Pre-mover data identifies households that are about to move. The signal comes from observed real estate listing activity — not predictions, not models, not change-of-address filings after the fact. When a property is listed for sale, the household at that address is entering a move cycle. Pre-mover data captures that moment.
Every residential property transaction starts the same way: someone lists a home for sale. That listing is a signal. The household at that address is about to change — they will need new insurance, new telecom services, new banking relationships, new utility accounts, and new local services. Every recurring relationship that household has is about to be re-evaluated.
Pre-mover data captures that listing event at the property level and delivers it as a structured file — typically weekly — to enterprise teams who act on move-cycle timing. The data includes the address, the listing date, the property status, and in more sophisticated pipelines, the full lifecycle: price changes, relists, days on market, and eventual sold events.
The critical distinction: pre-mover data is derived from observed activity in the real estate market, not from statistical models that estimate who might move. A property listed for sale is not a prediction. It is a fact.
The Canadian market has two categories of mover data, and they serve fundamentally different timing windows.
Pre-mover data flags the household before the move happens. The signal is the listing itself — captured the week it appears. This gives enterprise teams a four-to-eight-week window before the move is complete. During this window, the household is actively making decisions about which providers to keep, switch, or cancel.
New-mover data flags the household after the move has already happened. The signal comes from change-of-address filings with Canada Post, utility hookups at the new address, or post-sale registrations in land registry systems. By the time new-mover data arrives, the household has already chosen new providers for most services.
The difference is four to eight weeks. For a telecom company trying to acquire a customer before a competitor's retention offer lands, or an insurance carrier trying to quote a replacement policy before the old one lapses, those weeks are the entire value proposition.
Telecommunications. Canada's largest telecoms have used pre-mover signals for over a decade to acquire customers at the exact moment they are switching providers. When a household lists their home, they are about to move to a new address where they will need internet, mobile, and television service. The first provider to reach that household wins.
Insurance. When a household moves, their home insurance policy needs to be updated or replaced. Pre-mover data gives insurance carriers and brokers a quoting window before the existing policy lapses — enabling replacement policy offers timed to the actual move, not months after.
Banking and lending. A listed property often signals an upcoming mortgage discharge. Pre-mover data flags that discharge event weeks before it appears in registry systems, giving lenders a retention window for existing borrowers and an acquisition window for new ones.
Direct marketing. Home movers spend more in the first six months after a move than at any other point. Pre-mover data enables direct mail and digital campaigns timed to the listing event rather than the move-in event — reaching the household when they are actively researching local services.
The quality of pre-mover data depends entirely on the capture pipeline behind it. Three things matter.
Observed vs modelled. Pre-mover data based on observed listing activity is a factual signal: a property was listed for sale this week. Pre-mover data based on demographic models is a probability estimate: this household has characteristics that correlate with moving. The observed signal is stronger because it reflects what is actually happening in the market, not what might happen.
Cadence. Weekly capture means the signal arrives within days of the listing event. Monthly or quarterly capture means the signal may arrive after the move has already happened — which defeats the purpose. The cadence of the underlying pipeline determines whether the data is pre-mover or just late-mover.
Continuity. A pipeline that has been running for twelve years has seen every seasonal pattern, every market correction, every data format change, and every edge case in the Canadian real estate market. A pipeline that started two years ago is still learning. Continuity is what separates a production-grade pre-mover signal from an experiment.
BrightCat Data operates the longest-running continuous pre-mover data pipeline in Canada. Weekly capture across all 10 provinces since 2014 — over twelve years of uninterrupted weekly delivery. The signal is derived from observed listing activity, not modelled demographics. Every week, the pipeline captures new listings, price changes, status transitions, and sold events at the property level, and delivers structured pre-mover files to enterprise clients in telecommunications, insurance, banking, and direct marketing.
The pipeline tracks 5.8 million residential properties and delivers via Snowflake Marketplace, Snowflake Secure Data Share, MCP connector for AI agents, or structured flat files. Sample data is available for evaluation at no cost.
Request a sample file — sized to your region, delivered at no cost, ready to validate against your own CRM.