The proliferation of Airbnb was found to be associated with negative externalities, such as rising rents, house prices and gentrification. Nonetheless, it also seems to capture “positive” urban qualities related to the ordinary/traditional built environment. However, this relationship is still largely understudied as most works focus on small samples (a specific neighbourhood) and limited numbers of urban descriptors.  

In this work, we used the Urban MorphoMetric (UMM) approach in combination with machine learning techniques to investigate the relationship between urban form and density of Airbnbs in the whole city of Amsterdam (NL). More specifically, we, first, identify urban types (UTs), i.e. areas with homogenous patterns of urban form, via the clustering of a comprehensive set of 296 morphometric descriptors. Second, we investigate the relationship between UTs and density of Airbnbs via a composite machine learning technique, based on forward feature selection, gradient boosting and an interpretative tool of machine learning outputs. Third, we offer visual and textual profiles of the UTs most positively and negatively associated with density of Airbnbs.