Original research · 2026-07 edition

AI SEO Statistics: Commercial Real Estate (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 commercial real estate.

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

What is the difference between a listing agent and a tenant rep when I am looking for a new office?
Is it worth hiring a commercial real estate broker for a small 1,500 sq ft retail lease?
How do commercial broker commissions work and does the landlord always pay the fee?
I am looking to buy a warehouse for my distribution business; what specific experience should I look for in a broker?
What are the red flags I should look for when interviewing a commercial real estate firm?
Can I find off-market commercial properties on my own or do I need a professional for that?
How long does the average commercial property search take from first meeting a broker to signing a lease?
What should be included in a commercial real estate broker representation agreement?
Show all 15 questions
Should I hire a local boutique firm or a big national agency for a mid-sized industrial purchase?
What are the most important questions to ask a commercial broker before signing an exclusivity deal?
I need to sublease my office space because we are downsizing; how do I find a broker who specializes in that?
If I am a first-time commercial investor with a $2M budget, do I need a buyer agent or can I just call the numbers on the signs?
How do I vet a commercial property manager if I am buying an apartment complex out of state?
What is the typical timeline for due diligence on a commercial building and how does a broker help with that?
Why would a company use a site selection consultant instead of just a regular commercial real estate broker?

Model by model

29-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 commercial real estate buyers.

Behavior rates across 15 commercial real estate buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional93%80%47%53%
Suggests DIY first7%0%7%93%
Names specific providers7%7%13%80%
Gives price or cost info13%13%27%67%
Tells to check reviews33%20%0%67%
Tells to verify credentials40%27%0%53%
Mentions case studies / portfolio40%33%0%53%
Mentions local proximity80%67%20%27%
Gives selection criteria47%47%20%40%
Warns about red flags27%27%20%60%
Asks a clarifying question53%47%0%27%
Recommends multiple quotes27%20%0%60%

By model

How each assistant handled Commercial Real Estate questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same commercial real estate questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 93.3% (ChatGPT) down to 46.7% (Gemini), a 47-point gap on an identical question set.

Across the 15 commercial real estate answers it produced, ChatGPT recommended hiring a professional in 93.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.5 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT 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 40%, averaging 682 words per answer. On the remaining cues it told the buyer to check reviews in 33.3%, pointed to case studies or a portfolio in 40%, and framed the choice around local proximity in 80%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 commercial real estate answers it produced, Claude recommended hiring a professional in 80% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 26.7%, averaging 323 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 33.3%, and framed the choice around local proximity in 66.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 commercial real estate answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 13.3% of answers (about 0.5 distinct providers per answer) and included price or cost information 26.7% 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 0%, averaging 255 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 20%; 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 commercial real estate buyer to a professional (93.3%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 682 words on average. Specific providers were named most often by Gemini (13.3%) — even there, roughly one answer in 8 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 28.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a commercial real estate buyer happens to ask matters most:

  • Mentions local proximity: from 20% (Gemini) to 80% (ChatGPT) — a 60-point spread.
  • Asks a clarifying question: from 0% (Gemini) to 53.3% (ChatGPT) — a 53-point spread.
  • Recommends hiring a professional: from 46.7% (Gemini) to 93.3% (ChatGPT) — a 47-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.
  • Mentions case studies or portfolio: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.

The widest single gap — mentions local proximity, 60 points — means a commercial real estate 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 commercial real estate market.

Where they agree

The points of near-consensus in Commercial Real Estate.

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

  • Names a specific provider: 6.7%–13.3% across all three (a 7-point spread).
  • Suggests a DIY approach first: 0%–6.7% across all three (a 7-point spread).
  • Warns about red flags or scams: 20%–26.7% across all three (a 7-point spread).
  • Gives price or cost information: 13.3%–26.7% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (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 Commercial Real Estate, averaged across the three models.

The behaviors AI models reproduce most often for commercial real estate are recommends hiring a professional (73.3% on average), mentions local proximity (55.6%) and gives selection criteria (37.8%); the rarest are suggests a DIY approach first (4.5%), names a specific provider (8.9%) and recommends multiple quotes (15.6%). 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: 73.3% on average (ChatGPT 93.3%, Claude 80%, Gemini 46.7%) — a 47-point spread.
  • Mentions local proximity: 55.6% on average (ChatGPT 80%, Claude 66.7%, Gemini 20%) — a 60-point spread.
  • Gives selection criteria: 37.8% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 20%) — a 27-point spread.
  • Asks a clarifying question: 33.3% on average (ChatGPT 53.3%, Claude 46.7%, Gemini 0%) — a 53-point spread.
  • Warns about red flags or scams: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Mentions case studies or portfolio: 24.4% on average (ChatGPT 40%, Claude 33.3%, Gemini 0%) — a 40-point spread.
  • Tells the buyer to verify credentials: 22.2% on average (ChatGPT 40%, Claude 26.7%, Gemini 0%) — a 40-point spread.
  • Gives price or cost information: 17.8% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 26.7%) — a 13-point spread.
  • Tells the buyer to check reviews: 17.8% on average (ChatGPT 33.3%, Claude 20%, Gemini 0%) — a 33-point spread.
  • Recommends multiple quotes: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%) — a 27-point spread.
  • Names a specific provider: 8.9% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Suggests a DIY approach first: 4.5% on average (ChatGPT 6.7%, Claude 0%, Gemini 6.7%) — a 7-point spread.

Trust signals

How well the models protect the commercial real estate buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the commercial real estate buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 17.8% of answers on average. Verifying credentials or certifications appeared in 22.2%. Warning about red flags or scams appeared in 24.5%.

On structuring the decision, a selection-criteria checklist showed up in 37.8% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for commercial real estate is "recommends multiple quotes" 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 Commercial Real Estate providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 commercial real estate answers, a specific provider was named in 8.9% of responses on average — roughly 0.4 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for commercial real estate: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Commercial Real Estate questions cover.

The 15 questions behind every percentage on this page were drawn from real commercial real estate (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 commercial real estate 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 commercial real estate 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 →