AI SEO Statistics: Real Estate Law (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same real estate law questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 95% (ChatGPT) down to 67.5% (Gemini), a 28-point gap on an identical question set.

Across the 40 real estate law answers it produced, ChatGPT recommended hiring a professional in 95% of them and suggested a DIY approach first 30% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 17.5% of the time. ChatGPT asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 10%, averaging 506 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 70%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 10%.

Across the 40 real estate law answers it produced, Claude recommended hiring a professional in 82.5% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 40% of the time. Claude asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 0%, averaging 311 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 2.5%.

Across the 40 real estate law answers it produced, Gemini recommended hiring a professional in 67.5% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 5% of answers (about 0.2 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 12.5%, averaging 304 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route a real estate law buyer to a professional (95%) and Gemini the least (67.5%). ChatGPT produced the longest answers, at 506 words on average. Specific providers were named most often by Gemini (5%) — even there, roughly one answer in 20 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for real estate law, where all three landed within a few points of each other:

• Warns about red flags or scams: 12.5%–15% across all three (a 3-point spread).
• Names a specific provider: 0%–5% across all three (a 5-point spread).
• Tells the buyer to check reviews: 0%–5% across all three (a 5-point spread).
• Recommends multiple quotes: 2.5%–10% across all three (a 8-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (7.5%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the real estate law buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 3.3% of answers on average. Verifying credentials or certifications appeared in 7.5%. Warning about red flags or scams appeared in 14.2%.

On structuring the decision, a selection-criteria checklist showed up in 25.8% of answers on average and a recommendation to gather multiple quotes in 5%. The single least-reproduced protective signal for real estate law is "tells the buyer to check reviews" at 3.3% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real real estate law (legal services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact real estate law question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.