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How to use AI to locate and Identify ‘Likely Movers’ via Financial & Lifestyle Triggers

Identifying households on the brink of selling lets you phone, letter‐drop, or email first well before your competitors are even aware the owner is thinking about a move.

AI models can now surface those owners by watching financial and lifestyle signals that normally sit buried in dozens of databases and your own CRM.

Financial & lifestyle triggers worth tracking

Trigger type	Examples you can monitor with data	Why it hints at a move
Equity & loan pressure	Rapid equity growth, refinancing enquiries, mortgage delinquency notices	Owners can cash-out gains or relieve stress by selling
Household change	Births, divorces, empty-nest status, ageing parents	More (or fewer) bedrooms suddenly required
Employment & income	Job relocation, redundancy payouts, major pay rises	Relocation or lifestyle upgrade/downgrade
Lifestyle search behaviour	Frequent suburb-profile views, saved listings, appraisal tool usage	Digital “footprints” show early intent
Regulatory deadlines	End of fixed-rate period, land-tax changes, downsizer super incentives	Owners act to beat upcoming costs

A simple AI-powered workflow

Collect raw data
Pull property, credit, and demographic feeds plus your own CRM interactions.
Enrich with trigger flags
Tag each contact/property when a trigger fires—e.g. equity passes 50 %, new baby registered, or browsing activity spikes.
Score for “likelihood to move”
Feed the tagged dataset into a predictive model (many tools below provide one out-of-the-box). The model returns a percentage probability of selling inside 3, 6 or 12 months.
Surface hot prospects in your CRM
Write the score back to each contact and create daily call/SMS tasks for everyone above a set threshold.
Automate gentle outreach
Use personalised emails, letters, or RiTA-style two-way SMS to offer an updated appraisal or suburb-report.
Review & retrain quarterly
Compare predictions against actual listings to refine trigger weights and keep accuracy high.

Recommended tools

Cotality Propensity-to-List – National property, credit and life-event dataset (formerly CoreLogic). Ranks every address by sell likelihood; API drops scores straight into most CRMs.
PropTrack Property Sense – Uses portal browsing, listings history and image recognition to flag homes predicted to list or refinance within 90 days; no-code to full API options.
RiTA by Cotality – AI assistant that cleans your CRM, scores contacts, then starts two-way SMS chats exactly when a prospect’s sell-score peaks. Saves hours of cold calls.
Pricefinder Prospecting Suite – Domain-powered database covering 14 M properties and 30 years of sales; equity change and tenure filters uncover silent sellers in your patch.
Equifax Consumer Signals – Credit-bureau feed highlighting mortgage stress, refinancing enquiries and fixed-rate expiries—financial nudges often preceding a listing decision.
ABS Data API – Free demographic shifts (births, ageing, migration) at SA1 level; plug into spreadsheets or AutoML to weight lifestyle-change probability.
ChatGPT Plus + Code Interpreter – Converts messy export files into clean tables, builds quick logistic-regression or gradient-boost models, and drafts personalised letterbox copy in seconds.
Google Vertex AI Tabular – Drag-and-drop AutoML that trains a custom “likely mover” model on your own CRM plus open datasets; outputs deployable REST endpoint.

Tips to get started this month

Start small—export 12 months of past listings, tag the triggers manually, and test free AutoML models.
Protect privacy—comply with Spam Act, Do-Not-Call and state tenancy data laws when importing contact lists.
Keep the human touch—use AI scores to guide genuine service conversations, not blanket “thinking-of-selling?” spam.
Measure accuracy—track how many high-score contacts list within your chosen window; anything above 30 % hit-rate is a solid benchmark.

By harnessing the right mix of trigger data, prediction models and gentle personalised outreach, you can position yourself as the helpful local advisor before the “For Sale” sign even arrives.

Author – Ken Hobson.