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Using InsideAirbnb data, traditional, spatial, and machine learning models are compared directly. The Cape Town market shows notable spatial correlation and heterogeneity, meaning OLS regression fails to capture spatial dependence. Spatial models improve predictive performance, but the gains do not surpass non-spatial, non-linear machine learning approaches. Core drivers align with conventional property and accommodation factors such as property type, location, and amenities.","Cape Town Airbnb Price Prediction: An exploration of spatial statistic and machine  \nlearning methods  \nCourtney Williams  \nA Minor Dissertation Submitted for the Degree of Master of Science in Data Science  \nUniversity of Cape Town  \nDepartment of Statistical Sciences  \nFebruary 2023  \nSupervisors: Mr Sulaiman Salau  \nDr Sebnem Er  \nThe copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgement of the source. The thesis is to be used for private study or noncommercial research purposes only.  \nPublished by the University of Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author.  \nAbstract  \nThis thesis predicts the prices of Airbnb listings in Cape Town, South Africa and in doing so, investigates the price determinants in the market. Using data from InsideAirbnb, traditional, spatial and machine learning models are compared and contrasted. The Cape Town Airbnb market has significant spatial correlation and heterogeneity, and traditional models such as OLS regression do not account for this spatial dependence, however, it is addressed by spatial models. By accounting for spatial effects, model predictive performance does improve, but not so much as to outperform non-spatial, non-linear machine learning model predictions. While Airbnb is a new and unique platform, the most important price determinants are consistent with those of traditional housing and accommodation markets such as property type, location and amenities.  \nDeclaration  \nThis thesis is an original work of my research and contains no material which has been accepted for the award of any other degree or diploma at any university or equivalent institution and that, to the best of my knowledge and belief, this thesis contains no material previously published or written by another person, except where due reference is made in the text of the thesis.  \nSignature:  \n\n| Print Name: Courtney Williams |\n| --- |\n| Date: |\n\nAcknowledgements  \nI would like to thank my supervisors, Mr Sulaiman Salau and Dr Sebnem Er, for their consistent guidance and encouragement which has made this thesis possible. Additionally I would like to thank the larger UCT statistics department for its contribution to my undergraduate and postgraduate education which I value endlessly. Lastly, I would like to thank my family and friends without whose support I would not be here today.  \nContents  \nAbstract i  \nDeclaration ii  \nAcknowledgements iii  \nList of Figures vi  \nList of Tables viii  \nAbbreviations ix  \n1 Introduction 1  \n1.1 Airbnb .......................................... 1  \n1.2 Problem statement ................................... 2  \n2 Literature Review 3  \n2.1 Price prediction in real estate and travel accommodation markets ......... 3  \n2.1.1 Spatial models ................................. 5  \n2.1.2 Machine learning models ............................ 7  \n2.2 Real estate price prediction and the Airbnb and tourism market in Cape Town . 9  \n2.3 Summary ........................................ 10  \n3 Methods 12  \n3.1 Ordinary least squares regression (OLS) ....................... 12  \n3.2 Geospatial data and spatial models .......................... 13  \n3.2.1 Spatial error and spatial lag models ..................... 14  \n3.2.2 Geographically weighted regression ...................... 15  \n3.3 Machine learning tree based models ......................... 19  \n4 Exploratory Data Analysis 22  \n4.1 Data description .................................... 22  \n4.2 Variable exploration and transformations ...................... 23  \n4.2.1 Dependent variable ............................... 23  \n4.2.2 Independent variables ............................. 25  \n5 Results 39  \n5.1 Metrics ......................................... 39  \n5.2 Training and validation ................................ 41  \n5.3 Test set performance .................................. 65  \n6 Conclusion 75  \nA App","cbCaiplYnDE5UF98","https://ap.wps.com/l/cbCaiplYnDE5UF98","pdf",3623685,1,96,"English","en",105,"# 1 Introduction\n## 1.1 Airbnb\n## 1.2 Problem statement\n# 2 Literature Review\n## 2.1 Price prediction in real estate and travel accommodation markets\n## 2.2 Real estate price prediction and the Airbnb and tourism market in Cape Town\n## 2.3 Summary\n# 3 Methods\n## 3.1 Ordinary least squares regression (OLS)\n## 3.2 Geospatial data and spatial models\n## 3.3 Machine learning tree based models\n# 4 Exploratory Data Analysis\n## 4.1 Data description\n## 4.2 Variable exploration and transformations\n# 5 Results\n## 5.1 Metrics\n## 5.2 Training and validation\n## 5.3 Test set performance\n# 6 Conclusion\n# A Appendix A","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"The thesis aims to predict Airbnb listing prices in Cape Town and investigate the determinants influencing prices in the market.\"},{\"question\":\"How do spatial models compare with traditional OLS regression?\",\"answer\":\"The thesis finds that the Cape Town Airbnb market has spatial correlation and heterogeneity, so OLS regression does not account for spatial dependence, while spatial models address it and improve performance.\"},{\"question\":\"Do spatial effects lead to the best predictive results?\",\"answer\":\"Although incorporating spatial effects improves predictive performance, the improvements do not outperform non-spatial, non-linear machine learning models.\"}]","Cape Town Airbnb Price Prediction - An exploration of spatial statistic and machine learning methods | PDF",1785735945,242,{"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},"cape-town-airbnb-price-prediction-an-exploration-of-spatial-statistic-and-machine-learning-methods","",{"@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/cape-town-airbnb-price-prediction-an-exploration-of-spatial-statistic-and-machine-learning-methods/121497/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"The thesis aims to predict Airbnb listing prices in Cape Town and investigate the determinants influencing prices in the market.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do spatial models compare with traditional OLS regression?",{"text":80,"@type":76},"The thesis finds that the Cape Town Airbnb market has spatial correlation and heterogeneity, so OLS regression does not account for spatial dependence, while spatial models address it and improve performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Do spatial effects lead to the best predictive results?",{"text":84,"@type":76},"Although incorporating spatial effects improves predictive performance, the improvements do not outperform non-spatial, non-linear machine learning models.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]