## 2 Materials and Methods
### Data from Design Offices and Data on Existing Single-Family Houses in Koszalin  
In order to develop regression models, both linear and non-linear, and to efficiently train machine learning models for predicting the buildings' usable area, it is crucial to use reliable and complete data. In the present study, two data sources were utilised: architectural designs obtained from online sources and measurements of actual buildings.  
The first dataset came from the architectural offices Lipinscy [40], Archon [41] and Extradom [42], which provide detailed building designs and interior plans. This documentation has been developed according to existing standards for calculating floor space, thus enabling precise analysis and minimising errors in the models. Architectural designs also offer a more accurate indication of living spaces, which is a crucial factor in the property valuation process. A total of 436 residential projects were analysed, divided into two groups. The data from the first two sources constituted the first sample (previously partially analysed in paper [1]), and the data from the third source constituted the second sample. All design parameters were adopted without modification. The buildings analysed also included garages and boiler rooms, which are not included in the building floor area. The building parameters considered are shown in Table 1.  
The second dataset contained data on buildings in Koszalin, in the north-western region of Poland. The selected buildings, constructed between 2020 and 2023, featured multi-pitched roofs and diverse architectural styles. Information about the parameters of these buildings was obtained from documentation provided by the District Building Control Inspector.  
The study was divided into several stages. Initially, classical multiple linear regression was employed to estimate the floor area of buildings. The second stage involved analysis using non-linear regression. The third stage involved the use of regularised linear regression to increase the accuracy of the models and eliminate variables with less predictive power. The fourth stage of analysis used advanced machine learning methods. The objective of each stage was to identify the optimal model for predicting the value of the floor area of buildings. The analyses were carried out independently for a dataset of architectural designs and existing buildings. The paper also makes reference to earlier studies presented in [1]. In particular, a comparison is provided of the results obtained using machine learning methods in the current study with the results obtained in the current and previous articles using linear and non-linear modelling. The parameters of the models were estimated on the basis of the analysed datasets. If attempts are made to adapt the models to other datasets, optimal parameter values should be estimated separately. Nevertheless, the analyses which were carried out indicate a recurring set of significant independent variables in the regression models, and the approach itself has sufficient versatility to potentially serve as a general methodology for modelling missing data in databases containing different property characteristics.