We analyzed how major AI platforms respond to this query. Here's what they recommend.
Each platform interprets this query differently. Here is a summary of their responses.
OpenAI
ChatGPT recommends a multi-step process: asking for referrals from recent homebuyers, checking agent reviews on Zillow, Realtor.com, and Google, verifying license status through state real estate commission databases, and interviewing at least three agents before choosing. It emphasizes factors like local market knowledge, transaction volume, negotiation track record, and communication style.
The response typically lists questions to ask potential agents: their average list-to-sale price ratio, days on market for listings, transaction volume in your area, and commission structure. ChatGPT tends to recommend buyer's agent representation and explains how commission structures work, including the NAR settlement changes affecting buyer agent commissions.
Perplexity AI
Perplexity cites Zillow research, Realtor.com guides, and recent real estate journalism. It's more likely to mention agent matching services like HomeLight, Clever Real Estate, and UpNest that use data to match buyers/sellers with top-performing agents. Perplexity provides more specific data about what distinguishes top-performing agents—transaction volume percentiles, average days on market, and client retention rates.
The platform also addresses the impact of recent NAR commission changes, explaining how buyer-agent compensation is evolving and what buyers should know about negotiating agent fees.
Anthropic
Claude provides the most comprehensive framework for agent evaluation, discussing production metrics (transaction volume, list-to-sale ratio), specialization fit (first-time buyers, luxury, investment properties), and working style (full-service vs. limited-service, team vs. solo agent). It recommends checking the agent's recent sales on MLS or public property records rather than relying solely on self-reported data.
Claude also discusses discount brokerages and flat-fee alternatives (Redfin, Purplebricks, Clever), noting that full-service commission may not be necessary for experienced buyers/sellers in competitive markets. This balanced perspective helps users evaluate whether they need a traditional full-commission agent.
Gemini leverages Google Maps and local agent data to potentially show nearby real estate agents with reviews and ratings. It emphasizes Google reviews and business profile information while also linking to Zillow and Realtor.com for additional agent data. Gemini's response includes practical advice about searching Google for '[city] real estate agent reviews.'
The response frames agent search around Google's local business ecosystem, which favors agents who have invested in Google Business Profile optimization and Google Ads.
Patterns we observed across AI platform responses for this query.
Real estate agent discovery is a category undergoing structural disruption that AI platforms are capturing at different speeds. The NAR commission settlement, the rise of discount brokerages, and the growth of agent matching platforms are all changing how consumers find and hire real estate agents. AI platforms that draw from current sources (Perplexity) are more accurately reflecting these changes than platforms relying on older training data.
For individual real estate agents, AI visibility strategy should focus on building a digital presence that AI platforms can discover and verify. This means maintaining profiles on Zillow, Realtor.com, and Google Business Profile with consistent transaction data, client reviews, and specialization information. Agents with clear, data-backed positioning—'top-producing agent for first-time buyers in [neighborhood]'—are more likely to match specific user queries.
The broader industry implication is that AI-powered agent matching is becoming a more important channel than traditional referrals. As more consumers start their agent search with AI queries rather than asking friends, the agents and platforms with the strongest AI visibility will capture a growing share of client relationships. This shift favors data-driven agents over relationship-dependent ones.
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