How AreaIQ Scores Areas
Every score is computed from real data using transparent, deterministic formulas. Same postcode, same data, same score, every time.
Data Sources
Every report is built from 7 live UK government and open data sources, fetched in parallel at the time of request. No cached data. No estimates. No surveys.
Street-level crime incidents from the last 3 months, broken down by category (theft, violence, burglary, etc.). Includes monthly trend data for direction-of-travel analysis.
Index of Multiple Deprivation. Ranks 33,755 Lower Super Output Areas across income, employment, health, education, and living environment. Decile 1 = most deprived, decile 10 = least deprived.
Nearby amenities: schools within 1.5km, food and shops within 1km, transport stations within 2km, bus stops within 500m, parks and healthcare facilities.
Flood risk zones within 3km, active flood warnings within 5km, and identified rivers at risk. Data is fetched live per request.
Geocoding (latitude/longitude), LSOA code and name, local authority, ward, constituency, and region. Acts as the entry point for all other lookups.
Actual sold prices from the last 12 months via SPARQL query. Median and mean prices, year-on-year change, property type breakdown (detached, semi, terraced, flat), tenure split, and price range.
School inspection ratings (Outstanding, Good, Requires Improvement, Inadequate). England only.
Intent Types & Dimension Weights
The intent determines which dimensions are scored and how they are weighted. Different use cases care about different things. Moving prioritises safety and schools. Business prioritises foot traffic and spending power. Weights are calibrated internally and are not published.
moving· Residential relocationbusiness· Commercial viabilityinvesting· Property investmentresearch· General area profileHow Each Score Is Calculated
Each dimension has a dedicated scoring function. Inputs go in, a number between 0 and 100 comes out. No randomness. No AI-generated numbers. Below is a plain-English breakdown of every function.
Analyses 3 months of police.uk crime data using a non-linear curve that penalises high-crime areas more sharply. Adjusts for violent crime concentration and month-on-month trends. Areas with rising crime are penalised; areas with falling crime are rewarded.
Measures rail/tube stations and bus stops with diminishing returns for each additional station. Combines heavy rail connectivity with local bus coverage into a single accessibility score. Benchmarked against area type (urban, suburban, rural).
Counts schools and educational facilities nearby using a diminishing returns curve. Having at least one good school matters more than having many. Benchmarks adjust for area type so rural areas are not penalised for fewer institutions.
A weighted composite across five categories: education, food and drink, healthcare, retail, and green spaces. Each category is normalised against area-type benchmarks and combined into a single convenience score.
Derived from government deprivation indices (IMD for England, WIMD for Wales, SIMD for Scotland). Maps the official decile ranking to a score reflecting the socioeconomic profile of the neighbourhood, including the LSOA's national rank and percentile position.
Combines flood risk zones, active flood warnings, and green space availability. Areas with no flood risk and good park access score highest. Active warnings have significant negative impact. Data from the Environment Agency and OpenStreetMap.
Uses Land Registry sold prices as the primary input. When price data is available, scores are based on the ratio of local median prices to the national median. Falls back to deprivation data as a proxy when transaction data is unavailable.
Business reports use derived scores that combine transport, amenity, and deprivation data into commercially relevant metrics.
Combines transport connectivity with commercial activity density. Areas with strong rail, bus, and retail presence indicate higher natural footfall. Both components are weighted and capped to prevent outliers.
Measures commercial saturation by counting food, drink, and retail venues nearby, then inverts the count. Lower competition density scores higher. Useful for identifying underserved areas with unmet demand.
Derived from deprivation indices as a proxy for local disposable income. Less deprived areas indicate higher average spending power. Correlates with footfall quality, not just volume.
Uses Land Registry property values as a proxy for commercial rents and overheads. Higher local property prices correlate with higher commercial costs, resulting in a lower score. Falls back to deprivation data when price data is unavailable.
Investing reports combine deprivation data, transport connectivity, crime statistics, and flood risk into investment-focused metrics.
When Land Registry data is available, scores are based on real year-on-year price changes. Moderate growth scores highest; sharp declines and flat markets score lower. Falls back to deprivation-based growth potential estimates when transaction data is unavailable.
Uses Land Registry median prices as the yield denominator. Lower property values indicate higher potential gross yields. Adjusts upward for strong local amenities and transport that drive tenant demand. Falls back to deprivation data when price data is unavailable.
Scores areas by development potential. Higher-deprivation areas with good transport links score highest, indicating opportunity for uplift. Already-developed premium areas score lower. Infrastructure investment is a key multiplier.
A composite of transport connectivity, local amenities, bus coverage, and commercial activity. Areas with strong infrastructure and varied amenities attract more tenants, driving occupancy and reducing void periods.
Combines crime and environmental risk into a single downside metric. Low crime and no flood risk produce a high score. Areas with active flood warnings or elevated crime see significant score reductions.
What AI Does (and Does Not Do)
7 APIs queried in parallel for the target location
Deterministic functions produce locked 0-100 scores
AI Engine receives locked scores + raw data, writes the report
Server overwrites any AI deviation with computed values
Even if the AI model returns different numbers in its response, the server replaces them with the pre-computed scores before saving the report. The numbers you see are always the output of the deterministic scoring engine.
Overall Score
The AreaIQ overall score is a weighted average of all dimension scores for the selected intent. Each dimension contributes proportionally to its internally calibrated weight. The result is a single 0-100 number representing how well the area suits your stated purpose.
Score Scale
Scores are colour-coded using a RAG (Red, Amber, Green) system across every report. This applies to both dimension scores and the overall AreaIQ score.
The area performs well in this dimension. A strong foundation with no major concerns. For overall scores, this indicates a highly suitable location for your stated intent.
The area is adequate but has room for improvement. Some trade-offs to consider. Worth investigating further before making decisions.
The area underperforms in this dimension. Significant challenges identified. Does not necessarily disqualify the area, but indicates a specific weakness worth understanding.
A low score in one dimension does not make an area unsuitable. Context matters. A business location with a low competition score (meaning heavy saturation) might still succeed with strong differentiation. Read the narrative sections alongside the numbers.
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