Abstract The construction and use of living space require substantial amounts of energy and resources, and generate Greenhouse Gas (GHG) emissions. Historically, the increase in total living space has largely offset improvements in building shell efficiency and heating technologies. Average per-capita space continuously rises due to developments such as smaller household sizes, a preference for single-family homes, parents remaining in dwellings after their children moved out, and numerous barriers to downsizing. This trend leads to substantial under-occupation for significant parts of the population, while others remain without access to adequate living space. Therefore, sustainable transition scenarios for the building stock must go beyond modelling building efficiency and heating technologies, which is the current focus of existing building stock models. To address this gap, we propose a new framework that accounts for building stock occupancy—i.e., how the population distributes across existing dwellings. We use data on approximately 30,000 individuals in 15,000 households from the German Socio-Economic Panel (G-SOEP) over the years 1984–2022 to populate a representative model database. On this basis, we develop a macroscopic occupancy model for residential dwellings that matches the population to the dwelling stock and simulates moving patterns over time, differentiating 100 household types and 112 dwelling types. The model simulates the moving rates by household types, their preferences, and relocation in iterative steps. This allows to model the developments of occupancy distribution and the effects of interventions. We analyse exemplary scenarios with socio-demographic differentiations that show how patterns could be altered to reduce under-occupation.
Thema et al. (Wed,) studied this question.