Since the beginning of the Russian invasion of Ukraine on February 24, 2022, many people have fl ed the war and left their home country. By the end of January 2023, more than one million Ukrainian refugees had been registered in Germany alone. In contrast to refugees from other countries of origin in Germany, Ukrainian citizens can choose their place of residence if they have either found private accommodation with family members or friends or do not claim state support. However, little information exists on where within Germany Ukrainian refugees have moved and why certain regions are potentially more attractive than others. There exists a substantial literature on the location choices of migrants in general, showing that the existing level of immigrant concentration is an important determinant, while economic factors have a smaller effect – if not in the initial location choice after immigration, then at least in later location decisions. Whereas these studies mainly focus on labour migrants, research on refugees’ location choices is still scarce, because refugees are usually assigned to specifi c places of residence by the authorities in many European countries. In the context of forced migration, spatial patterns may therefore largely be related to administrative decisions. In this paper, we aim to answer the question of the settlement patterns of recently arrived refugees from Ukraine in Germany by using current data from the Central Register of Foreigners. These patterns are modelled on the NUTS-3 level and consider the proportion of previous Ukrainian migrants living within those regions as well as additional economic, demographic, and geographical factors. Spatial regression models show that, on the one hand, Ukrainian refugees indeed settle where the number of Ukrainians is already high. The empirical analyses also indicate a correlation between the spatial patterns of refugees in general and Ukrainian refugees, suggesting that dispersal policies may play a role in explaining settlement patterns. Furthermore, affordable housing and lower rents are important explanatory variables.