Date of Graduation
12-2023
Document Type
Thesis
Degree Name
Master of Science in Geography (MS)
Degree Level
Graduate
Department
Geosciences
Advisor/Mentor
Limp, Fredrick W. Jr.
Committee Member
Holland, Edward C.
Second Committee Member
Cothren, Jackson D.
Keywords
Game Theory; Geospatial Data Science; Machine Learning; Spatiotemporal Analysis
Abstract
This 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 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.
Citation
Wyatt, K. (2023). Airbnb Valuation: A Machine Learning Approach. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5144
Included in
Business Intelligence Commons, Geographic Information Sciences Commons, Management Sciences and Quantitative Methods Commons, Spatial Science Commons
