. xv  \nChapter 1- Introduction .............................................................................................. 18  \n1.1. Background .................................................................................................. 18  \n1.1.1. Valuation Method ................................................................................ 19  \n1.1.2. Automated Valuation Model ................................................................ 22  \n1.1.3. Valuation Professions in Australia ....................................................... 23  \n1.2. Problem Statement ....................................................................................... 25  \n1.3. Aim and Objectives ....................................................................................... 27  \nThesis Structure .................................................................................................. 28  \nReference ............................................................................................................ 31  \nChapter 2-Literature Review ..................................................................................... 36  \n2.1. Introduction ................................................................................................... 36  \n2.2. Theoretical Review ....................................................................................... 36  \n2.2.1. Hedonic Price Theory ......................................................................... 36  \n2.2.2. Locational Externality Theory .................................................","cbCaijAdLyQLjik5","https://ap.wps.com/l/cbCaijAdLyQLjik5","pdf",10167416,4,1,251,"English","en",105,"# Abstract\n# Chapter 1 - Introduction\n## Background\n## Problem Statement\n## Aim and Objectives\n# Chapter 2 - Literature Review\n## Theoretical Review\n## Methodological Review\n## Summary\n# Chapter 3 - Methods – Data and Models\n## Sales Price and Macroeconomics Characteristic\n## Structural Characteristics\n## Location and Neighbourhood Characteristics\n## AVM Algorithms","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses how to estimate residential property values using automated valuation models built with machine learning approaches.\"},{\"question\":\"Which theories and model types guide the research?\",\"answer\":\"The research is guided by hedonic price theory and locational externality theory, and it reviews both hedonic price models and machine learning models.\"},{\"question\":\"What kinds of data and algorithms are used?\",\"answer\":\"The methods combine sales price with macroeconomic, structural, and location/neighbourhood characteristics, including point-of-interest, census, and crime-related data; algorithms include ordinary least squares, geographically weighted regression, and random forest.\"}]","Automated Valuation Model for Residential Properties using Machine Learning Approaches - A Case Study of Greater Sydney, Australia | PDF",1785904114,633,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"automated-valuation-model-for-residential-properties-using-machine-learning-approaches-a-case-study-of-greater-sydney-australia","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/automated-valuation-model-for-residential-properties-using-machine-learning-approaches-a-case-study-of-greater-sydney-australia/126261/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"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 problem does the thesis address?","Question",{"text":76,"@type":77},"It addresses how to estimate residential property values using automated valuation models built with machine learning approaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which theories and model types guide the research?",{"text":81,"@type":77},"The research is guided by hedonic price theory and locational externality theory, and it reviews both hedonic price models and machine learning models.",{"name":83,"@type":74,"acceptedAnswer":84},"What kinds of data and algorithms are used?",{"text":85,"@type":77},"The methods combine sales price with macroeconomic, structural, and location/neighbourhood characteristics, including point-of-interest, census, and crime-related data; 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