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<p>Damp in residential buildings poses risks to indoor air quality, occupant health, and structural integrity, and affects up to 27% of homes in the England. This study develops a predictive model for damp risk, using 2,073 inspection records from a housing association across 125 local authorities. Homes were labelled as damp (1,630) or non-damp (443), with data supplemented by national Energy Performance Certificate (EPC) records, incorporating building characteristics and energy efficiency indicators. To evaluate model performance, both a balanced dataset (869 homes, 426 damp, 443 non-damp) and a larger imbalanced dataset (2,073 homes) were used. Seven machine learning algorithms were deployed, with the best-performing model achieving 0.636 accuracy on balanced data and 0.793 on imbalanced data. SHAP (SHapley Additive exPlanations) analysis identified heating cost, energy consumption, and wall energy efficiency as the strongest predictors of damp. Statistical tests and causal analysis were applied to interpret SHAP results, offering insights into potential damp risk and mitigations. The findings suggest that machine learning can support early identification of homes likely to develop damp, helping housing managers prioritise interventions before damp issues escalate.</p>

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A predictive model for damp risk in english housing with explainable AI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月12日

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  • Gulala Aziz,
  • Adam Hardy

摘要

Damp in residential buildings poses risks to indoor air quality, occupant health, and structural integrity, and affects up to 27% of homes in the England. This study develops a predictive model for damp risk, using 2,073 inspection records from a housing association across 125 local authorities. Homes were labelled as damp (1,630) or non-damp (443), with data supplemented by national Energy Performance Certificate (EPC) records, incorporating building characteristics and energy efficiency indicators. To evaluate model performance, both a balanced dataset (869 homes, 426 damp, 443 non-damp) and a larger imbalanced dataset (2,073 homes) were used. Seven machine learning algorithms were deployed, with the best-performing model achieving 0.636 accuracy on balanced data and 0.793 on imbalanced data. SHAP (SHapley Additive exPlanations) analysis identified heating cost, energy consumption, and wall energy efficiency as the strongest predictors of damp. Statistical tests and causal analysis were applied to interpret SHAP results, offering insights into potential damp risk and mitigations. The findings suggest that machine learning can support early identification of homes likely to develop damp, helping housing managers prioritise interventions before damp issues escalate.