Awareness of the huge environmental impact of buildings has created more interest in resource efficiency and in the potential of circular economy. This is not only relevant for new buildings. Also the existing building stock represents a great amount of building materials, which could become available for reuse and recycling in the future. Currently, there is no database in Belgium that reflects the potential of existing buildings as material banks. Nevertheless, existing databases, e.g. the EPC (Energy Performance Certificate) database of the Flemish Energy Agency (VEA), contain valuable information on the material content in the existing building stock. Therefore, this paper explores the possibility of combining existing databases to reflect the (future) potential of the existing building stock as material bank. The two main databases under study are: 1) the EPC database of VEA which contains general building characteristics, such as building volume, building typology, floor area and information on the building envelope of more than 1 million buildings in the Flemish region and 2) the database of Essencia Marketing, a marketing agency specialized in the construction sector, which contains general building characteristics as well as geometrical and material data on almost 6000 new residential buildings spread all over Belgium (constructed between mid-2010 and mid-2017). The data in the databases are discussed and possible methods to combine data from both databases are explored.

Retrofitting existing buildings is a cornerstone of Europe’s strategy for a sustainable built environment. Therefore, accurate short-term forecasts to evaluate policy impacts and inform future strategies are needed. This study investigates the short-term predictive modelling of renovation activity in Belgium, focusing on overall renovation activity (RA) and energy-specific renovation activity (EA). Using data from 2012 to 2023, linear modelling was employed to analyze the relationships between RA/EA and macroeconomic indicators, market confidence, building permits, and loan data, with model performance evaluated using Mean Absolute Percentage Error (MAPE). Monthly data and time lags of up to 24 months were considered. The three best-performing models for RA achieved MAPE values between 2.9% and 3.1%, with validated errors ranging from 0.1% to 4.1%. For EA, the best models yielded MAPE values between 4.4% and 4.6% and validated errors between 8.9% and 14%. Renovation loans and building permits emerged as strong predictors for RA, while building material prices and loans were more relevant for EA. The time lag analysis highlighted that renovation processes typically span 15–24 months following loan approval. However, the low accuracy observed for EA underscores the need for further refinement. This explorative effort forms a solid base, inviting additional research to enhance our predictive capabilities and improve short-term modelling of the (green) residential renovation market.

Accurate service life (SL) data for building components are a critical yet poorly constrained input for life cycle assessment (LCA). Reference service life (RSL) values published in national frameworks are typically derived from technical standards rather than empirical observation. This paper compares two independent, methodologically aligned empirical datasets for windows, heating systems, and roofs for Belgium and The Netherlands. SL distributions are estimated using Weibull survival analysis, selected with AICc model comparison, and differences between survival functions are assessed using a Bonferroni-corrected Log-Rank test. Estimates are benchmarked against Belgian and Dutch RSLs. Two sets of insights emerge. First, on SL estimates: robust cross-national divergence is found only for flat roofs, is sensitivity-dependent for windows and pitched roofs and could not be assessed for heating systems due to incompatible definitions across surveys. Second, on survey methodology: both datasets query a single renovation cycle, biasing SL estimates towards longer values, particularly for shorter-lived components, underscoring that definitional alignment and multi-cycle survey design are essential for valid cross-national comparison. Together with the empirical SL estimates, these methodological lessons, of broader relevance beyond the present datasets, provide practical input for LCA and renovation planning in Belgium and The Netherlands, and guide future survey-based SL research.

In order to achieve a sustainable built environment by 2050, the European building stock needs to be retrofitted. Also in Belgium, with 75% of the residential buildings built before 1980, deep energy renovation is stimulated. The Flemish government premises long term goals for the energy performance of the existing building stock in the Renovation Pact (2014). To be able to monitor the progress and effectiveness of the Renovation Pact, knowledge of the actual renovation rate is indispensable. Since years, a renovation rate of 0.7% to 1% is communicated as an argument that renovation should be stimulated. However, there is little empirical evidence for this figure and how it reflects the actual energy renovation activity. Furthermore, non-energy renovation activities are considered as possible gate for home owners towards energy renovation, but little is known about these renovation activities. Since 2013, a yearly survey is done by Essencia Marketing, a marketing agency specialized in the construction sector, on the renovation activity of Belgian households. A representative sample of 1000 households indicates their renovation activity in the previous year through an online questionnaire on structural works (façade, wall, roof, etc.), technical works (HVAC, electricity, etc.) and energetic works (insulation, windows, etc.). For the period 2012-2018, the results show that each year almost 2,1% undertakes a deep energy renovation activity (excluding decorative works). To better estimate the energy renovation activity in Belgium, the paper will firstly discuss the different types of renovation works home-owners execute, and determine the share of energy related renovations. This will be confronted with official statistics on (energy) renovation. Then the non-energy renovation activity will be discussed to determine the share of renovations that could serve as a gate to promote deep energy renovation. This could inform policy makers on policy actions to stimulate combined renovations.