…and where health comes in
Today, the social housing sector plans its investment around component replacement cycles. A kitchen is 20 years old, so we replace it. A roof is 30 years old, so we should have a look at it.
At best, capital investment is prioritised on the physical condition of the housing stock alone. This overlooks the direct impact that living environments have on residents' health, and the costs that falls on other public services, particularly the NHS. The system is fundamentally unoptimised: it spends on treatment rather than on prevention by fixing the root cause of poor health. That is the opposite of what government priorities, and logic, suggest we should be doing.
The same approach applies to large government funding streams such as the Warm Homes: Social Housing Fund, which is distributing over £1 billion in Wave 3. That follows £1 billion spent under previous governments, before counting the match funding provided by housing associations themselves.
DG Cities has developed a tool that brings together every available social housing dataset: stock condition surveys, repairs history, resident complaints, EPC data and more.
Here, the survey tracker: when each element in every asset group is due for replacement.
Alongside the surveys sits three years of repairs history, so we can see which homes keep failing and why, not just which components are old.
Darker cells show asset groups needing far more repairs per home than the borough average.
The tool uses this to prioritise investment, optimising spend by year and investment type. It tells housing providers what to do, at which homes and when.
We believe this data-driven approach is the first of its kind for housing providers. It lets them make confident, validated choices about where to spend their money.
The same methodology can support regulatory reviews and help explain decisions to council tenants and leaseholders.
Housing investment is optimised to achieve better physical homes. It could do a lot more.
Each housing condition has a measurable health consequence, and a matching investment that could prevent it.
Scientific evidence shows that warmer homes reduce GP visits for asthma events by 6.5%, and broader respiratory events by over 10%.1
When selecting homes for retrofit, we should therefore target the homes where these conditions are more likely.
And it doesn't stop at cold. Heat stress in homes can lead to more cardiac problems. Certain home archetypes can lead to more falls, or more severe ones.
We can take QOF data, GP-level indicators of how common each condition is, and map it across the housing stock.
That gives a completely new way to categorise homes, and lets investment programmes shift their focus towards homes where residents have particular needs.
Shown: COPD & asthma prevalence by neighbourhood (LSOA).
The same data can be used to design and deliver pre-emptive public health interventions.
Here is the tool's Warm Homes programme builder. It lists every social home that qualifies for retrofit on physical grounds: poor EPC, fabric due for replacement, or recurring mould.
Now we target homes not only on their physical condition, but on relevant health conditions too.
Let's assume that higher asthma propensity should help prioritise where investment goes.
Both lists are drawn from the same pool of social rented homes that already qualify for retrofit. The only difference is whether health is considered.
Ranked on damp & mould repairs, retrofit need (SAP score) and a low leaseholder share in the asset group, weighted equally.
The same score, combined 50 : 50 with the COPD & asthma prevalence of the home's neighbourhood.
One list targets homes because of their fabric. The other targets people whose health outcomes are partly driven by their homes.
The same applies across other health metrics and home investments.
In none of these is health data driving asset investment decisions.
We'd like to change that, and we are looking for partners to work with us on it.
Send us your thoughtsbalazs.csuvar@dgcities.com · dgcities.com