Proptech AISource: RISMedia
How a 10-person Tennessee team gets recommended by ChatGPT: its own MLS data spin + weekly audits
Original headline: “DIY for AI: How One Team Leader Snowballed Data and Trust Into ChatGPT Referrals” — RISMedia, Oct 15, 2026
RISMedia, in the third part of a series, asked multiple AI models to recommend the top agent in markets rated as hot, then interviewed the agents they named — including John Turner of the 10-member Turner Victory Team in Murfreesboro, Tennessee. Turner says he began pulling market data every week when the market turned in 2022, built internal tools to process it, and added a website redesign, data reports for prospective clients, audits and consistent content; the team does not pay for online leads, yet after callers began saying they found the team online, lead volume grew every month. He says AI favors unique local data rather than recycled stats — the team uses MLS data with its own spin — makes videos and blog posts mostly to build authority with AI, runs weekly searches to see how it shows up, and cautions that AI hallucinates, ChatGPT is moving toward paid ads, the work takes a lot of time, and agents should not abandon what already works.
Our take (opinion & analysis, not reporting)
For solo agents and small teams: start with the low-cost step Turner suggests — put your name and market into ChatGPT and make sure your bios and profiles are consistent across social media and the web before investing in content. If you publish market reports built on MLS data, add your own local analysis, as he does, and check your MLS's display rules first. Tag inbound leads in your CRM by how they found you (including an 'AI chatbot' source) so you can tell whether this effort is actually producing business rather than guessing.
Online discussion: no notable public discussion found yet (checked Oct 16, 2026).