Can the deep retrofit of the housing stock cost less? Learning curves and the costs of off-site industrialised construction in France
Eleonora Righetto et al.
Abstract
The transition to energy-efficient buildings, central to the European EPBD Directive, requires deep retrofitting to reduce consumption and greenhouse gas emissions. However, high construction costs and fragmentation of the supply chain limit its large-scale deployment. Therefore, it is essential to investigate whether the adoption of off-site technologies can reduce construction costs, increasing the efficiency of production processes and the financial viability of interventions. The study assesses the extent to which learning-by-doing mechanisms in off-site production processes have reduced the costs of deep retrofitting, improving efficiency and productivity. The research analyses 22 French social housing projects completed between 2018 and 2025, including single-family homes and multi-family buildings. Using logarithmic learning curves, the relationship between unit construction costs and cumulative production is quantified, considering the sample both in aggregate form and distinguishing between the two types of buildings. The results show a significant reduction in costs, with average learning rates of 7.7% for single-family homes, 12.4% for apartments and 7.2% for the aggregate sample. Although lower than typical values in other industrial sectors, the data confirm the impact of learning by doing on the economic viability of deep retrofitting. Overall, the estimated rates place the industrialisation process at an intermediate stage of technological maturity: sufficiently advanced to generate significant reductions, but still constrained by scale, supply chain fragmentation, and operational complexity.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.