accomodiMethodology & Data Sources
No black box: here's exactly how the 0-100 Neighborhood Viability Score is calculated.
The composite score
The score out of 100 is a weighted blend of three sub-scores, each out of 10: Financial Resilience (40%), Structural Quality (35%), and Social Dynamic (25%). Each sub-score is derived from several underlying open-data metrics, normalized onto a 0-10 scale.
Financial Resilience
Driven by the local WOZ (property value) growth rate versus the national average, and average income per inhabitant versus the national distribution, plus (once unlocked) median household wealth and the local low-income/high-income household split.
Structural Quality
Driven by the local energy label distribution (share of A/B vs. E/F/G rated homes) and the share of pre-1970 construction, a proxy for foundation and insulation risk.
Social Dynamic
Driven by the owner-occupied vs. rental housing split and population density relative to the surrounding city.
Livability & Proximity
A separate, informational set of metrics — not part of the 0-100 composite weighting — covering daily-life distances (supermarket, daycare, highway access, train stations), lifestyle density (cafés, restaurants, cinemas, theaters, museums), healthcare and education access, and recreation (libraries, swimming pools). These help you judge day-to-day livability independently of the macro score.
Safety & Nuisance
Also informational rather than part of the composite weighting: a 0-10 safety score derived from registered residential burglaries per 1,000 households and the share of registered crime that's nuisance-type (vandalism, disorder), sourced from Politie Open Data.
Viewing as Family or Investor
Switching the persona toggle (Standard, Family, Investor) re-weights the same three sub-scores differently — Family shifts weight toward Structural Quality, Investor shifts weight toward Financial Resilience — so the same postcode's composite score can shift depending on what matters most to you. The underlying data never changes, only the weighting.
Data sources
PC4 postcode statistics and boundaries come from PDOK's CBS Postcode4 OGC API (CC BY 4.0). Energy label distributions come from RVO's EP-Online registry. Safety figures come from Politie Open Data — CBS dataset 47018NED, registered crime by neighborhood (CC BY 4.0), matched to each postcode via its CBS neighborhood code. Nearby amenities come from OpenStreetMap contributors (ODbL). All are official or community open-data sources — nothing is scraped from commercial listing sites.
Limitations
CBS suppresses some figures for very small or sparsely populated postcodes to protect privacy; those fields fall back to a neutral value rather than being fabricated. Safety figures (burglaries, nuisance share) come from police-registered crime data (CBS dataset 47018NED) matched to each postcode's neighborhood, not a self-reported survey.
Three pillars, one composite score
WOZ asset appreciation metrics and localized buyer affordability tiers.
Building era tracking (pre-1970 construction risk flags) and local energy label efficiency spreads.
Population density balances, neighborhood turnover indexes, and crime/nuisance trend lines.
Where does this data come from?
We strictly use official, verified public open data provided by the Central Bureau for Statistics (CBS), the Cadaster (Kadaster), and the Dutch National Police (Politie), plus community-mapped amenity data from OpenStreetMap. No commercial bias, no scraped fluff. Just cold, hard civic analytics mapped to your prospective doorstep.
What data or feature is missing that would help you?
We read every response — your feedback directly shapes what we build next.