AI SEO Statistics: Mortgage Industry (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same mortgage industry questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 67.5% (ChatGPT) down to 17.5% (Gemini), a 50-point gap on an identical question set.

Across the 40 mortgage industry answers it produced, ChatGPT recommended hiring a professional in 67.5% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 5% of answers (about 0.3 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 10%, averaging 530 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 37.5%.

Across the 40 mortgage industry answers it produced, Claude recommended hiring a professional in 45% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 10% of answers (about 0.4 distinct providers per answer) and included price or cost information 32.5% of the time. Claude asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 7.5%, averaging 309 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 30% of its answers and a recommendation to gather multiple quotes in 37.5%.

Across the 40 mortgage industry answers it produced, Gemini recommended hiring a professional in 17.5% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 10% of answers (about 0.2 distinct providers per answer) and included price or cost information 37.5% 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 0%, averaging 279 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 7.5%.

Taken together, ChatGPT is the assistant most likely to route a mortgage industry buyer to a professional (67.5%) and Gemini the least (17.5%). ChatGPT produced the longest answers, at 530 words on average. Specific providers were named most often by Claude (10%) — even there, roughly one answer in 10 carried a name.

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

• Mentions case studies or portfolio: 0% across all three models.
• Warns about red flags or scams: 12.5%–15% across all three (a 3-point spread).
• Names a specific provider: 5%–10% across all three (a 5-point spread).
• Tells the buyer to check reviews: 2.5%–7.5% across all three (a 5-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (10%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the mortgage industry buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5.8% of answers on average. Verifying credentials or certifications appeared in 5.8%. Warning about red flags or scams appeared in 13.3%.

On structuring the decision, a selection-criteria checklist showed up in 26.7% of answers on average and a recommendation to gather multiple quotes in 27.5%. The single least-reproduced protective signal for mortgage industry is "tells the buyer to check reviews" at 5.8% 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 mortgage industry (real estate; 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 mortgage industry 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.