Property and collateral intelligence for lenders.
Support origination and portfolio monitoring with automated property context — valuations context, collateral characteristics, demographics and market signals from one API.
Lending decisions need a defensible view of the property and its market. Assembling collateral, demographic and market context per address slows origination and adds risk.
What Neighbourly gives banking teams.
The specific, production-grade capabilities behind banking on Homicity — not features on a slide, but data and tooling you can ship on.
Collateral attribute assembly
Resolve a subject address into standardized components, boundary hierarchy, property characteristics and neighbourhood context in a single call, giving a loan file a complete collateral picture. The result is structured JSON ready to attach to a decision record.
Market signal enrichment
Layer Forward Sortation Area demographics, Building Permits activity and Real Estate market context onto a property to inform valuation and credit models. Permit activity in particular acts as a forward-looking signal of neighbourhood investment.
Batch portfolio re-enrichment
Run an existing book of collateral addresses through the same enrichment pipeline on a schedule to detect shifts in market and neighbourhood conditions. Every re-run uses the identical schema, so period-over-period comparison is clean.
Decision-ready structured output
Attributes are returned as consistent JSON keyed to the standardized address, so they feed straight into automated decisioning without manual reconciliation. This keeps origination workflows fast and auditable.
Boundary-anchored comparability
Because every address is stitched to the same Country to Neighbourhood hierarchy, collateral in different cities can be compared on a consistent geographic basis. This underpins concentration and exposure analysis across the book.
Address validation at intake
Validate and normalize borrower and property addresses at application time to reduce downstream errors and duplicate collateral records. Clean intake data improves every model that consumes it later.
How Banking teams use Neighbourly.
Collateral context
Property characteristics, boundaries and neighbourhood data for every address you underwrite.
Market signals
Demographics, permit activity and neighbourhood trends for lending models.
Portfolio monitoring
Batch-enrich and re-check portfolios against current location data.
Automated decisioning
Feed clean, structured location attributes straight into credit workflows.
Why banking teams choose Homicity.
Defensible collateral view
A consistent, standardized set of location attributes gives credit and audit teams a view of collateral they can stand behind.
Shorter origination cycles
Property and market context resolves automatically per address, removing manual research from the origination path.
Continuous portfolio insight
Scheduled re-enrichment surfaces changing market conditions across the book without manual data pulls.
Regulator-friendly residency
Canadian hosting and PIPEDA compliance align with the data-governance expectations of federally and provincially regulated lenders.
Data layers for Banking
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 banking.
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.
Banking & Lending — common questions
What data helps lenders assess property collateral?
Standardized address and boundary data, property characteristics, neighbourhood demographics and permit activity together give lenders a defensible, consistent view of collateral and its market.
Can Neighbourly enrich a lending portfolio in bulk?
Yes — addresses can be enriched in batch with consistent location attributes for origination and ongoing portfolio monitoring.
What market signals are available for lending models?
Neighbourhood demographics, building-permit activity and market trends provide forward-looking signals to feed valuation and credit models.
Can I integrate location data into origination workflows?
Yes. Clean, structured location attributes are returned as consistent JSON, ready to feed directly into automated decisioning and origination systems.
Does it support portfolio monitoring over time?
Yes — portfolios can be re-enriched against current data to monitor changes in market and neighbourhood conditions.
Which data layers are most relevant for lending?
Address & Geocoding, Demographics, Building Permits, Location Boundaries and Real Estate are the core layers for collateral and market context.
Can we enrich at application time and again during servicing?
Yes. The same address-based enrichment runs at origination for a real-time collateral view and on a batch schedule during servicing, using an identical schema so results are directly comparable over time.
How does this fit alongside our appraisal and valuation providers?
Neighbourly supplies the standardized location and market context around collateral rather than a formal valuation, so it complements appraisal by giving models consistent neighbourhood, demographic and permit inputs.
What does integration into a decisioning system involve?
You call REST or GraphQL with one API key and receive consistent JSON, so most teams add a single enrichment step ahead of their credit decision without changing their core workflow.
Can Homicity deliver enriched collateral data as a dataset instead?
Yes. If you prefer not to call the API in-line, our data services can enrich a portfolio file and return it in your schema, or a custom build can wrap the logic your credit team needs.
Bring Neighbourly to your banking product.
Tell us what you're building and we'll map the right data layers and plan to your use case.