## 4 Discussion  
The analysis of the obtained results leads to several methodological conclusions, as well as insights into the technical and practical aspects of modelling. Additionally, it highlights systemic solutions for real estate data collection, reliability assessment, and their application in real-world property management tasks. The hypothesis of the superiority of ML models was confirmed by the analyses conducted. Although no single, definitively best model was identified, in most cases, the obtained ML models proved to be more resilient to data variability and delivered slightly better results than regression models. At the same time, no model type can be directly transferred between real-data and design-data modelling tasks. Also, for each estimation task related to a specific local market, a separate analysis must be conducted, and models must be estimated independently. These two features are probably the main limitations of the proposed method. The overall conclusion of the analysis is that if we are investing effort in modelling, it is more efficient to invest in ML models, in particular, simple neural networks. In addition, as demonstrated in Sections 3.4 and 3.5, it is worth considering the use of hybrid models that utilise different component models for specific groups of buildings. In the conducted study, the hybrid model combining gradient-boosted trees and regression delivered significantly better results than any of its individual component models. A similar improvement was observed for the hybrid of a neural network and regression, with the hybrid containing a non-linear model providing the most accurate overall estimates of the usable floor area for designed buildings. Meanwhile, the simplest hybrid model, which replaced underestimated usable floor area values below a certain threshold with that threshold value, produced almost as accurate estimations as the best-performing model while requiring considerably less effort to develop. At the same time, it should be noted that creating such post hoc hybrid models is merely a temporary measure. The correct approach should involve the ex ante identification of coherent groups of similar objects, for which optimal models will be developed within individual classes, followed by the integration of these models across groups during the testing phase. This approach will be further analysed in future research on building usable area models, particularly in the development of a solution that incorporates models for buildings with different roof structures--flat, gable, and multi-pitched. Since all models are local, the generalisation of such a modelling task turns into a new modelling round with every change in the scope (switching a local market will end up in a new set of models). However, it is still possible to build an application (e.g., with a modelling backend mechanism running on a server in the cloud) that will not only use previously estimated models but also estimate new models on the fly. Building such an application is planned. An important methodological conclusion is the confirmed necessity of traditional variable selection by an operator. The study demonstrated that regularisation within the model does not work as effectively in the examined cases as manual adjustments to the set of explanatory variables. This is likely due to both the relatively small size of the models under consideration and the considerable heterogeneity of the data. In such cases, the role of an expert is invaluable in designing the learning procedure and selecting models tailored to the specific scenario, such as a local real estate market. Another important factor is that in tasks with only a few explanatory variables and a training set ranging from several dozen to around two hundred instances, the number of parameters in a regression model (without interactions) is too small to fully capture the relationships between variables, which are not entirely revealed in such a limited dataset.