AI SEO Statistics: Multi Family Housing (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your multi family housing 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 multi family housing.
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
23.3% 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 multi family housing benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 80% | 72.5% | 47.5% | 66.7% |
| Suggests DIY first | 15% | 10% | 5% | 10% |
| Names specific providers | 7.5% | 5% | 15% | 9.2% |
| Gives price or cost info | 30% | 30% | 27.5% | 29.2% |
| Tells to check reviews | 10% | 2.5% | 5% | 5.8% |
| Tells to verify credentials | 25% | 15% | 10% | 16.7% |
| Mentions case studies / portfolio | 25% | 15% | 7.5% | 15.8% |
| Mentions local proximity | 47.5% | 37.5% | 15% | 33.3% |
| Gives selection criteria | 60% | 57.5% | 42.5% | 53.3% |
| Warns about red flags | 22.5% | 22.5% | 15% | 20% |
| Asks a clarifying question | 47.5% | 45% | 0% | 30.8% |
| Recommends multiple quotes | 27.5% | 15% | 0% | 14.2% |
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 | 57.5% |
| Suggests DIY first | 90% |
| Names specific providers | 82.5% |
| Gives price or cost info | 60% |
| Tells to check reviews | 85% |
| Tells to verify credentials | 77.5% |
| Mentions case studies / portfolio | 62.5% |
| Mentions local proximity | 50% |
| Gives selection criteria | 32.5% |
| Warns about red flags | 70% |
| Asks a clarifying question | 42.5% |
| Recommends multiple quotes | 70% |
By model
How each assistant handled Multi Family Housing questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same multi family housing questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 80% (ChatGPT) down to 47.5% (Gemini), a 33-point gap on an identical question set.
Across the 40 multi family housing answers it produced, ChatGPT recommended hiring a professional in 80% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 7.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 30% of the time. ChatGPT asked a clarifying question before answering in 47.5% of cases, warned about red flags or scams in 22.5%, and told the buyer to verify credentials in 25%, averaging 681 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 25%, and framed the choice around local proximity in 47.5%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 27.5%.
Across the 40 multi family housing answers it produced, Claude recommended hiring a professional in 72.5% of them and suggested a DIY approach first 10% 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 30% of the time. Claude asked a clarifying question before answering in 45% of cases, warned about red flags or scams in 22.5%, and told the buyer to verify credentials in 15%, averaging 329 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 15%, and framed the choice around local proximity in 37.5%; a selection-criteria checklist appeared in 57.5% of its answers and a recommendation to gather multiple quotes in 15%.
Across the 40 multi family housing answers it produced, Gemini recommended hiring a professional in 47.5% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 15% of answers (about 0.4 distinct providers per answer) and included price or cost information 27.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 10%, averaging 229 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 7.5%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching multi family housing toward professional help (80%) and Gemini the least (47.5%). ChatGPT produced the longest answers, at 681 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.
Question-level model disagreement is 23.3%. 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 multi family housing:
- Asks a clarifying question: from 0% (Gemini) to 47.5% (ChatGPT). The spread is 48 points.
- Recommends hiring a professional: from 47.5% (Gemini) to 80% (ChatGPT). The spread is 33 points.
- Mentions local proximity: from 15% (Gemini) to 47.5% (ChatGPT). The spread is 33 points.
- Recommends multiple quotes: from 0% (Gemini) to 27.5% (ChatGPT). The spread is 28 points.
- Mentions case studies or portfolio: from 7.5% (Gemini) to 25% (ChatGPT). The spread is 18 points.
The widest single gap concerns asks a clarifying question at 48 points. This means a buyer researching multi family housing 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 multi family housing market.
Where they agree
The points of near-consensus in Multi Family Housing.
On other behaviors the three models move almost in lockstep. The points of near-consensus for multi family housing, where all three landed within a few points of each other:
- Gives price or cost information: 27.5%–30% across all three (a 3-point spread).
- Tells the buyer to check reviews: 2.5%–10% across all three (a 8-point spread).
- Warns about red flags or scams: 15%–22.5% across all three (a 8-point spread).
- Suggests a DIY approach first: 5%–15% across all three (a 10-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 90% of questions) and least consistently on "gives selection criteria" (32.5%).
Every behavior, measured
All twelve coded behaviors for Multi Family Housing, averaged across the three models.
The behaviors AI models reproduce most often for multi family housing are recommends hiring a professional (66.7% on average), gives selection criteria (53.3%) and mentions local proximity (33.3%); the rarest are tells the buyer to check reviews (5.8%), names a specific provider (9.2%) and suggests a DIY approach first (10%). 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:
- Recommends hiring a professional: 66.7% on average (ChatGPT 80%, Claude 72.5%, Gemini 47.5%). The spread is 33 points.
- Gives selection criteria: 53.3% on average (ChatGPT 60%, Claude 57.5%, Gemini 42.5%). The spread is 18 points.
- Mentions local proximity: 33.3% on average (ChatGPT 47.5%, Claude 37.5%, Gemini 15%). The spread is 33 points.
- Asks a clarifying question: 30.8% on average (ChatGPT 47.5%, Claude 45%, Gemini 0%). The spread is 48 points.
- Gives price or cost information: 29.2% on average (ChatGPT 30%, Claude 30%, Gemini 27.5%). The spread is 3 points.
- Warns about red flags or scams: 20% on average (ChatGPT 22.5%, Claude 22.5%, Gemini 15%). The spread is 8 points.
- Tells the buyer to verify credentials: 16.7% on average (ChatGPT 25%, Claude 15%, Gemini 10%). The spread is 15 points.
- Mentions case studies or portfolio: 15.8% on average (ChatGPT 25%, Claude 15%, Gemini 7.5%). The spread is 18 points.
- Recommends multiple quotes: 14.2% on average (ChatGPT 27.5%, Claude 15%, Gemini 0%). The spread is 28 points.
- Suggests a DIY approach first: 10% on average (ChatGPT 15%, Claude 10%, Gemini 5%). The spread is 10 points.
- Names a specific provider: 9.2% on average (ChatGPT 7.5%, Claude 5%, Gemini 15%). The spread is 10 points.
- Tells the buyer to check reviews: 5.8% on average (ChatGPT 10%, Claude 2.5%, Gemini 5%). The spread is 8 points.
Trust signals
How well the models protect the multi family housing buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the multi family housing 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 16.7%. Warning about red flags or scams appeared in 20%.
On structuring the decision, a selection-criteria checklist showed up in 53.3% of answers on average and a recommendation to gather multiple quotes in 14.2%. The single least-reproduced protective signal for multi family housing 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 Multi Family Housing providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 multi family housing answers, a specific provider was named in 9.2% 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 multi family housing: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Multi Family Housing questions cover.
The 40 questions behind every percentage on this page form a frozen multi family housing (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 multi family housing 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 multi family housing 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: Multi Family Housing (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/real-estate/multi-family-housing