Boundaries, addresses, listings, permits, environment, demographics, and energy — all anchored to the same geopolitical hierarchy and queryable through a single API.
Six nested levels of Canadian geography, each with full MultiPolygon boundaries, bounding boxes, and centroids. This is the foundation — every listing, permit, business, and data point in Neighbourly resolves back to its place in the hierarchy.
Address is the leaf node of the hierarchy and the join point between your listings and everything else in Neighbourly. We normalize street components into lookup tables and denormalize the full hierarchy onto every record, so you get instant context with no spatial joins at runtime.
MLS and CREA-format listings, with ~80 lookup tables describing every property attribute that buyers filter on. Agent and office profiles are enriched through a live partner integration with Sutton Homebase and indexed for fuzzy name match and geo bounding box.
Each municipality publishes permits in its own format. Neighbourly normalizes them into a single schema with shared sources, types, statuses, and structure types — so you can search renovation activity, growth signals, and investment patterns the same way everywhere we cover.
A unique-in-the-market layer for Canada. Spatial data on aquatic resources, wildlife management, public lands, and recreation — with pre-computed boundary GeoJSON at three levels of detail and pre-joined hierarchy arrays for fast viewport queries.
Demographic data keyed by Forward Sortation Area, ready for buyer profiles, neighbourhood guides, and underwriting models. Computed-percentage helpers handle the math — homeownership rate, age distribution percentages, education and employment breakdowns — so you don't have to.
Buyers see square footage, bedrooms, lot size — but not what the home actually costs to run. Neighbourly's newest layer joins verified utility consumption data to listings, benchmarked locally, ready for the listing page.
Which elementary school does this address feed into? Which Catholic secondary? Which school board? Neighbourly's Schools layer resolves those questions at the coordinate level — with catchment polygons, board hierarchy, bilingual designation, and feeder links all pre-joined.
address_id FK — joins directly to listings, boundaries, and the Livability Score catchment dimension
Canadian business data is scattered across provincial corporate registries, municipal licence databases, and POI aggregators with inconsistent schemas. Neighbourly's Business layer normalizes it all into a single coordinate-queryable dataset — registry filings with NAICS sector codes, operating status, and density aggregates.
address_id FK — joins to listings, demographics, permits, and the Livability Score amenity dimension without extra reconciliation
The hierarchy, addresses, listings, and spatial layers live in PostGIS-enabled Postgres. Permits and legacy MLS records sit in dedicated databases, joined through shared keys.
PostGIS for precise spatial joins. Elasticsearch for viewport listings, fuzzy agent match, and geo_shape boundary queries. Meilisearch for typeahead and filterable permit search.
Everything ships through /api/v1/ — predictable, RESO-aligned, and designed to drop into MLS infrastructure, brokerage CRMs, and listing portals.
Book a 20-minute walkthrough. We'll show you the platform, demo the layers most relevant to your market, and discuss what a partnership could look like.
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