Overview of the Report
The study “Aspirations and Applications of AI in Social Housing” was produced by Service Insights Ltd, a consultancy specializing in research for the housing sector. The authors – Dr Simon Williams (Managing Director, Service Insights Ltd), Dr Nicky Shaw and Dr Emma Forsgren (both senior academics at Leeds University Business School), and Stephen Blundell (Associate Consultant, Service Insights Ltd) – combine expertise in AI technology, public policy, and social housing operations. The research, funded in 2025, examines how English social housing providers are currently using artificial intelligence and what they aim to achieve with it in the future.
Current AI Adoption in Housing
Data from 220 employee surveys and 50 in‑depth interviews across nine housing organisations reveal that AI use is still early‑stage. While 22 % of staff report AI tools being made available for specific roles, a higher 31 % actually use AI in practice. Of those users, 93.8 % consider AI beneficial, citing time‑saving tasks such as summarising documents, generating ideas, and simplifying complex information. However, 64.4 % of respondents are unsure about the existence of AI tools in their organisation, indicating limited top‑down visibility.
Aspirations for Predictive AI
Respondents express strong interest in moving from generative tools (e.g., large language models like ChatGPT) to predictive AI models that can support core services such as maintenance forecasting, tenant risk assessment, and resource allocation. The study notes that while predictive aspirations are prominent, practical implementations remain limited, with most organisations still experimenting at the front‑line level rather than through formal policy.
Values, Ethics, and Equity Concerns
Only 41.7 % of employees feel AI is being built with organisational values and ethics, and confidence in identifying bias is low (36.7 %). Perceptions of AI helping to provide fairer services (40.8 %) or increase awareness of vulnerable tenants (49.3 %) are modest, suggesting that equity and inclusion considerations are not yet central to AI deployment strategies.
Policy and Governance Gaps
The research highlights a mismatch between practice and policy: 13.5 % of staff are aware of any AI usage policy, and merely 3.8 % know of an AI strategy. Employees report limited training (6.3 % aware of AI training) and mixed confidence in using AI tools (24.9 % rate their confidence as good). This “policy catch‑up” is identified as a persistent challenge for the sector.
Data Quality as a Foundation
High data quality is recognised as essential, with 93.1 % agreeing it underpins strategic goals and 92.0 % affirming its importance for daily operations. Yet only 43.2 % find data easy to access and 42.2 % trust its accuracy, underscoring a critical barrier to effective AI outcomes.
Implications for Sustainable Housing
The report links AI potential to broader sustainability objectives: AI could improve cost efficiencies (68.2 % see cost benefits), boost productivity (67.4 % anticipate higher staff efficiency), and support smart resource management in housing stock. Nonetheless, concerns about environmental impact of AI technologies are low (29.7 % aware), indicating a need for greater integration of sustainability metrics in AI planning.
Recommendations for Pan‑European Stakeholders
- Promote sector‑wide data standards and maturity models to enhance data reliability.
- Develop clear AI governance frameworks that embed ethical, equity, and sustainability criteria.
- Encourage shared learning platforms for best‑practice AI applications across housing providers.
- Invest in targeted training to raise staff confidence and competence in AI tools.
- Align AI initiatives with EU‑wide sustainable housing targets, leveraging AI for energy‑efficient building management and tenant wellbeing.