Address Standardization

Your Canadian address data — clean, canonical, and consistent.

Messy listing feeds and fragmented CRM data cost real estate teams hours every week. The Neighbourly address standardization service normalizes every Canadian address to a shared schema — no code required.

Managed service Feed ingestion Bulk normalization API available Canadian data

Talk to us → Explore the API

Input (as received) Standardized output

147 lakeshore rd e, miss on

147 Lakeshore Road East, Mississauga, ON · L5G 1E1

1205-88 Scott st toronto

1205 – 88 Scott Street, Toronto, ON · M5E 0A8

99 maple cres, Van BC V6B

99 Maple Crescent, Vancouver, BC · V6B 2A1

450 boul rene-levesque o montreal

450 Boulevard René-Lévesque Ouest, Montréal, QC · H2Z 1A8

The problem

Real estate address data is inherently inconsistent.

Agents type addresses freehand. Boards ingest from disparate sources. CRMs store whatever was submitted first. The result is the same address stored five different ways, in five different records, across the same system.

Without standardization

Addresses that can't be trusted

"147 lakeshore rd e" and "147 Lakeshore Road East" treated as different properties
Geo-queries return incomplete results because coordinates are missing or wrong
Duplicate leads, listings, and CRM contacts pile up
Property history can't be assembled because join keys don't match
Neighbourhood-level analytics are unreliable
Manual review consumes hours every week

With Neighbourly standardization

One canonical address record

Every address resolves to the same canonical ID — regardless of how it was entered
Full coordinates, FSA, and LDU attached to every record
Unit and suite numbers extracted and normalized separately
Duplicate records are easy to detect via shared address_id
Property history, permits, and energy data all link reliably
No manual review for standard Canadian formats

Two paths to standardization

Use the API, or let us run the pipeline.

Developer teams can call the address API directly. Non-technical teams — or anyone who wants ongoing normalization without infrastructure — use the managed service.

Option A · REST API

Integrate directly

Call the /v1/address endpoint from your application, ingestion pipeline, or CRM webhook. Responses include all normalized components and geographic context.

Get an API key from your Neighbourly dashboard
POST or GET any address string to /v1/address
Receive structured, normalized JSON
Store the canonical address_id as your join key

Option B · Managed service

We run the normalization

Send us your listing feed, CRM export, or property database. We normalize it against the Neighbourly address layer and return a standardized CSV or JSON file — or pipe the results back to your system.

Share your data file or feed endpoint
We map your schema to the Neighbourly address schema
Normalization runs on a schedule you define
Receive clean, enriched output — no integration code needed

What gets normalized

Every component of a Canadian address, resolved.

The Neighbourly address layer is built specifically for Canadian real estate data — not a generic geocoder repurposed for the market.

🏷

Street-type normalization

Resolves ~100 common street-type synonyms and abbreviations — "rd", "road", "Rd.", "Route" all map to a single canonical suffix. Directional variants (E, East, est) are expanded and standardized.

📮

Postal code resolution

Full six-character postal code (LDU) is assigned when available. FSA (Forward Sortation Area) is always returned. Both are validated against Canada Post address data.

🇨🇦

French-language support

Quebec and francophone municipality naming conventions are handled natively. Accented characters are preserved and normalized. Boulevard, Rue, Avenue, and Chemin are all recognized.

Output schema

Structured output, ready to use.

Standardized addresses are returned as structured JSON with discrete fields for every component. Store the address_id as a foreign key to unlock every other Neighbourly data layer.

address_id acts as a persistent join key across
all Neighbourly data layers — permits, demographics,
environment, and energy are all keyed to it.

Full API reference → canadian-address-api.html

Standardized address record normalized

{
  "address_id":    8421,
  "full_addr":     "147 Lakeshore Road East,
                   Mississauga, ON",
  "number":        147,
  "street":        "Lakeshore",
  "suffix":        "Road",
  "direction":     "East",
  "unit":          null,
  "fsa":           "L5G",
  "ldu":           "L5G 1E1",
  "neighbourhood": "Port Credit",
  "city":          "Mississauga",
  "province":      "Ontario",
  "lat": 43.5513,   "lng": -79.5817
}

Who uses it

Standardization for every stage of the real estate data lifecycle.

MLS Boards

Clean addresses at listing ingestion

Run every new listing through the standardization layer before writing to your database. Catch format errors, fill missing postal codes, and keep your address dataset consistent as it grows.

Brokerages

Normalize your CRM property records

One-time bulk normalization of your existing CRM or BOS data. Then keep it clean with an ongoing webhook that standardizes each new property record at creation.

Proptech

Reliable join keys for analytics

Standardized address IDs let you join listing data, permit history, demographic profiles, and energy records without manual key reconciliation. Build property timelines and neighbourhood dashboards on clean data.