## 3 Results and Discussion  
Firstly, before starting the presentation of our results, it is crucial to show the findings in a usable manner. For this reason, we may divide the total into various probable applications:  
* Useful reference buildings for evaluating the possibility of planned improvements to existing buildings such as evaporative cooling, intelligent control of air conditioning systems, and so on. * Useful reference buildings for assessing the cost of intervention and the associated benefit following energy retrofitting of existing buildings. * Useful reference buildings for estimating the cost of retrofitting existing buildings to meet stronger national rules aimed towards nZEB. Once the potential uses are outlined, the various levels of cataloging reference buildings may have diverse applications. The grouping method 1 is appropriate for research with a high degree of detail, focusing on only one of the nations participating in this study, or focusing on only one building type. This is due to the fact that each country is represented by six reference buildings. Furthermore, by quantifying envelope properties, the use of these data in other works might give the option to conduct investigations with a high degree of detail. In the same way, Clustering Method 2 would be applied. The data acquired using this method can be used in studies that are required for reference buildings in the European Mediterranean region. However, in case of implementing reference buildings in more general studies, such as those focused on the consumption derived from the quality and geometry of the envelope or in works with a large computational part, it would be more convenient to use the reference buildings derived from clustering methods 3 and 4. It is important to note that the buildings derived from implementing [PERSON]'s minimum variance method are an approximation of agglomerating all the original reference buildings into a few buildings, allowing large-scale building improvement studies to be conducted without the number of possible cases making the task impossible in terms of calculation time. In order to present our results in a clear way, we will focus on classifying them according to the previously mentioned clustering methods. First, a compilation of information provided by the member countries is displayed, with this grouping being the outcome of Clustering Method 1. Second, after implementing the previously described approach, the results of Clustering Method 2 are provided. Finally, the results from clustering methods 3 and 4 will be presented by using the [PERSON]'s minimal variance method. * Clustering Method 1 results  
After an exhaustive literature review, 42 reference buildings were generated from the seven member countries. The results are presented in Supplementary Materials, in Table S1. Thermal characterization of the building envelope of the Mediterranean-European building stock. The parameters studied are the type (MFH, SFH), the shape of the floor plan and whether it adjoins other dwellings, the floor area, the number of floors, the area of external walls, party walls, and windows broken down by orientation, and the U-values of each of the components of the thermal envelope. * Clustering Method 2 results  
Strongly linked variables generate important concerns in clustering analysis findings, and therefore variable reduction should be performed prior to clustering to eliminate redundant variables. In this research, five different variables (U-values, footprint area, shape, number of floors, and party walls) concerning the previous indicators were used. The clusters that were formed for each variable are presented in Table 5.  
* Clustering Method 3 results In order to identify the more representative typologies, we employed hierarchical clustering with [PERSON]'s minimum variance method [28], which is a method that is not very common but has been used in recent studies, such as [PERSON] et al. (2019) [29].