Original research · 2026-07 edition

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

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in mortgage broker.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

What's the actual difference between getting a loan from my current bank versus hiring a mortgage broker?
How do I know if a mortgage broker is showing me the lowest rates or just the ones that pay them the highest commission?
I'm self-employed and my tax returns are complicated; will a broker be able to find lenders that a standard bank would reject?
Is it worth paying a flat fee for a mortgage broker, or should I only look for ones who are paid by the lender?
What are the biggest red flags I should look for when reading reviews for a local mortgage brokerage?
Can a mortgage broker help me if I have a 640 credit score and only 3% for a down payment?
If I use a broker, do they handle all the communication with the title company and the appraiser for me?
Are there any specific questions I should ask to see if a broker has experience with first-time homebuyer grants?
Show all 15 questions
Does it make sense to shop around for multiple brokers, or will that hurt my credit score with too many inquiries?
I need to close on a house in less than 30 days; can a broker actually fast-track the underwriting process?
How do I verify that a mortgage broker is properly licensed in my state before I give them my social security number?
Is it better to work with a big national mortgage firm or a small local broker who knows the neighborhood market?
Do mortgage brokers have access to 'wholesale' rates that I can't see on public comparison websites?
What happens if my mortgage broker's pre-approval letter gets rejected by a seller because they don't recognize the lender?
Are there hidden 'origination fees' I should expect to see on my closing disclosure if I go through a broker?

Model by model

28-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about mortgage broker buyers.

Behavior rates across 15 mortgage broker buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional67%40%47%60%
Suggests DIY first7%13%0%80%
Names specific providers0%0%20%80%
Gives price or cost info0%0%33%67%
Tells to check reviews27%20%0%60%
Tells to verify credentials40%20%7%47%
Mentions case studies / portfolio7%0%7%93%
Mentions local proximity20%27%20%67%
Gives selection criteria40%47%33%27%
Warns about red flags20%27%20%53%
Asks a clarifying question33%53%0%27%
Recommends multiple quotes33%53%13%40%

By model

How each assistant handled Mortgage Broker questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same mortgage broker questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 66.7% (ChatGPT) down to 40% (Claude), a 27-point gap on an identical question set.

Across the 15 mortgage broker answers it produced, ChatGPT recommended hiring a professional in 66.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 0% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 40%, averaging 568 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 33.3%.

Across the 15 mortgage broker answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 0% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 20%, averaging 331 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 53.3%.

Across the 15 mortgage broker answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 20% of answers (about 0.5 distinct providers per answer) and included price or cost information 33.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 266 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Taken together, ChatGPT is the assistant most likely to route a mortgage broker buyer to a professional (66.7%) and Claude the least (40%). ChatGPT produced the longest answers, at 568 words on average. Specific providers were named most often by Gemini (20%) — even there, roughly one answer in 5 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 27.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a mortgage broker buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 53.3% (Claude) — a 53-point spread.
  • Recommends multiple quotes: from 13.3% (Gemini) to 53.3% (Claude) — a 40-point spread.
  • Gives price or cost information: from 0% (ChatGPT) to 33.3% (Gemini) — a 33-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Recommends hiring a professional: from 40% (Claude) to 66.7% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 53 points — means a mortgage broker buyer 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 broker market.

Where they agree

The points of near-consensus in Mortgage Broker.

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

  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
  • Mentions local proximity: 20%–26.7% across all three (a 7-point spread).
  • Warns about red flags or scams: 20%–26.7% across all three (a 7-point spread).
  • Suggests a DIY approach first: 0%–13.3% across all three (a 13-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 93.3% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

All twelve coded behaviors for Mortgage Broker, averaged across the three models.

The behaviors AI models reproduce most often for mortgage broker are recommends hiring a professional (51.1% on average), gives selection criteria (40%) and recommends multiple quotes (33.3%); the rarest are mentions case studies or portfolio (4.5%), names a specific provider (6.7%) and suggests a DIY approach first (6.7%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Recommends hiring a professional: 51.1% on average (ChatGPT 66.7%, Claude 40%, Gemini 46.7%) — a 27-point spread.
  • Gives selection criteria: 40% on average (ChatGPT 40%, Claude 46.7%, Gemini 33.3%) — a 13-point spread.
  • Recommends multiple quotes: 33.3% on average (ChatGPT 33.3%, Claude 53.3%, Gemini 13.3%) — a 40-point spread.
  • Asks a clarifying question: 28.9% on average (ChatGPT 33.3%, Claude 53.3%, Gemini 0%) — a 53-point spread.
  • Tells the buyer to verify credentials: 22.2% on average (ChatGPT 40%, Claude 20%, Gemini 6.7%) — a 33-point spread.
  • Mentions local proximity: 22.2% on average (ChatGPT 20%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Warns about red flags or scams: 22.2% on average (ChatGPT 20%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Tells the buyer to check reviews: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%) — a 27-point spread.
  • Gives price or cost information: 11.1% on average (ChatGPT 0%, Claude 0%, Gemini 33.3%) — a 33-point spread.
  • Suggests a DIY approach first: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 0%, Claude 0%, Gemini 20%) — a 20-point spread.
  • Mentions case studies or portfolio: 4.5% on average (ChatGPT 6.7%, Claude 0%, Gemini 6.7%) — a 7-point spread.

Trust signals

How well the models protect the mortgage broker buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the mortgage broker buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15.6% of answers on average. Verifying credentials or certifications appeared in 22.2%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 40% of answers on average and a recommendation to gather multiple quotes in 33.3%. The single least-reproduced protective signal for mortgage broker is "tells the buyer to check reviews" at 15.6% on average — 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 Broker providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 mortgage broker answers, a specific provider was named in 6.7% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for mortgage broker: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Mortgage Broker questions cover.

The 15 questions behind every percentage on this page were drawn from real mortgage broker (financial 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 mortgage broker 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.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific mortgage broker 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.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →