Location intelligence for insurance underwriting and risk.
Price risk with confidence using property, environmental and neighbourhood data resolved from any address — flood and natural context, building characteristics, demographics and energy.
Underwriting decisions hinge on what surrounds a property — water, land, building attributes, neighbourhood risk. That context is fragmented across incompatible datasets and rarely address-ready.
What Neighbourly gives insurance teams.
The specific, production-grade capabilities behind insurance on Homicity — not features on a slide, but data and tooling you can ship on.
Address-to-exposure resolution
Standardize and validate any Canadian address, geocode it to precise coordinates, then resolve it against the Environmental layer for nearby lakes, waterways, wildland and crown land. This turns a raw address field into a structured exposure profile in one pass.
Pre-computed environmental geometry
The Canada-specific Environmental layer stores lakes, aquatic resources, wildlife management units and public land as pre-computed geometry, so proximity and containment lookups are fast enough to run inline during quoting. You are not running heavy spatial operations at underwriting time.
Building characteristic enrichment
Pull commercial building characteristics including LEED and BOMA certifications and facade metadata, alongside MLS/CREA property attributes, to inform condition and construction assumptions. These attributes attach to the same address record used for exposure.
Batch portfolio scoring
Run an entire book of addresses through the same standardization, boundary and environmental pipeline to produce consistent attributes across every risk. Because the schema is identical for one address or a million, portfolio review and origination use the same fields.
Neighbourhood risk context
Layer Forward Sortation Area demographics and Neighbourhood Data onto each location to characterize the surrounding area for pricing models. This adds population and area context that a coordinate alone cannot supply.
Energy and efficiency signals
Attach verified Energy Data to inform property-condition and efficiency assumptions in risk models. These signals give underwriters a repeatable input rather than a manual inspection note.
How Insurance teams use Neighbourly.
Address-level risk context
Resolve any address to its boundaries, environmental features and building characteristics.
Environmental exposure
Lakes, waterways, wildland and public-land context a coordinate alone can't capture.
Portfolio enrichment
Batch-enrich books of business with consistent, standardized location attributes.
Energy & efficiency signals
Verified utility data for property-condition and risk models.
Why insurance teams choose Homicity.
Defensible, repeatable pricing
Every risk resolves to the same standardized attributes, so pricing decisions rest on a consistent, auditable location basis rather than analyst judgement.
Faster quote-to-bind
Exposure and building context resolve inline from an address, removing manual research that slows the underwriting path.
Portfolio-wide consistency
The same pipeline scores origination and in-force books, so new business and portfolio review speak the same data language.
Canadian residency by default
Data is hosted in Canadian data centres and PIPEDA-compliant, which simplifies the compliance conversation for regulated carriers.
Data layers for Insurance
The Neighbourly layers most relevant to this industry — all under one API key, one schema, Canada-wide.
Three ways to put Neighbourly to work for insurance.
Use the data yourself, have us build the whole thing, or bring in our team for strategy and analysis — whatever fits how you work.
Canadian data residency
Hosted in Canadian data centres, end to end.
PIPEDA-compliant
Privacy-first by default, with provincial compliance built in.
One consistent schema
Every layer shares a spine, so data joins cleanly.
Always current
Live, maintained data — never stale snapshots.
Insurance — common questions
How can insurers use location data for underwriting?
Resolve any address into standardized boundaries, environmental exposure, building characteristics and neighbourhood demographics to price and assess risk consistently across a portfolio.
Does Neighbourly cover environmental risk data?
Yes — a Canada-specific environmental layer covering lakes, aquatic resources, wildlife management units, crown land and trails, with pre-computed geometry for fast lookups.
What property and building characteristics are available?
Commercial building characteristics including LEED/BOMA certifications and façade metadata, plus MLS/CREA property attributes and standardized address data for any location.
Can I enrich an insurance portfolio in bulk?
Yes. Addresses can be enriched in batch with consistent boundaries, environmental exposure and demographic attributes for origination and ongoing portfolio review.
Is the data available at the address level?
Yes. Every address is standardized, validated and stitched to the full geographic hierarchy, so risk context resolves precisely to the property.
Is the data Canadian-hosted and privacy-compliant?
Yes — all data is hosted in Canadian data centres, PIPEDA-compliant by default, with provincial privacy compliance built into the architecture.
How do you handle addresses that do not geocode cleanly?
Addresses are normalized into street components with FSA and LDU postal data and stitched to the full geographic hierarchy, so even partial or messy inputs resolve to the best available boundary level rather than failing outright.
Can environmental exposure be delivered as flags rather than raw geometry?
Yes. You can consume raw GeoJSON for your own spatial engine, or work with our data services to receive pre-computed proximity and containment flags shaped for your rating model.
How do we integrate this into an existing rating engine?
The API returns consistent JSON over REST or GraphQL under one key, so exposure and building attributes feed directly into a rating call or a pre-quote enrichment step.
Does Neighbourly provide a risk score or the underlying data?
Neighbourly provides the underlying standardized attributes and geometry; your actuarial and underwriting teams keep control of scoring. If you want a tailored derived signal, that is something a custom build or data services engagement can deliver.
Bring Neighbourly to your insurance product.
Tell us what you're building and we'll map the right data layers and plan to your use case.