Predictive Homeowner Data: Reaching Sellers Before They Search

Most movers buy leads that are already shopping. By the time a homeowner fills out a form on three moving company websites, the race is a price war and the margin is gone. On the Snoball Effect Podcast, Hunter Munroe, VP of Sales at USA Home Listings and its new My Home Story platform, laid out a different approach: use homeowner data to get to sellers first, before they ever start searching. This piece goes deeper on how that predictive data actually works and how a mover can put it to use.

The Problem With Waiting for the Hand Raise

A homeowner who is actively searching is a homeowner every competitor can also see. The data most movers rely on is reactive. It tells you someone wants a quote right now, which means you are already one of several names in the running.

Hunter’s point is that the useful signal shows up much earlier than the quote request. A house going on the market is a signal. A house tagged coming soon is a signal. A house going under contract is a signal. Each of those events happens weeks before the homeowner is ready to book, and most of them are invisible to a mover who is waiting on inbound.

On-Market and Off-Market Data

USA Home Listings started about eight years ago as a homeowner-data and direct-mail business, and today it works with about 500 movers. The company’s strength has always been targeting houses that are newly on the market or newly under contract and putting the mover’s brand in front of them.

My Home Story, launched this month, widens the aperture. Movers keep the on-market data they are used to, and they add off-market data, houses that are not currently listed at all, along with the full history of the home. The filters are built for a mover’s buying signals: pending and under-contract homes, newly listed homes within the last 14 days, coming-soon tags, price, square footage, and year built. There is also a do-not-call filter that screens out numbers a mover legally cannot dial, and the ability to exclude vacant homes so you are not mailing an empty house.

The off-market detail is where the picture gets rich. Hunter walked through seeing beds and baths, lot size, how much equity the homeowner holds, the listing agent and their contact information, how long the current owner has held the home, and when it last sold. As he put it, that lets an owner decide who their ideal customer is and who it is not, all before picking up the phone.

A Lead Score That Learns From You

The feature Hunter is most excited about is the lead score, and the reason is that it is not a static number pulled off a purchased list. It improves with use.

“This lead score is gonna learn from you and your performance in the platform,” Hunter said. “The more you use it, the more accurate it will be.” As a mover pulls listings, reaches out, and books jobs, the system captures what actually converts for that specific company. Over time it can point out patterns like which price points tend to book or whether homes that just went under contract respond better to outreach.

That feedback loop matters because no two movers have the same sweet spot. A premium long-distance mover and a budget local crew will book very different homes. A score that learns from your own results reflects your ideal listing, not an industry average. It is the difference between renting someone else’s definition of a good lead and building your own.

Why Equity and Home Size Are Timing Clues

Two of the data points Hunter highlighted double as timing signals. Equity tells you something about the homeowner’s ability and motivation to move. Home size tells you something about lead time. Hunter noted that larger homes tend to plan their moves further in advance, which is why he would call the owner of a larger home earlier in the process. A mover who reads those signals can sequence outreach intelligently instead of blasting everyone on day one.

This is really a question of timing, and timing is a skill movers can build. Reaching the right homeowner at the right moment is the same discipline behind knowing the best time to ask for a referral. Data tells you who and when. The mover still has to act on it.

Turning Data Into a Repeatable Engine

Predictive data is a foundation, not a finished machine. Hunter described My Home Story as something a mover can plug into the marketing tools they already use, so the ideal listings flow straight into the systems that do the outreach. That is the point where a list of addresses becomes a working motion.

The highest-leverage use of this data is not chasing individual movers one at a time. It is identifying the realtors who consistently move inventory in your market and building relationships with them, so their sellers come to you by default. Homeowner data shows you which agents have the listings worth pursuing. From there, the play is to build a realtor referral engine that turns those relationships into recurring, low-cost leads.

The takeaway from Hunter is straightforward. Stop treating the quote request as the starting line. The homeowner was a seller long before they were a shopper, and the data to reach them at that stage now exists. The movers who get there first are the ones setting the price instead of matching it.