Sierra Rockwell turned to ChatGPT for false advertising AI home listings guidance while buying a home in Bangor’s Little City neighborhood earlier this year. The self-employed hairstylist and salon owner used the tool for questions about mortgages, appraisal issues, inspection issues, and property expenses.
She said it let her ask extremely specific questions and compare scenarios without leaning on her mortgage broker for every detail. Rockwell also said ChatGPT helped her find a local mortgage lender.
Bank of America Homebuyer Insights
A Bank of America Homebuyer Insights Report published earlier this year found that 1 in 5 prospective buyers used AI tools or chatbots for homebuying research in the last year. The report found the practice was most popular among Millennials and Gen Z, and that buyers leaned on AI for affordability estimates, mortgage payments, closing costs, neighborhood research, market trends, and property values.
Julie Williams, the owner and CEO of ERA Dawson-Bradford Co., REALTORS, said, “In the same way Google is ubiquitous, I think AI is dominating the conversation.”
Julie Williams on AI limits
Williams said AI software likely does not have access to the same information Maine real estate agents use every day to produce a comparable market analysis for clients. That leaves a gap between general research and the numbers that shape an offer or a listing price.
Judy Oberg, president of the Maine Association of Realtors and an agent at Bridgton-based Oberg Real Estate, said AI may be most useful at the beginning of the process because it can explain the steps involved in buying or selling a home so a client knows what to expect.
Judy Oberg on pricing
Oberg drew a firmer line around the decisions that matter most. “It shouldn’t be trusted to direct essential parts of the homebuying process, such as recommending how much a buyer should offer or how much a seller should list their house for,” she said.
For buyers, that means ChatGPT can help organize homework and define terms, but it does not replace the pricing judgment tied to local data. Rockwell’s use case shows where AI is already entering the search process, while Oberg’s warning marks the boundary that still matters most in a deal.
