Developing advanced control strategies is a relevant pathway to achieve energy savings in heating, ventilation, and air conditioning systems. Nevertheless, the studies presenting these controls usually quantify the potential energy savings by analysing a single housing configuration: a thermal envelope, a behavioural scenario and a type of external conditions. By doing this, the study omits that these savings highly depend on the material and behavioural environments. Sensitivity studies are helpful but insufficient to characterize the vast variability of housing situations. This research proposes a methodology that describes the relevance of automation in the living spaces in which they are installed and thus improves the social and technical relevance of the abundant literature on this topic. The methodology is divided into three parts. First, a database of representative housing situations is constructed. Second, the theoretical energy savings through advanced control are estimated using thermal simulation. Third, the database on energy savings and housing situation is segmented through a factorial analysis. This clustering is then used to identify the housing situations where the implementation of controls is most favourable. The article presents first the methodology and then an example of implementation. The computation is based on the TABULA database and studies the use of predictive control of heating systems in French housing situations. The results underline that the housing type, the insulation level, and the surface are significant determinants of the automation interest. The large segment of estimated average gains (between 1.8% and 17% of the reference energy consumption) underlines the high need to improve the targeting of the field