Cape Town Airbnb price prediction: an exploration of spatial statistic and machine learning methods

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.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2023

Author dc:creator

• Williams, Courtney

Advisors dc:contributor.advisor

• Salau, Sulaiman
• Er Sebnem

• Statistical Sciences

Handle dc:identifier.uri

http://hdl.handle.net/11427/39945

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/39945