Clustering AirBnB Listings in Istanbul Using Unsupervised Machine Learning Techniques

The rapid expansion of Airbnb and the increase in the number of listings on the platform have raised concerns about the impact on housing markets and urban dynamics, particularly in regards to housing affordability. This study aims to examine the relationship between Airbnb listings in Istanbul, Turkey, a major tourist destination with a high demand for accommodations and a significant housing affordability problem. Using unsupervised learning techniques, the study clusters and analyzes Airbnb listings in Istanbul to identify patterns and trends in the data. In the study, k-means and fuzzy c-means algorithms are applied to group the data into various numbers of clusters and the results were evaluated using the Silhouette Score and the Dunn Index methods. Results showed that k-means clustering algorithm could be preferred to fuzzy c-means, with the optimal number of clusters being between two and five. Authors selected k-means algorithm with five clusters for further analysis and named the resulting clusters based on their most important characteristics, providing an overview of what to expect from listings within each group, revealing valuable insights for researchers and stakeholders in the housing market.