AI SEO Statistics: Letting Agents (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in letting agents.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
23-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 letting agents buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 63% | 33% | 20% | 40% |
| Suggests DIY first | 20% | 15% | 5% | 75% |
| Names specific providers | 10% | 10% | 15% | 80% |
| Gives price or cost info | 8% | 23% | 35% | 63% |
| Tells to check reviews | 8% | 10% | 0% | 83% |
| Tells to verify credentials | 10% | 13% | 10% | 75% |
| Mentions case studies / portfolio | 8% | 3% | 3% | 90% |
| Mentions local proximity | 38% | 33% | 18% | 58% |
| Gives selection criteria | 33% | 43% | 20% | 40% |
| Warns about red flags | 10% | 18% | 18% | 83% |
| Asks a clarifying question | 55% | 63% | 0% | 23% |
| Recommends multiple quotes | 13% | 10% | 0% | 80% |
By model
How each assistant handled Letting Agents questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same letting agents questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 62.5% (ChatGPT) down to 20% (Gemini), a 43-point gap on an identical question set.
Across the 40 letting agents answers it produced, ChatGPT recommended hiring a professional in 62.5% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 10% of answers (about 0.5 distinct providers per answer) and included price or cost information 7.5% of the time. ChatGPT asked a clarifying question before answering in 55% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 10%, averaging 569 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 7.5%, and framed the choice around local proximity in 37.5%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 12.5%.
Across the 40 letting agents answers it produced, Claude recommended hiring a professional in 32.5% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 22.5% of the time. Claude asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 12.5%, averaging 309 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 32.5%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 10%.
Across the 40 letting agents answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 15% of answers (about 0.6 distinct providers per answer) and included price or cost information 35% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 10%, averaging 263 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 17.5%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a letting agents buyer to a professional (62.5%) and Gemini the least (20%). ChatGPT produced the longest answers, at 569 words on average. Specific providers were named most often by Gemini (15%) — even there, roughly one answer in 7 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 22.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a letting agents buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 62.5% (Claude) — a 63-point spread.
- Recommends hiring a professional: from 20% (Gemini) to 62.5% (ChatGPT) — a 43-point spread.
- Gives price or cost information: from 7.5% (ChatGPT) to 35% (Gemini) — a 28-point spread.
- Gives selection criteria: from 20% (Gemini) to 42.5% (Claude) — a 23-point spread.
- Mentions local proximity: from 17.5% (Gemini) to 37.5% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 63 points — means a letting agents 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 letting agents market.
Where they agree
The points of near-consensus in Letting Agents.
On other behaviors the three models move almost in lockstep — the points of near-consensus for letting agents, where all three landed within a few points of each other:
- Tells the buyer to verify credentials: 10%–12.5% across all three (a 3-point spread).
- Names a specific provider: 10%–15% across all three (a 5-point spread).
- Mentions case studies or portfolio: 2.5%–7.5% across all three (a 5-point spread).
- Warns about red flags or scams: 10%–17.5% across all three (a 8-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 90% of questions) and least consistently on "asks a clarifying question" (22.5%).
Every behavior, measured
All twelve coded behaviors for Letting Agents, averaged across the three models.
The behaviors AI models reproduce most often for letting agents are asks a clarifying question (39.2% on average), recommends hiring a professional (38.3%) and gives selection criteria (31.7%); the rarest are mentions case studies or portfolio (4.2%), tells the buyer to check reviews (5.8%) and recommends multiple quotes (7.5%). 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: 39.2% on average (ChatGPT 55%, Claude 62.5%, Gemini 0%) — a 63-point spread.
- Recommends hiring a professional: 38.3% on average (ChatGPT 62.5%, Claude 32.5%, Gemini 20%) — a 43-point spread.
- Gives selection criteria: 31.7% on average (ChatGPT 32.5%, Claude 42.5%, Gemini 20%) — a 23-point spread.
- Mentions local proximity: 29.2% on average (ChatGPT 37.5%, Claude 32.5%, Gemini 17.5%) — a 20-point spread.
- Gives price or cost information: 21.7% on average (ChatGPT 7.5%, Claude 22.5%, Gemini 35%) — a 28-point spread.
- Warns about red flags or scams: 15% on average (ChatGPT 10%, Claude 17.5%, Gemini 17.5%) — a 8-point spread.
- Suggests a DIY approach first: 13.3% on average (ChatGPT 20%, Claude 15%, Gemini 5%) — a 15-point spread.
- Names a specific provider: 11.7% on average (ChatGPT 10%, Claude 10%, Gemini 15%) — a 5-point spread.
- Tells the buyer to verify credentials: 10.8% on average (ChatGPT 10%, Claude 12.5%, Gemini 10%) — a 3-point spread.
- Recommends multiple quotes: 7.5% on average (ChatGPT 12.5%, Claude 10%, Gemini 0%) — a 13-point spread.
- Tells the buyer to check reviews: 5.8% on average (ChatGPT 7.5%, Claude 10%, Gemini 0%) — a 10-point spread.
- Mentions case studies or portfolio: 4.2% on average (ChatGPT 7.5%, Claude 2.5%, Gemini 2.5%) — a 5-point spread.
Trust signals
How well the models protect the letting agents buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the letting agents 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 10.8%. Warning about red flags or scams appeared in 15%.
On structuring the decision, a selection-criteria checklist showed up in 31.7% of answers on average and a recommendation to gather multiple quotes in 7.5%. The single least-reproduced protective signal for letting agents 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.
Referral behavior
Do AI models name Letting Agents providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 letting agents answers, a specific provider was named in 11.7% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for letting agents: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Letting Agents questions cover.
The 40 questions behind every percentage on this page were drawn from real letting agents (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 letting agents 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 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific letting agents 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 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 2026-07-06, 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 →