Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods
| dc.contributor.advisor | Salau, Sulaiman | |
| dc.contributor.advisor | Er Sebnem | |
| dc.contributor.author | Williams, Courtney | |
| dc.date.accessioned | 2024-06-19T07:50:58Z | |
| dc.date.available | 2024-06-19T07:50:58Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2024-06-06T13:16:13Z | |
| dc.description.abstract | This 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. | |
| dc.identifier.apacitation | Williams, C. (2023). <i>Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/39945 | en_ZA |
| dc.identifier.chicagocitation | Williams, Courtney. <i>"Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2023. http://hdl.handle.net/11427/39945 | en_ZA |
| dc.identifier.citation | Williams, C. 2023. Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/39945 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Williams, Courtney AB - This 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. DA - 2023 DB - OpenUCT DP - University of Cape Town KW - Statistical Sciences LK - https://open.uct.ac.za PY - 2023 T1 - Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods TI - Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods UR - http://hdl.handle.net/11427/39945 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/39945 | |
| dc.identifier.vancouvercitation | Williams C. Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods. []. ,Faculty of Science ,Department of Statistical Sciences, 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/39945 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Statistical Sciences | |
| dc.title | Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |