[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118078-en":3,"doc-seo-118078-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118078,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Airbnb Valuation - A Machine Learning Approach","This thesis uses a geospatially enhanced machine learning framework to analyze variation in Airbnb rental outcomes on the peer-to-peer property sharing platform. Decision tree modeling incorporates geographic factors, listing attributes and amenities, customer response metrics, and host attributes to predict the short-term probability of receiving a review. The study evaluates which variables most improve predictive accuracy and examines how their relative importance shifts using Shapley values.","University of Arkansas, Fayetteville  \nScholarWorks@UARK  \nGraduate Theses and Dissertations  \n12-2023  \nAirbnb Valuation: A Machine Learning Approach  \nKatherine Wyatt  \nUniversity of Arkansas-Fayetteville  \nFollow this and additional works at: [https://scholarworks.uark.edu/etd](https://scholarworks.uark.edu/etd)  \n Part of the Business Intelligence Commons, Geographic Information Sciences Commons, Management Sciences and Quantitative Methods Commons, and the Spatial Science Commons  \nCitation  \nWyatt, K. (2023) . Airbnb Valuation: A Machine Learning Approach. Graduate Theses and Dissertations  \nRetrieved from [https://scholarworks.uark.edu/etd/5144](https://scholarworks.uark.edu/etd/5144)  \nThis Thesis is brought to you for free and open access by ScholarWorks@UARK. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of ScholarWorks@UARK. For more information, please contact [scholar@uark.edu](scholar@uark.edu).  \nAirbnb Valuation: A Machine Learning Approach  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Geography  \nby  \nKatherine Wyatt  \nUniversity of Arkansas Bachelor of Arts in Geography, 2016  \nDecember 2023  \nUniversity of Arkansas  \nThis thesis is approved for recommendation to the Graduate Council.  \nFred Limp, Ph.D Thesis Director  \nEdward Holland, Ph.D Committee Member  \nJackson Cothren, Ph.D Committee Member  \nABSTRACT  \nThis thesis uses a geospatially-enhanced, machine learning approach to investigate variations in rental [success on the peer-to-peer property sharing website Airbnb.com. Geographic](success on the peer-to-peer property sharing website Airbnb.com. Geographic)[ ](success on the peer-to-peer property sharing website Airbnb.com. Geographic)factors, listing attributes and amenities, customer response metrics, and host attributes are included in decision tree modeling to predict the short-term probability of receiving a review. The most important variables in increasing model accuracy are assessed and variations in the importance of these variables investigated using Shapley values.  \nCONTENTS  \nResearch Statement ...................................................................................................................... 1  \nIntroduction................................................................................................................................... 2  \nSharing Economy ........................................................................................................................ 2  \nAirbnb & Disruptive Innovation.................................................................................................. 3  \nGeographic Data Science............................................................................................................. 5  \nStatement of the Problem ............................................................................................................ 7  \nLiterature Review ......................................................................................................................... 9  \nSignaling Theory, Trust, & Quality in Airbnb Valuation............................................................ 9  \nListing Amenities ...................................................................................................................... 12  \nGeographic Location ................................................................................................................. 14  \nMethodology ................................................................................................................................ 16  \nAnalytical Techniques ............................................................................................................... 16  \nGradient Boosting .................................................................................................................. 16  \nSentiment Analysis.............","cbCaip85qaWXtayd","https://ap.wps.com/l/cbCaip85qaWXtayd","pdf",11942992,1,189,"English","en",105,"# Research Statement\n# Introduction\n## Sharing Economy\n## Airbnb & Disruptive Innovation\n## Geographic Data Science\n# Statement of the Problem\n# Literature Review\n## Signaling Theory, Trust, & Quality in Airbnb Valuation\n## Listing Amenities\n## Geographic Location\n# Methodology\n## Analytical Techniques\n## Gradient Boosting\n## Sentiment Analysis\n## Shapley Values\n# Study Design\n## Materials\n## Data Sources\n## Airbnb Data\n## GIS Data","[{\"question\":\"What does the thesis predict in the Airbnb valuation problem?\",\"answer\":\"It predicts the short-term probability of receiving a review using decision tree modeling fed by listing, host, geographic, and response metrics.\"},{\"question\":\"How is geospatial information used in the modeling process?\",\"answer\":\"The approach enhances the machine learning workflow with geographic factors and data, which are combined with listing attributes and amenities during prediction.\"},{\"question\":\"How does the thesis determine which variables matter most?\",\"answer\":\"Model accuracy is assessed while varying inputs, and Shapley values are used to quantify and compare the importance of variables for the prediction task.\"}]","Airbnb Valuation - 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