Predicting modeling and analysis of moisture damages in Swedish buildings Moisture damage in buildings poses significant challenges, leading to costly repairs and negative impacts on indoor environments. Understanding the patterns and prevalence of such damage is crucial for implementing effective preventive and mitigative measures. This report synthesizes findings from the comprehensive study on moisture damage in Swedish buildings, highlighting the complex interplay of multidimensional variables that contribute to these issues. Analyzing 2,100 moisture-related damage records from empirical damage investigations from 2014 to 2020, the study employs data-driven analytical techniques to identify primary moisture damage profiles and evaluate their prevalence. Multivariate analyses reveal diverse associations among factors related to moisture damage, with significant variations in damage types across different building components, phases, and actors responsible. The findings indicate that moisture damage is most prevalent in buildings constructed between 1960-1980 and 2000-2020, with common issues including microbial growth, deformation of building envelopes and roofs, odors caused by humidity-related problems and wind-driven rain. Buildings with non-ventilated crawlspaces and non-ventilated attics are particularly susceptible. By characterizing moisture damage patterns and their associated factors, these findings enhance our understanding of moisture damage occurrences and informs strategies for relevant actors to improve their practices on moisture damage prevention along building lifecycles. Furthermore, various predictive modeling techniques and machine learning algorithms were explored to predict the presence of prevalent moisture damage types, including microbial growth, deformation, odor, and water leakage. The binary and multilabel classification models achieve high prediction rates of over 0.9 average AUCPR and F2 scores by training with building-related features in cross-validation and validation. The lead binary relevance models estimate that approximately one-third of school buildings, 20% of commercial and office buildings, and 15% of residential dwellings in the uninvestigated Swedish building stock may contain unreported moisture damage. These findings advance quantitative research on moisture damage, providing new insights and tools for assessing the extent and patterns of in-situ moisture damage, thereby aiding existing moisture safety evaluations, and building maintenance strategies. Specifically, the results can help building owners, insurance companies, and damage investigators predict and address potential moisture damage in buildings. They also support developers, designers, and contractors in making informed decisions about construction solutions, helping to avoid designs with a high risk of damage. By preventing moisture damage in both existing and new buildings, we can significantly reduce costs and the environmental impact of remediation and material replacement, which currently result in substantial expenses and CO2 emissions.