A little bird told us.
Methodology
How nestfinder scores postcodes — the weights behind the ranking.
What we score
For every Australian postcode in our index, we compute a per-feature score — commute time, school fees fit, bedroom/bathroom/carpark fit, crime rates, amenity density, school performance, and the rest. Each of those features is percent-ranked across all postcodes — a cycle-commute score of 0.85 means "closer to your target than 85% of postcodes". Percent-ranking keeps the numbers comparable: a postcode in regional Tasmania and one in inner Melbourne can both have a school-performance score of 0.7 and it means the same thing relative to the national distribution.
We surface five rolled-up dimension scores (transport, schools, crime, affluence, amenities) on the heatmap so the map can paint along one axis at a time. Those rolled-up scores are unweighted equal-weight means of their feature components — they exist for the map view, not the ranking. The actual rank uses the per-feature weights you set.
The formula
Your total score for a postcode is the weighted sum of the percent-rank-normalised feature scores against the weights on your profile. Each slider in the wizard maps directly to one term in the sum:
total_score = sum over features f of ( preference[f] * percent_rank(postcode[f]) ) # preference[f] is your slider value in [0.1, 1.0] # percent_rank(...) is in [0, 1] with higher = better-for-you # (for things like crime rates, lower raw values score higher # so the polarity stays consistent across all features)
There are 21 atomic preference sliders — one per feature — and a small set of unweighted budget + school-fees fit terms. Total scores are not normalised to [0, 1]; they're a raw weighted sum, so what matters is the relative ranking between postcodes, not the absolute value.
Heads up: the heatmap dimension switcher shows five rolled-up scores — transport, schools, crime, affluence, amenities — which are equal-weight means of their component features. Those means are not combined back into the total score the ranking uses. If you've seen a "40% transport, 20% schools, …" pitch elsewhere, that's a different model.
Default weights
The wizard lets you set each weight yourself between 0.1 and 1.0. If you'd rather start from a sensible baseline, here are the out-of-the-box defaults — one per feature:
| Group | Feature | Default | Notes |
|---|---|---|---|
| Housing | Bedrooms match | 0.50 | How close postcode median bedroom counts are to your requirement. |
| Housing | Bathrooms match | 0.50 | How close postcode median bathroom counts are to your requirement. |
| Housing | Carparks match | 0.50 | How close postcode median carpark counts are to your requirement. |
| Transport | Cycle commute | 1.00 | How close postcode cycle commute time is to your target. Weighted highest by default — adjust to match how you actually get to work. |
| Transport | Public-transport commute | 0.75 | How close postcode public-transport commute time is to your target. |
| Transport | Drive commute | 0.50 | How close postcode drive commute time is to your target. |
| Crime + community | Low crime | 0.50 | Break-ins, motor-vehicle theft, and violent crime rates (per capita). |
| Crime + community | Low renter share | 0.50 | Share of dwellings rented vs owner-occupied. Higher weight → more owner-occupier-leaning postcodes. |
| Crime + community | High affluence | 0.50 | ABS census signals (income, unemployment, qualifications) aggregated to the postcode. |
| Crime + community | Coastal proximity | 0.50 | Straight-line distance from the postcode to the nearest coastline (Geoscience Australia coastline data). A one-off measurement, not every postcode has been measured yet — unmeasured postcodes score neutrally. |
| Amenities | Childcare access | 0.50 | Density of childcare facilities in the postcode catchment. |
| Amenities | Fitness amenities | 0.50 | Density of gyms, pools, and sport facilities. |
| Amenities | Health amenities | 0.50 | Density of GPs, clinics, and pharmacies. |
| Amenities | Hospitality | 0.50 | Density of cafes, restaurants, and bars. |
| Amenities | Parks + green space | 0.50 | Density of parks and reserves. |
| Amenities | Retail | 0.50 | Density of shops, supermarkets, and retail strips. |
| Schools | School performance | 0.50 | NAPLAN bands averaged across the schools in the postcode (ACARA). |
| Schools | Student-teacher ratio | 0.50 | Smaller class sizes score higher. Sourced from myschool.edu.au. |
| Schools | School capex | 0.50 | Recent capital investment per school. Sourced from myschool.edu.au. |
| Character | Period character homes | 0.50 | Share of a postcode's LLM-tagged listings built in a period/heritage style (Victorian, Federation, Art Deco, etc). Derived from listing descriptions — not every postcode has enough tagged listings to speak to this. |
| Character | Renovated homes | 0.50 | Share of a postcode's LLM-tagged listings described as renovated. Same listing-description-derived signal as period character above. |
Swipe to see more →
You can tune any of these in your preferences profile.
Intent-aware affordability
The affordability signal depends on what you're trying to do — buying, renting, or investing.
- Buying a home (PPOR): average sale price (mean across recent listings in the postcode), scored against your budget.
- Renting: median advertised weekly rent.
- Investing: approximate gross yield (median weekly rent × 52 ÷ avg sale price).
Sale-price and weekly-rent inputs come from public real-estate listing data, refreshed monthly.
Data currency
Numbers are only as good as the date they were pulled. Census figures (which feed the affluence score) are a fixed 2021 release. Crime, schools, and NBN data are refreshed on an ongoing basis from their upstream sources, but we don't have a precise "as at" date for those three yet — we say so rather than guess.
Honest limits
See also about.