Climate change has shifted weather and climate patterns and increased the frequency and severity of extreme events. Many studies highlighted that the exposure to extreme events is unevenly distributed across socio-economic groups, although the observed effects vary in direction, magnitude and across spatial areas. The study focus of this thesis was Switzerland, and aimed to provide a first understanding of how exposure to extreme heat and floods affects different socioeconomic groups among the Swiss population. For this purpose, socio-economic data from the Swiss Neighborhood Index of Socio-Economic Position (N = 1’527’168) was used, where each observation represented a residential building with a corresponding socio-economic position (SEP). Using ArcGIS, these observations were compared to land surface temperatures (LST) and to the Aquaprotecet flood risk layers. Four different thresholds for extreme heat (LST thresholds of 36.3°C, 37.2°C, 39.4°C, 41.9°C) and floods (recurrence periods of 50-year, 100-year, 250-year, 500-year) were applied, and general (entire Switzerland) as well as spatial (urban, intermediate, and rural areas) logistic regression (LR) models with cluster robust standard errors were calculated in R-Studio. The LR results showed that the exposure to extreme heat changes the direction across the four thresholds. In the general models, residential buildings with lower SEPs experienced less exposure to lower thresholds, while this effect changes to the opposite for higher thresholds. In the spatial models, buildings in urban areas experienced a similar level of exposure for the lower thresholds but showed less exposure on higher thresholds for the buildings with the highest SEPs. However, the rural areas in the spatial models indicated an opposite trend where vulnerable people were less exposed, although the interpretation of this result is limited due to an imbalanced distribution of observation across SEPs in rural areas. In case of floods, residential buildings with higher SEPs experienced significantly less exposure throughout all models and spatial areas.