AI SEO Statistics: Mortgage Industry (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your mortgage industry SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for mortgage industry.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
21.1% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 40 mortgage industry benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 67.5% | 45% | 17.5% | 43.3% |
| Suggests DIY first | 22.5% | 17.5% | 7.5% | 15.8% |
| Names specific providers | 5% | 10% | 10% | 8.3% |
| Gives price or cost info | 20% | 32.5% | 37.5% | 30% |
| Tells to check reviews | 7.5% | 7.5% | 2.5% | 5.8% |
| Tells to verify credentials | 10% | 7.5% | 0% | 5.8% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 0% |
| Mentions local proximity | 15% | 12.5% | 5% | 10.8% |
| Gives selection criteria | 32.5% | 30% | 17.5% | 26.7% |
| Warns about red flags | 15% | 12.5% | 12.5% | 13.3% |
| Asks a clarifying question | 70% | 67.5% | 2.5% | 46.7% |
| Recommends multiple quotes | 37.5% | 37.5% | 7.5% | 27.5% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 30% |
| Suggests DIY first | 85% |
| Names specific providers | 87.5% |
| Gives price or cost info | 47.5% |
| Tells to check reviews | 90% |
| Tells to verify credentials | 87.5% |
| Mentions case studies / portfolio | 100% |
| Mentions local proximity | 82.5% |
| Gives selection criteria | 60% |
| Warns about red flags | 87.5% |
| Asks a clarifying question | 10% |
| Recommends multiple quotes | 52.5% |
By model
How each assistant handled Mortgage Industry questions.
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 buyer researching mortgage industry toward professional help (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.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 21.1%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where the choice of assistant matters most for a buyer researching mortgage industry:
- Asks a clarifying question: from 2.5% (Gemini) to 70% (ChatGPT). The spread is 68 points.
- Recommends hiring a professional: from 17.5% (Gemini) to 67.5% (ChatGPT). The spread is 50 points.
- Recommends multiple quotes: from 7.5% (Gemini) to 37.5% (ChatGPT). The spread is 30 points.
- Gives price or cost information: from 20% (ChatGPT) to 37.5% (Gemini). The spread is 18 points.
- Suggests a DIY approach first: from 7.5% (Gemini) to 22.5% (ChatGPT). The spread is 15 points.
The widest single gap concerns asks a clarifying question at 68 points. This means a buyer researching mortgage industry can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the mortgage industry market.
Where they agree
The points of near-consensus in Mortgage Industry.
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%).
Every behavior, measured
All twelve coded behaviors for Mortgage Industry, averaged across the three models.
The behaviors AI models reproduce most often for mortgage industry are asks a clarifying question (46.7% on average), recommends hiring a professional (43.3%) and gives price or cost information (30%); the rarest are mentions case studies or portfolio (0%), tells the buyer to verify credentials (5.8%) and tells the buyer to check reviews (5.8%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Asks a clarifying question: 46.7% on average (ChatGPT 70%, Claude 67.5%, Gemini 2.5%). The spread is 68 points.
- Recommends hiring a professional: 43.3% on average (ChatGPT 67.5%, Claude 45%, Gemini 17.5%). The spread is 50 points.
- Gives price or cost information: 30% on average (ChatGPT 20%, Claude 32.5%, Gemini 37.5%). The spread is 18 points.
- Recommends multiple quotes: 27.5% on average (ChatGPT 37.5%, Claude 37.5%, Gemini 7.5%). The spread is 30 points.
- Gives selection criteria: 26.7% on average (ChatGPT 32.5%, Claude 30%, Gemini 17.5%). The spread is 15 points.
- Suggests a DIY approach first: 15.8% on average (ChatGPT 22.5%, Claude 17.5%, Gemini 7.5%). The spread is 15 points.
- Warns about red flags or scams: 13.3% on average (ChatGPT 15%, Claude 12.5%, Gemini 12.5%). The spread is 3 points.
- Mentions local proximity: 10.8% on average (ChatGPT 15%, Claude 12.5%, Gemini 5%). The spread is 10 points.
- Names a specific provider: 8.3% on average (ChatGPT 5%, Claude 10%, Gemini 10%). The spread is 5 points.
- Tells the buyer to check reviews: 5.8% on average (ChatGPT 7.5%, Claude 7.5%, Gemini 2.5%). The spread is 5 points.
- Tells the buyer to verify credentials: 5.8% on average (ChatGPT 10%, Claude 7.5%, Gemini 0%). The spread is 10 points.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the mortgage industry buyer.
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. This is the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.
Referral behavior
Do AI models name Mortgage Industry providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 mortgage industry answers, a specific provider was named in 8.3% of responses on average, or roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for mortgage industry: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Mortgage Industry questions cover.
The 40 questions behind every percentage on this page form a frozen mortgage industry (real estate; buyer hiring decisions for this specific service) buyer-intent benchmark. 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 rather than 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.
How to read this
A note on the numbers.
A percentage here is the share of a model's 40 answers in which the behavior appeared at least once. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific mortgage industry question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
Methodology
A controlled snapshot, documented end to end.
40 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Mortgage Industry (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/real-estate/mortgage-industry