Mapping community need across a statewide neighbourhood house network

When a network of local services has grown up organically over decades, the hardest question isn't whether it's valued. It's where the next dollar should go, and what it should buy. This project gave a state department a consistent, comparable read on community need across every catchment its network serves, and a way to see the acute pockets that averages normally hide. The lesson for any organisation funding a distributed network: ranking tells you how much need a place carries, but only typology tells you what kind of support it needs, and you need both before you can fund well.

Neighbourhood houses are locally governed community hubs. They run social programs, education and skills development, health and wellbeing activities and targeted support for people doing it tough, usually with small teams and a lot of volunteers. Across this state, they form one of the most extensive pieces of community development infrastructure in the public system.

The department commissioned an independent needs analysis because the picture it had was uneven. Houses were reporting visitors with more complex needs and rising cost-of-living pressure, but there was no consistent evidence base that let the department compare one catchment with another, or explain a funding decision in the same terms across the whole network.

They wanted to understand:

• Where community need is highest across the network, on a consistent and defensible basis.
• What kind of need each community carries, so support can be matched to it rather than spread evenly.
• Where need is growing, so investment can move ahead of demand rather than behind it.

We built the evidence from the ground up, one catchment at a time. Each of the 38 catchments was defined using ABS Statistical Area Level 2 boundaries, one or more per catchment, so every house could be profiled with the same data on the same footing. Every indicator was benchmarked against the equivalent state figure, because a local number only means something next to the wider context.

We then distilled the profiles into a community needs index. It combines five need domains: socio-economic disadvantage, housing pressure, ageing and isolation, workforce and education vulnerability, and access and digital inclusion. Indicators are standardised across the 38 catchments, oriented so a higher score always means greater need, averaged within each domain, then combined with equal weight. Catchments are ranked from 1 to 38 and grouped into tiers.

Running alongside the modelling, we designed and ran a statewide stakeholder survey. It reached 2,741 people who use, volunteer for, work with or fund the network, in every local government area in the state. Its job was to test whether lived experience matched what the data said.

This enabled assessment of:

• Demographic profile and projected change to 2045.
• Socio-economic conditions, including income, benefit reliance and labour force attachment.
• Household and housing dynamics, including rental stress, mortgage stress and social housing concentration.
• Community strengths and assets, including volunteering and population stability.
• Barriers, pressures and vulnerabilities, including youth disengagement, employment vulnerability and travel time to health services.

We were explicit about the limits. The index is a relative comparison across these 38 catchments, not an absolute measure of need, and it complements the individual profiles rather than replacing them. It draws on 2021 Census data as its socio-economic backbone, so it carries the currency of that release. And because it reports at catchment level, an average can understate need where a highly disadvantaged community sits alongside more advantaged neighbours. That limitation turned out to be one of the most useful findings in the work.

• Need is widespread, but it presents differently. Nine catchments fall in the highest-need tier, and they split into two very different situations: entrenched township and urban disadvantage, and remote or rural communities where thin service access is the binding constraint. Around 18 of the 38 catchments sit in the bottom three statewide disadvantage deciles on their catchment average, and in 27 catchments government pensions and allowances are the main personal income source at a higher rate than the state. This matters because a single funding formula, or a single service model, will fit neither group well. It changes the investment question from 'how much per house' to 'which kind of pressure are we funding here'.
• The catchment average hides the sharpest need. Eight catchments look moderate or lower on the ranking yet contain a statewide decile 1 or 2 community offset by more advantaged neighbours. In some of these, a decile 1 or 2 core sits beside decile 8 to 10 areas, and roughly a third to a half of residents live in the disadvantaged core. If the department funded on catchment averages alone, these houses would look like a lower priority precisely where their work is most acute. This is why we reported a dual view: the catchment-average ranking alongside a concentrated-disadvantage flag. It changes the targeting rule, not just the ranking.
• Ranking tells you how much. Typology tells you what to fund. Grouping the catchments by the character of their need produced five types: entrenched socio-economic disadvantage, remote and rural access-constrained, concentrated or masked disadvantage, growth-pressure peri-urban, and mixed regional moderate. Each points to a different service mix: practical supports, family support and youth pathways where disadvantage is entrenched; health, wellbeing and social inclusion where communities are ageing, which is the case in 20 of the 38 catchments; and outreach, transport and digital access where communities are remote. It turns a funding conversation about quantum into one about mix.
• The data and the community said the same thing, and both pointed at resourcing. The survey independently ranked cost of living, affordable housing, loneliness and mental health as the biggest community challenges, which are the lived form of the income, housing stress and ageing pressures the modelling measured. Houses were rated very or extremely important by 95% of respondents, while only 7% believed they have enough funding to meet current demand. The barriers people named, led by transport, limited awareness and a sense that houses aren't 'for people like me', show access is shaped by more than distance. When two independent methods converge this closely, the evidence question is largely settled, and the constraint moves to resourcing and service design.

The network's futures diverge. Around 14 catchments are projected to lose population by 2045, typically the remote and rural communities that are also ageing fastest, while 14 are projected to grow by 5% or more, mostly peri-urban. Both directions create pressure, but they call for opposite responses: scaling capacity ahead of demand in the growth catchments, and sustaining access and connection for an older, thinning population in the declining ones.

The network's strengths are unevenly distributed too, and often strongest where pressure is greatest. Volunteering rates run above the state average in many rural and regional catchments, which is a real asset to build delivery on rather than a nice-to-have.

The framework itself is built to be refreshed. The 2026 Census will be the natural moment to update the socio-economic backbone, re-run the index and test whether the tiers and typologies have shifted.

The evidence base is with the department for its review, alongside the individual catchment profiles and the survey report. It gives the department:

• A single, consistent and defensible basis for comparing need across all 38 catchments, replacing case-by-case argument with a shared frame.
• A targeting rule that reaches both whole-catchment disadvantage and hidden pockets, so acute local need isn't lost inside a moderate average.
• A service-mix logic tied to typology, so support can be matched to the character of need rather than spread evenly.
• A forward view that separates catchments needing capacity growth from those needing sustained access.
• A reusable framework for funding, planning and monitoring that can be refreshed as new data lands.

If you fund or run a distributed network of services and you're weighing where the next investment should go, we can help you build the same kind of evidence base: consistent across every location, benchmarked, honest about its limits and built to be refreshed. Get in touch and we'll talk through what it would take for your network.

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