With the affordability of housing becoming an increasingly pressing issue due to urbanisation, rising housing costs and widening income disparities, this study systematically examines the integration of artificial intelligence (AI) in addressing the complexities of affordable housing (AH) in Europe, highlighting its implications for sustainability and construction efficiency. To enable a comprehensive analysis, the study employs a systematic review approach with mixed methods, incorporating both a quantitative bibliometric analysis and a qualitative systematic analysis. The bibliometric analysis identifies influential journals, authors and research clusters that focus on governance and stakeholder dimensions, practical AI applications, sustainable innovation in urban contexts and policy-driven digital transformation. The systematic review categorises the literature into four main research topics: knowledge discovery, building automation systems, intelligent optimisation and big data and data mining. These areas show significant methodological strengths in AI prediction capabilities, resource optimisation, construction efficiency and real-time stakeholder collaboration. The results of the study highlight current trends, influential research sources and prevailing topics, while also revealing significant research gaps. These insights are critical for policymakers, academics and industry practitioners seeking to effectively and responsibly use AI technologies to create inclusive, affordable and sustainable housing solutions.
BRIGITTE STEINHOFF (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: