The 2021 Australian Census collected data from every household in the country. Most founders either don't know it exists at the suburb level, or find it too dry to wade through. This article is my attempt to make it practically useful — the variables that actually matter for a location decision, and what each number is really telling you.
What the Census actually tells you about a suburb
The Census collects data across hundreds of variables at the suburb level (Statistical Area Level 2). For business location analysis, the variables that matter most are: median household income, age distribution, household type, employment type and population density. Each tells you something different about whether a suburb can support your business.
$95K
Australian median household income (ABS Census 2021)
$140K+
Median HHI, selected premium SA2s e.g. Mosman, Toorak, Cottesloe (ABS Census 2021)
$62K
Illustrative lower-quartile outer-suburban SA2 (ABS Census 2021 — examples vary)
Why median household income is the most important single variable
Median household income predicts discretionary spend more reliably than almost any other variable. For cafes, specialty retail, gyms and restaurants, above-average income means customers with the means to spend regularly at your price point. This does not mean low-income suburbs cannot support businesses — it means you must align price point and model accordingly.
What age distribution predicts
Suburbs skewed 25–40 (ABS age/income mix) line up with higher visit frequency for cafes, casual dining, gyms, childcare and convenience retail. Suburbs skewed 60+ skew toward health services, lower-price dining and everyday services — less spend on boutique coffee or premium fitness. This is why the same business can thrive in one suburb and fail 2km away with similar rent.
Suburb-level demographic data reveals customer profiles that shift dramatically within just a few kilometres.
Demographic profiles by business type
Cafes & specialty coffee: 25–45 professional demographics. Gyms & boutique fitness: 22–42, higher income, high apartment density. Family restaurants: 30–45, family households, suburban areas. Premium retail: 30–55, above-median income, owner-occupiers. Quick service takeaway: works across most demographics.
Population density and catchment size
Raw population within your catchment matters alongside demographics. A suburb with 7,000 residents within 500m gives a much larger potential customer pool than one with 2,000 — even with identical demographics. High-density residential areas (apartment precincts) are particularly valuable for destination businesses like gyms and specialty retail.
How Locatalyze combines Census data into a score
Published Locatalyze reports use Scoring v2.1: Rent Affordability 20%, Competition 25%, Market Demand 20%, Profitability 25%, Location Quality 10%. Census variables — median income, age mix, density, employment — feed primarily into Market Demand and Profitability, alongside live competitor and rent benchmarks. Competition and Location Quality add non-Census layers (mapping, access, anchors).
How inputs differ by business type (weights stay v2.1)
For a café, daytime employment and walk-past proxies weigh heavily inside Market Demand. For a gym, residential catchment within 2–3km and income skew matter more inside the same factors. For a restaurant, lunch worker density and evening residential spend both feed the model. The headline percentages do not change — the data going into each bucket does.
Why the same address scores differently by category
A café needs workers and passing traffic; a gym needs residents who will return several times a week; a restaurant blends both. Calibrating inputs by business type avoids pretending a gym lives or dies on the same footfall curve as a takeaway coffee window — while keeping a single transparent weight table (v2.1) across the product.
This is why the same address can score 78 for a café and 52 for a gym. The address has not changed. The business model has — and the engine stresses different signals inside the same five-factor frame.
About the author
Prashant Gupta
Founder, Locatalyze
Prashant is the founder of Locatalyze. He built the product after watching a food business he was involved with close because of a bad location decision — the kind that better data would have prevented. He launched Locatalyze in 2024 to make location analysis accessible to independent operators before they sign a lease, not after.
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