Estimating the level of airtightness of a building can offer valuable information for energy performance simulation tools or decision-making during retrofitting processes. However, it remains a challenge given the great variability of the variables involved, the complexity of addressing some of these variables, and some contextspecific features. Based on previous research in this direction, this paper proposes an alternative predictive model based on Generalized Linear Models (GLIM) and validated using cross-validation that involves 13 main effects and 4 interactions. This leads to a substantial enhancement in predictive capacity, accounting for nearly 50% of the response variability. A detailed set of variables fully described offers the opportunity to transcend region-specific applicability and opens a window for other populations. The model provides more reliable estimates of airtightness and expands its applicability to a broader range of construction conditions, while maintaining the statistical significance of its predictors and achieving a satisfactory fit.
