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Custom Canadian Property Data

Custom Canadian property data. Built to your specification.

Off-the-shelf data products work for many buyers. For everyone else, BrightCat builds custom datasets — your fields, your filters, your delivery, your refresh cadence. Starting from 12 years of continuous Canadian property data, we shape the exact view your team needs and license it the way your procurement requires.

Scope a custom dataset See standard datasets
When custom data makes sense

Off-the-shelf doesn't fit every team.

Standard BrightCat datasets cover most use cases. When a team needs something different — a specific blend of fields, an enriched signal that isn't a published product, a regional slice, or a workflow-specific delivery — the same pipeline produces a custom dataset shaped to fit.

Specific geographies

A single city, an FSA-bounded territory, a regional bank's lending footprint, an insurer's catastrophe zone. Custom geographic cuts at any level — postal code, FSA, city, region, province, or polygon-defined boundary.

Derived signals

Distressed-seller flags, renovation indicators, dual-listing detection, time-on-market patterns, investor-property markers. Signals built on raw lifecycle data, calibrated to your detection logic.

Custom field selection

Only the fields you need, in the structure your system expects. Custom schemas, custom column names, custom NULL handling — fit to your ingestion pipeline.

Custom refresh cadence

Weekly is the default. Some buyers want daily incremental deltas; others want monthly aggregated snapshots. The underlying pipeline runs weekly — delivery shapes around your workflow.

Specific use cases

AVM training, mortgage portfolio monitoring, telecom retention, insurance underwriting, CRE underwriting, government housing analysis. Custom datasets sized and shaped to the use case.

Procurement-specific licensing

MDLA terms tailored to AI/ML training rights, multi-year commitments, sub-licensing for sub-brands, or regulated-industry compliance. Custom contracts for custom data.

The process

From spec to production in four to six weeks.

Week 01

Scope

Use-case interview, field requirements, geography, delivery shape, license terms. Written scope document by end of week.

Week 02

Sample

Representative sample matching your scope, delivered at no cost. Test against your CRM, your model, your campaign infrastructure.

Weeks 03-04

Refine

Iterate on the sample. Adjust fields, filters, lifecycle rules, or scope based on your validation results. Lock the final spec.

Weeks 05-06

Production

First production delivery. MDLA executed. Recurring delivery scheduled. Direct line to your data-engineering contact.

Examples

Common custom dataset patterns

Regional mortgage portfolio monitoring feed

A schedule-I bank wants weekly monitoring on residential properties backing its mortgage book within Ontario and BC. Custom feed: filtered to those two provinces, including only the 12 fields the bank's collateral system ingests, with custom lifecycle triggers for revaluation events.

Telecom pre-mover acquisition list, weekly delivery

A national telecom wants pre-mover signals filtered to their service territories with 14 days of advance notice on the move event. Custom feed: pre-mover signals only, region-filtered, delivered every Sunday for Monday campaign launch.

Insurance underwriting risk feed, claim-validation overlay

A P&C insurer wants vacancy detection, renovation flags, and investor-property indicators for active policies. Custom feed: lifecycle-event-driven, address-matched to the insurer's policy book, delivered on policy renewal cycles.

PropTech enrichment API, AI/ML training rights

A Canadian PropTech platform wants to enrich properties on its consumer-facing app with sold history, listing lifecycle, and AVM features. Custom feed: full Canadian coverage, delivered via API and Snowflake share, with explicit AI/ML training rights in the MDLA.

CRE investment analytics, asset-class-specific

A commercial real estate investment manager wants industrial-only commercial data with dual-listing detection across all 10 provinces. Custom feed: industrial sub-class only, dual-listing signals, weekly refresh, two-year MDLA term.

Common questions

About custom Canadian property data

What is custom Canadian property data?

A property dataset built to your specification — the fields you need, the geography you care about, the filters that match your use case. BrightCat starts with its 12-year continuous pipeline and shapes a custom view rather than handing over a generic export.

What can be customized?

Region, property type, lifecycle stage, fields, filters, delivery format, refresh cadence, and license terms. Almost every dimension of the data product can be tailored. Common customizations include geographic slicing, asset-class filtering, lifecycle event triggers, and custom field selection.

How long does it take to build custom property data?

A typical custom dataset is scoped within a week, sampled within two weeks, and in production delivery within four to six weeks. Simple region or filter customizations are faster; complex enrichment or new field derivations take longer.

How is custom data licensed?

Custom datasets are licensed under the BrightCat Master Data License Agreement (MDLA) with terms calibrated to the use case. Standard terms include defined AI/ML training rights, multi-year licenses with renewal options, and use scope tailored to the client.

Can custom data be delivered via Snowflake or MCP?

Yes. Custom datasets are delivered via Snowflake Marketplace as a Secure Data Share, via the BrightCat MCP connector for AI workflows, via developer API, or as flat files — whichever fits your existing infrastructure.

What's the minimum commitment?

Custom datasets are typically licensed under 12-month, 24-month, or 36-month MDLA terms with appropriate volume and use-case scoping. Shorter-term custom engagements are available for proof-of-value and discovery work.

Build a custom dataset

Tell us what you need. We'll scope, sample, and deliver.

Every custom engagement starts with a use-case interview and a representative sample at no cost.

Scope a custom dataset See standard datasets