Areality is an independent platform that turns public data into plain-English suburb scores. No sponsored placements. No real estate portals.
Anyone making a suburb decision in Australia — first home buyers, renters, investors, newcomers, families choosing schools, people moving closer to work. We try to surface information that used to require hours of research.
Our scores are built from official public and government statistics — not commercial data providers, real estate portals, or scraped content, and with no sponsored placements. We combine and transform those datasets into comparable, plain-English scores.
We don't predict property prices. We don't give financial advice. We don't rank suburbs as objectively 'good' or 'bad' — every score is relative and every weighting is a judgement call. Read the limitations.
All scores are 0–100. Higher is better. Scores are rank-normalised within each state or territory — they are relative, not absolute. The Safety score blends a within-state position with a cross-state position so you can compare suburbs both locally and nationally.
Severity-weighted crime rate per 1,000 residents over the 3 most recent years, then rank-normalised (higher score = lower crime). The final Safety score blends two equal parts: a within-state position (how the suburb compares to others in its own state, across all crime types) and a cross-state position (how safe the suburb’s state is nationally, anchored to ABS Recorded Crime — Victims, the only nationally comparable source). See the full breakdown below.
Capped weighted composite: transport stops (25%), schools weighted by a school-quality index (20%), supermarkets (20%), parks (20%), libraries/community centres (15%). The school sub-score multiplies count by a school-quality factor — suburbs with higher-quality schools score higher than those with the same number of lower-quality schools.
Affordability measures housing cost relative to local income, not just the sticker price. For each suburb we take median rent and median mortgage repayments as a share of median household income, rank every suburb nationally on each, and average the two (higher score = more affordable). Because it is income-relative, an expensive suburb with high incomes can still rate as affordable. Two caveats: mortgage repayments are only reported up to "$5,000 and over", and median rent and median mortgage repayments are compared against median household income, which is drawn from a different mix of households, so these are the standard housing-stress approximations.
A national socio-economic disadvantage index (2021) — percentile rank per suburb. Score of 100 = least disadvantaged; 1 = most disadvantaged. Falls back to median household income rank-normalisation where the index is unavailable.
Public-transport access from stop-location data: rail stations within 1km of the suburb (40%), bus stops within 400m (30%), distance to the nearest train station, inverted (20%), and overall stop density (10%).
Safety is our most-asked-about score, and the hardest to make comparable across the country — every state publishes crime data differently. Here is exactly how it is built.
For each suburb we sum reported offences over the three most recent years, weighting each offence by severity (violent offences count for the most, then property, then drug and public-order). We divide by the suburb's population and express it per 1,000 residents per year, so a small town and a big city suburb can be compared fairly.
The Safety score has two equal components. The within-state part ranks each suburb against others in its own state using that state’s full police data (all crime types, including assault and property offences). The cross-state part anchors to ABS “Recorded Crime — Victims”, the only source that publishes comparable figures across all states and territories — covering homicide and related offences, sexual assault, and kidnapping/abduction, the offences it publishes as nationally comparable. Assault, robbery, and property offences are excluded from the cross-state comparison because each state uses different definitions and counting rules that make direct comparison misleading. The two parts are averaged to produce the final score, so a suburb’s local safety picture (all crime types) still carries full weight, but inter-state ranking is anchored to the offences that can be compared fairly.
A suburb with only a handful of residents can swing wildly on a single incident, so suburbs below 50 residents don't get their own rate. Instead they inherit the combined crime rate of their surrounding SA2 (a group of nearby suburbs) — the same approach used for all of WA. This is why almost every populated locality on the map is now coloured, where previously thousands of small towns showed up grey.
Only areas where the entire surrounding SA2 has fewer than 50 residents — national parks, water, industrial zones and genuinely uninhabited country — are left grey. Nationwide that is about 80 of 15,000+ localities. A suburb is never grey simply for having low crime: zero reported offences yields the safest possible score, not a blank.
The finer the source data, the more precise the suburb score. Where data is published for a wider area, every suburb inside that area shares the same rate.
Beyond the five lens scores, each suburb page includes the following data layers. These appear in the map detail card, the Profiles explorer, and the Compare view.
Shows first-preference party percentages for every federal election from 2010 to 2025, for suburbs in all 8 states & territories. Official polling-place results are spatially joined to suburb boundaries — each booth is assigned to the suburb it sits within, then votes are aggregated per suburb.
Covers ALP, Liberal/National coalition, Greens, One Nation, and Other. Each party is also compared against the official national and state averages (deltas and a dashed national line on the trend chart). The lean label is a disclosed arithmetic rule — it compares the suburb's ALP+Greens share against its Coalition+One Nation share and positions that gap relative to the nation — not an endorsement.
A linear regression is fitted to a suburb's historical crime-rate series (offences per 1,000 residents). The dashed line on the crime chart extrapolates the trend two financial years forward.
The projection is a straight-line extrapolation of the historical trend — not a predictive model. It is excluded when the most recent year is still in progress (partial year), since incomplete data biases the slope.
A composite signal built from four indicators: real rent growth (CPI-adjusted, Census data), population growth (2016–2021), crime trend direction (declining = positive), and accessibility relative to the national median.
Each signal is normalised to −1 → +1. The final score (0–100) is a weighted sum: rent 30%, population 25%, crime 25%, accessibility 20%. Labelled High / Moderate / Stable / Declining.
These are honest notes about where our data falls short. We think transparency is more useful than false confidence.
Income, rent, and population figures are from the August 2021 Census. Australia's housing markets and demographics have changed since then. Rankings are relative within the dataset — the direction of difference between suburbs is more reliable than the absolute numbers.
Not every state publishes crime at suburb level (see the safety breakdown above). NSW, VIC, QLD, SA and ACT release per-suburb figures; WA publishes at SA2, so the suburbs within an SA2 share its rate; the NT reports by town and region; Tasmania publishes only a single state-wide figure. Small suburbs everywhere inherit their wider SA2 rate. Where data is shared across an area, the individual suburbs inside it cannot be differentiated.
Areas whose entire surrounding district has fewer than 50 residents — national parks, water, industrial zones and uninhabited country — are left grey rather than guessed at. This is roughly 80 of more than 15,000 localities nationwide. A populated suburb is never grey just for having low crime.
The accessibility score is built from public-transport stop-location data: how many train stations and bus stops are near the suburb, and how close the nearest station is. It does not measure car/drive times, road congestion, or service frequency. States without published stop data are left unscored rather than guessed.
Amenity coverage is generally good but inconsistent. Some amenities — particularly in newer estates — may be missing or out of date. Liveability scores can undercount amenities in recently developed areas.
All lens scores involve subjective weighting decisions. A 40% weight on nearby rail stations for accessibility reflects our judgement, not an objective standard. We'll refine weights over time as we get feedback.
Federal election votes are tallied at polling places, not at suburb boundaries. We assign each booth to the suburb its coordinates fall within. Booths near suburb boundaries may draw voters from adjacent areas, so the figures are an approximation — more reliable for larger suburbs with several booths and less reliable for small or boundary suburbs. Electoral boundaries also changed between elections, so cross-year comparisons reflect consistent geographic boundaries (SALs), not consistent electorates.
The dashed projection line on crime charts is a linear regression on historical annual data — not a machine-learning model or expert forecast. It assumes the recent trend continues unchanged, which may not hold if policing, demographics, or land use changes significantly. Treat it as directional context, not a prediction.
The Queensland Police Service Online Crime Map API provides a rolling 5-year window of suburb-level data. No bulk historical suburb-level download is publicly available beyond this window. QLD safety scores and crime trend charts reflect 2022–2024 data only; pre-2022 trends cannot be shown for Queensland suburbs.
The NT Government Open Data Portal rolling file only covers 2023 onwards. NT safety scores are therefore based on 3 years of data (2023–2025), and crime trend charts for NT suburbs show this shorter window. Additionally, NT crime is reported at town and region level rather than individual suburb — figures are distributed across suburbs within each reporting area and carry higher estimation error than states with direct suburb-level data.
The Tasmania Police Crime Statistics Supplement PDFs report crime totals for Tasmania as a whole — no suburb, local government area, or SA2 breakdown is published. All Tasmanian suburbs therefore receive an identical within-state safety score. Scores do reflect Tasmania’s actual per-capita crime rate relative to other states, but cannot differentiate between individual Tasmanian suburbs.
Ready to explore the data?