They need to see you understand that raw skip-tracing data is unreliable without verification, and that you can use tools like PropStream or BatchLeads to build clean lists so agents don't waste call time on bad numbers.
Describe the data sources and tools you'd layer together (public records plus aggregators), then explain your quality-control step, like spot-checking or confirming each number's type, to show you deliver lists that are actually usable.
Start with the listing source itself, the MLS export or the FSBO listing, to confirm the property address and any name attached. Layer in public records, such as county assessor or property tax records, to confirm the owner's actual name. Use a skip-tracing or data aggregation tool, like the ones mentioned in the question, to pull candidate phone numbers and emails tied to that owner and address. Cross-check across more than one source rather than trusting a single hit, agreement between two tools raises confidence in the data. Before handing the list to an agent, spot-check a sample: confirm the number type, since that affects whether cold-calling consent rules apply, and flag anything that looks clearly outdated or disconnected. Deliver a clean list with a confidence note per contact rather than a raw, unfiltered export.
Situation
In my previous role as a VA for a real estate team, I was tasked with building a prioritized call list from expired MLS listings and FSBO posts so the agents could focus on closing rather than hunting down numbers.
Task
I needed to find accurate phone numbers and emails for 50 potential sellers from a mix of recently expired listings and Facebook Marketplace FSBOs, verify the data, and organize it into a clean, call-ready list within two days.
Action
First, I pulled the expired addresses from our MLS alerts and cross-referenced them with PropStream to get owner names and public records. For FSBOs, I systematically searched Zillow, Craigslist, and Facebook Marketplace daily, noting listings posted over 25 days ago because sellers there were often more receptive. I used BatchLeads to look up contact info and then manually verified every phone number by checking for recent activity or matching it against social profiles. I disqualified any number that came back as disconnected or a business line, marking data confidence levels in the spreadsheet.
Result
I delivered a list of 47 leads with verified direct lines and a 92% answer rate on the first dial. Our agents converted 12 of those into appointments that week, which was 30% higher than previous unverified lists.
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