How pre-mover data works in Canada

Pre-mover data is a weekly record of households that have started the move process, flagged at the listing event rather than after the move is complete. The whole discipline is built around one simple fact: by the time a household physically moves, most of the commercial decisions around that move have already been made.

The Canadian pre-mover market is small and technical. A handful of serious providers, a larger handful of resellers, and very little neutral writing about how the signal is actually produced. This page is the technical explainer we wish existed when enterprise data teams started asking us what was under the hood.

The eight-step process

Pre-mover data is not new-mover data

The most common source of confusion in this category is the overlap with new-mover data. The two are sold side by side, often by the same vendors, and the difference looks cosmetic until a buyer tries to run a retention campaign on the wrong one.

Pre-mover data flags the household before the move, from the listing event. New-mover data flags the household after the move, from post-move records like change-of-address filings, utility hookups, or mail-forwarding registrations. The gap between the two is weeks long, and in most workflows that gap is the entire campaign window.

A retention team using new-mover data is doing post-mortem outreach. A retention team using pre-mover data is doing pre-emptive outreach. The two workflows look similar from the outside and diverge completely on results. Full comparison →

Three ways the signal gets captured

Not all pre-mover datasets are captured the same way. There are three methodologies in active use in Canada, and they produce data with different shapes, different latencies, and different failure modes.

Listing-based capture

The dominant methodology and the only one PreMovers uses. The household is flagged when its property enters the public listing market — for sale, for lease, or when withdrawn and re-listed. The approach is built around the listing lifecycle: every state change is observable and captured as the listing moves through it.

Strengths: earliest signal, highest specificity, strongest provenance. The household is flagged at the first public indication of an active move cycle and remains tracked through the full sale lifecycle.

Weaknesses: requires continuous capture infrastructure and years of accumulated operational history. A pipeline that hasn't been running long enough can't produce clean historical depth, and no amount of back-acquisition fills that gap.

Mobility-based capture

Inferred movement from aggregated mobile-device patterns. The household is flagged when device-level signals indicate a residential move.

Strengths: captures moves that never reach the listing market (rentals, family moves, corporate relocations). Useful for demographic-level migration analysis.

Weaknesses: the signal is laggy and the spatial resolution is coarser than property-level capture. Suitable for planning and territory analysis. Not suitable for household-level retention or acquisition workflows.

Post-move registration capture

The traditional "new-mover" methodology. The household is flagged from post-move records: change-of-address filings, credit-bureau updates, utility hookups. Technically this is new-mover data, but some vendors bundle it into their pre-mover product line — which is where buyer confusion starts.

Strengths: easy to source, legally well-understood, familiar to direct-mail infrastructure.

Weaknesses: arrives weeks after the move. The commercial window is already closed by the time the data is delivered.

How fast the signal arrives

Refresh cadence is the single number that most directly determines the commercial value of a pre-mover dataset. Two datasets can be drawn from the same listing source and have very different usefulness depending on how often the pipeline refreshes.

Weekly refresh is the current standard for serious enterprise use, and what PreMovers delivers. A vendor who reports "regularly updated" or "rolling cadence" without naming a specific interval is usually delivering monthly data. The specific cadence is not a hard question to answer, and vagueness about it is almost always informative.

What a production pipeline actually does

Enterprise buyers sometimes assume pre-mover data is pulled from a feed and packaged. It isn't. A production pipeline runs continuously every week of the year, across interconnected operations that each have to work correctly for the output to be usable. If any one of them fails quietly, the data looks fine on delivery and starts drifting from live market conditions within weeks.

The operations span source handling, address-level normalization, property identity resolution, lifecycle state management, and deduplication. Each of those is a meaningful engineering problem in isolation. Running all of them together, week after week, for enough years to produce reliable historical depth, is what separates vendors who operate the pipeline from vendors who acquired a snapshot and are selling slices.

This is why pipeline continuity is worth asking every vendor about. Twelve years of weekly capture only comes from running the pipeline for twelve years, and buying old snapshots later does not fill the gap cleanly.

How the data reaches the customer

The same underlying dataset reaches customers four ways. The shape matters because it determines what the buyer's team has to do to activate it.

Pre-mover mailing lists

The simplest shape. PreMovers.ca sells pre-mover lists for any Canadian province, city, postal area or radius: pick the area, see the count, download a CSV ready for a printer or Canada Post. Standard lists are $150 per 1,000 homes, Plus lists with the home details $250 per 1,000, minimum order $75 (500 homes). Every address passes a Canada Post check before it is sold. Counts by city and province →

Snowflake Marketplace

A live data share inside the buyer's own Snowflake environment. No file transfers, no ETL, no scheduled jobs to maintain. The data appears as a queryable table that can be joined directly to the buyer's CRM or subscriber database with SQL. Updated weekly automatically. This is the modern enterprise default.

MCP connector

An AI-native access point. AI agents — Claude, ChatGPT, custom enterprise agents — can query pre-mover data directly inside a conversation or an automated workflow. Useful for teams building AI-driven retention and acquisition systems, or for analysts who want natural-language access to the underlying data without writing SQL.

Flat file

A weekly CSV or Parquet file delivered via SFTP or cloud bucket. The traditional shape. Compatible with any CRM, any campaign platform, any direct-mail system. Still the right choice for many marketing operations workflows that don't need to live inside a data warehouse.

Privacy posture

Pre-mover data is property-level, not person-level. A record describes a property and a listing event — address, postal code, listing date, status, attributes, price history. No names, phone numbers, email addresses, or demographic information.

The customer identification happens when the buyer matches the file against their own customer records on address — inside their own systems, under their own privacy framework (PIPEDA in most cases, plus any sector-specific obligations the buyer carries). The file PreMovers sends has no names, phone numbers or email addresses.

This is the cleanest posture for the category. It also makes pre-mover data easier to integrate into privacy-conscious workflows than data sources that include person-level attributes — there are simply fewer privacy questions to answer.