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Data & AI for housing associations
A housing association manages thousands of homes and just as many stories.
Property data, maintenance histories, repair requests, rent payments and contact with tenants are spread across different systems. This makes it difficult to see which housing complexes need maintenance, where damp and mould keep returning, or where tenants are running into difficulties. Data and AI bring these sources together.
Challenges,
Housing associations face the need to make homes more sustainable, a backlog of maintenance, a shortage of housing and tenants who need more support — all at the same time. They need to allocate their available people and budgets as effectively as possible. That calls for a reliable picture of each home and housing complex.
How we support you,
We bring property, maintenance and tenant data together in one data foundation. On that foundation, we build reports and models that show where maintenance, sustainability improvements or contact with tenants will have the greatest impact. We also help staff find answers in policies, contracts and case files more quickly. These are a few examples of what is possible. The right solution always depends on the challenge and the organisation.
DSL in numbers,
40
Data experts
10+
Years of experience
200+
Projects completed
8,7
Client satisfaction
Get in touch with our experts,
Want to know what we can do for your organisation?
Younes Seghrouchni
Managing Consultant GenAI & Data StrategyE-mail info@datasciencelab.nl
Carmen Wolvius
Managing Consultant Data Science & AIE-mail info@datasciencelab.nl
Stef Stoelwinder
Business DevelopmentE-mail info@datasciencelab.nl
Where we do that,
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The organisations that do this,
Frequently asked questions,
AI can combine maintenance histories, repair requests and housing data to reveal emerging problems earlier. This helps a housing association plan maintenance more effectively, target sustainability improvements and support tenants sooner.
Start with one question that currently takes a lot of time to answer, such as recurring repairs in the same housing complexes or the order in which homes should be made more sustainable. Existing records can quickly show whether data gives a clearer picture than current estimates.
More than you might think: property data and energy ratings, maintenance plans, repair requests, inspections, rent payments and contact with tenants. The work lies in linking these sources for each home and housing complex so they provide a single picture.
We determine which data is genuinely needed for each question, set access by role and show sensitive indicators only to staff who need to act on them. A model flags an issue; a member of staff decides what to do about it.
By showing the results where maintenance planners, lettings staff and neighbourhood teams already work, and agreeing who will maintain the model as the housing stock or policies change. That keeps it useful in practice.