Predicting buildings' EPC in Italy: a machine learning based-approach

Francesco Braggiotti (), Nicola Chiarini (), Giulio Dondi (), Luciano Lavecchia, Valeria Lionetti (), Juri Marcucci and Riccardo Russo ()
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Francesco Braggiotti: Datasinc
Nicola Chiarini: Datasinc
Giulio Dondi: Datasinc
Valeria Lionetti: Bank of Italy
Riccardo Russo: Bank of Italy

No 850, Questioni di Economia e Finanza (Occasional Papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: EU member states have committed to achieving carbon neutrality by 2050. Given that building-related activities contribute to almost a quarter of EU greenhouse gas emissions, the implementation of new regulations to decarbonize this sector is paramount. However, policymakers must carefully evaluate policies to mitigate transition risks associated with these regulations, as buildings represent a significant portion of household wealth and bank assets. Accurate metrics regarding buildings' energy efficiency, including the energy class reported in Energy Performance Certificates (EPCs), are essential for such evaluations. In this study, we developed a machine learning-based model to predict the energy class of Italian buildings using publicly accessible data. The model, trained on a geographic subset of the Italian territory, achieves a 37% accuracy rate, which increases to 74% when allowing for a one-class margin of error (1-class accuracy). Further testing against a mortgage portfolio provided by a commercial bank yielded a 69% 1-class accuracy. Comparison with statistics reported by the official EPC register (SIAPE) suggests a potential discrepancy in the representation of the worst energy efficiency class.

Keywords: energy performance certificates; EPC; Italy; buildings; transition risk; machine learning; random forest classifier; random forest (search for similar items in EconPapers)
JEL-codes: C45 C53 C55 Q47 Q54 Q58 (search for similar items in EconPapers)
Date: 2024-06
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