Predicting the occurrence of landsliding is of key importance for understanding the geomorphological development of mountain environments as well as to assess the potential risk posed by landsliding to human societies in such environments. Global landslide susceptibility models use a generic model formulation to predict landslide susceptibility anywhere on the planet from openly available data. Regional models, on the contrary, use local information on landslide occurrence to constrain model parameters and may also bene t from better spatial information with respect to controlling factors. This study aims to investigate the added value of the construction of regional landslide susceptibility models (versus global and continental models) in the western branch of the East African Rift, a data-scarce landslide-prone tropical environment. First, a comprehensive landslide database containing 6446 instances was compiled for the study area using Google Earth imagery. Second, three regional data-driven landslide susceptibility models were developed. Third, the efforts to construct these regional models were quanti ed by analysing how their quality is impacted by (1) the use of more accurate, regional peak ground acceleration and geology data, and (2) an increasing inventory size. Fourth, regional and global/continental models were compared in terms of predictive power and geomorphological plausibility. We observe that global/continental landslide susceptibility models are capable of identifying landslide-prone areas, but lack prediction power and geomorphological plausibility when compared to our regional models. Importantly, this difference in quality is not driven by the use of more accurate and detailed geology and peak ground acceleration data, but rather by the use of a detailed regional landslide inventory to calibrate the models. We also show that the model quality only increases marginally beyond a certain inventory size. We conclude that the regional landslide susceptibility assessment does provide an added value compared to existing global models in terms of geomorphological plausibility and model performance, whereby the largest gain is to be found in the construction of a regional landslide inventory, rather than the investment in more detailed covariates or the application of more complex modelling techniques. The latter suggests that the role of controlling variables depends, to some extent, on the regional context: making adequate susceptibility predictions proves di cult when local conditions are not accounted for by means of a regional inventory.