Every listings site shows you the house. Most of them will even give you a rough school catchment and a bus stop. Fewer than a handful score the area, and the ones that do score it the same way for everyone: buyer, investor, or business owner reading the same number.
Paid data tools existed, but they were wholesale. Spreadsheets the size of a phone book, priced for consultancies, without any effort to help a reader reach a decision.
One place where a postcode goes in and a proper read comes out. Scored against what the reader is actually deciding, citing the data it used, and written the way a friend would explain it.
A score a buyer could trust. A score a developer could embed. Same postcode, same answer every time, even when four different people ask for four different reasons.
Every score shows the data behind it. If a neighbourhood scores 72 for safety, the report tells you which crimes, over which months, and how the rate compares to other urban areas. Nothing important is hidden behind a footnote.
Scores come from public data using the same formulas every time. Two readers querying the same postcode for the same intent see the same number. The narrative explains the number. It never invents it.
A great area to move to isn't the same as a great area to open a coffee shop. We rebalance the five dimensions per intent. Safety and schools weigh heaviest when you're moving, footfall and spending power when you're trading.
A village with one school is not the same as a city with one school. We classify every postcode as urban, suburban, or rural and benchmark it against its own category. No unfair comparisons between a London high street and a Lake District lane.
Every fact in a report comes from one of the seven. Each response carries a data_freshness block so you can see the source and age of each datapoint.
The front door. Every postcode resolved to coordinates, LSOA, and MSOA so every downstream query knows exactly where it's looking.
12 months of incidents by category and street, used to score safety per intent and cite specific figures in the narrative.
The Ministry of Housing, Communities and Local Government's 2025 release for England, with WIMD (Wales) and SIMD (Scotland) for full UK coverage.
Schools, GP surgeries, shops, cafés, parks, bus stops, and train stations within 0.5–2km radii. Volunteer-maintained, surprisingly current.
Real transactions. Median sold price, year-on-year change, transaction counts, and property-type breakdowns. No asking prices, no estate-agent optimism.
Flood-zone classification and live flood-warning data. Lives in the Environment & Quality dimension, surfaced as a citation when the zone is 2 or 3.
Inspection ratings seeded locally, queried by coordinates for schools within 1.5km. Scotland (Education Scotland) and Wales (Estyn) planned.
Ran into the same question for the fourth time in a row: is this area any good? Checked every tool available. Answer: not really, not for this intent.
Seven public datasets wired in parallel. Narrative generated from the numbers, not from thin air.
Swapped AI-generated scores for deterministic formulas. The narrative stayed AI-written, but the numbers became reproducible.
Went live at area-iq.co.uk. Stripe checkout, API keys, Ofsted integration, watchlist. The lot.
OneGoodArea rebrand. Cleaner type system, editorial voice, the engine finally gets a page that does it justice.
I kept running into the same problem: trying to make location decisions without reliable, structured data. Rightmove gives you vibes, PropertyData gives you spreadsheets, nothing gave you scored, transparent, intent-driven intelligence at a reader's price point.
OneGoodArea is the tool I wanted on the other side of those decisions. Every feature exists because it solves a problem I had myself. No vanity metrics, no filler.
Make area intelligence accessible, transparent, and useful for every UK location decision.