Original research · 2026-07 edition

AI SEO Statistics: Hoa Management (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

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The question bank

The questions we tested — sampled from real buyer journeys in hoa management.

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

What does an HOA management company actually do day-to-day for a small neighborhood?
Is it cheaper to self-manage our 20-unit condo or hire a professional firm?
Our current HOA manager never responds to emails, how do we go about firing them?
What is the average cost per unit for HOA management in a mid-sized suburb?
What's the difference between full-service management and accounting-only services for an HOA?
How do I know if an HOA management company is properly licensed and insured in my state?
What are the red flags to look for when interviewing a new community manager?
Can an HOA management company help us with legal disputes between neighbors over property lines?
Show all 40 questions
What specific clauses should be included in a standard HOA management contract to protect the board?
How many properties should one HOA manager realistically handle at once before quality drops?
We have a $50,000 annual budget for management, what kind of service level does that usually buy?
Do HOA managers handle the entire bidding process for major roof repairs and landscaping?
Is it normal for HOA management companies to charge extra fees for attending evening board meetings?
How do I find an HOA management company that specializes in high-rise luxury buildings?
What software do the best HOA management firms use for resident portals and online payments?
Our board is burnt out, can a management company take over all the enforcement of restrictive covenants?
How long are typical HOA management contracts and can we negotiate a month-to-month trial?
What happens to our digital records and bank access if we decide to switch management companies?
Do HOA managers take a percentage or kickback from the vendors they hire for the neighborhood?
We need a manager who understands specific state laws regarding solar panels and xeriscaping.
How do we transition from developer-controlled management to a resident-led board smoothly?
What is the process for auditing an HOA management company’s financial books to ensure no fraud?
Are there management companies that focus specifically on small townhome communities with minimal common areas?
How much extra does an HOA manager charge for overseeing special assessments or major construction?
Can a management company help us collect years of unpaid dues from delinquent owners without a lawyer?
What specific questions should I ask during a site visit with a potential management firm?
Should the HOA manager be required to be present for every single monthly board meeting?
How do we handle a middle-of-the-night emergency like a burst main pipe if we don't have a management company?
Is it better to hire a local boutique firm or a large national HOA management corporation?
What is considered a reasonable response time for a manager to get back to a homeowner's maintenance request?
Do management companies provide help with the annual budget and long-term reserve study planning?
We’re seeing a lot of turnover in our assigned managers, is this a sign of a failing management company?
Can an HOA management company help us rewrite or update our outdated CC&Rs and bylaws?
What are the pros and cons of remote HOA management versus having an on-site manager?
How do I verify credentials like CMCA or PCAM when vetting a community manager?
What is the typical fee for a start-up or onboarding phase when hiring a new HOA firm?
Can we hire a management company just for financial reporting and tax filings but handle maintenance ourselves?
How do I check for official complaints or legal actions against an HOA management company?
Our neighborhood common areas are a mess, how fast can a new manager realistically turn things around?
Do HOA managers provide a 24/7 emergency maintenance hotline for residents to use?

Model by model

22-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 hoa management buyers.

Behavior rates across 40 hoa management buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional75%50%30%50%
Suggests DIY first23%13%10%80%
Names specific providers3%8%10%85%
Gives price or cost info10%15%18%90%
Tells to check reviews15%8%3%80%
Tells to verify credentials30%8%13%63%
Mentions case studies / portfolio18%3%0%83%
Mentions local proximity25%30%10%58%
Gives selection criteria48%45%23%43%
Warns about red flags25%18%8%78%
Asks a clarifying question48%63%0%23%
Recommends multiple quotes25%15%5%70%

By model

How each assistant handled Hoa Management questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same hoa management questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 75% (ChatGPT) down to 30% (Gemini), a 45-point gap on an identical question set.

Across the 40 hoa management answers it produced, ChatGPT recommended hiring a professional in 75% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 10% of the time. ChatGPT asked a clarifying question before answering in 47.5% of cases, warned about red flags or scams in 25%, and told the buyer to verify credentials in 30%, averaging 634 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 17.5%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 25%.

Across the 40 hoa management answers it produced, Claude recommended hiring a professional in 50% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 7.5% of answers (about 0.4 distinct providers per answer) and included price or cost information 15% 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 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 2.5%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 15%.

Across the 40 hoa management answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 10% 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 17.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 12.5%, averaging 265 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 10%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 5%.

Taken together, ChatGPT is the assistant most likely to route a hoa management buyer to a professional (75%) and Gemini the least (30%). ChatGPT produced the longest answers, at 634 words on average. Specific providers were named most often by Gemini (10%) — even there, roughly one answer in 10 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 22.2 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a hoa management 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 30% (Gemini) to 75% (ChatGPT) — a 45-point spread.
  • Gives selection criteria: from 22.5% (Gemini) to 47.5% (ChatGPT) — a 25-point spread.
  • Tells the buyer to verify credentials: from 7.5% (Claude) to 30% (ChatGPT) — a 23-point spread.
  • Mentions local proximity: from 10% (Gemini) to 30% (Claude) — a 20-point spread.

The widest single gap — asks a clarifying question, 63 points — means a hoa management 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 hoa management market.

Where they agree

The points of near-consensus in Hoa Management.

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

  • Names a specific provider: 2.5%–10% across all three (a 8-point spread).
  • Gives price or cost information: 10%–17.5% across all three (a 8-point spread).
  • Suggests a DIY approach first: 10%–22.5% across all three (a 13-point spread).
  • Tells the buyer to check reviews: 2.5%–15% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "gives price or cost information" (identical coding in 90% of questions) and least consistently on "asks a clarifying question" (22.5%).

Every behavior, measured

All twelve coded behaviors for Hoa Management, averaged across the three models.

The behaviors AI models reproduce most often for hoa management are recommends hiring a professional (51.7% on average), gives selection criteria (38.3%) and asks a clarifying question (36.7%); the rarest are mentions case studies or portfolio (6.7%), names a specific provider (6.7%) and tells the buyer to check reviews (8.3%). 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: 51.7% on average (ChatGPT 75%, Claude 50%, Gemini 30%) — a 45-point spread.
  • Gives selection criteria: 38.3% on average (ChatGPT 47.5%, Claude 45%, Gemini 22.5%) — a 25-point spread.
  • Asks a clarifying question: 36.7% on average (ChatGPT 47.5%, Claude 62.5%, Gemini 0%) — a 63-point spread.
  • Mentions local proximity: 21.7% on average (ChatGPT 25%, Claude 30%, Gemini 10%) — a 20-point spread.
  • Tells the buyer to verify credentials: 16.7% on average (ChatGPT 30%, Claude 7.5%, Gemini 12.5%) — a 23-point spread.
  • Warns about red flags or scams: 16.7% on average (ChatGPT 25%, Claude 17.5%, Gemini 7.5%) — a 18-point spread.
  • Suggests a DIY approach first: 15% on average (ChatGPT 22.5%, Claude 12.5%, Gemini 10%) — a 13-point spread.
  • Recommends multiple quotes: 15% on average (ChatGPT 25%, Claude 15%, Gemini 5%) — a 20-point spread.
  • Gives price or cost information: 14.2% on average (ChatGPT 10%, Claude 15%, Gemini 17.5%) — a 8-point spread.
  • Tells the buyer to check reviews: 8.3% on average (ChatGPT 15%, Claude 7.5%, Gemini 2.5%) — a 13-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 10%) — a 8-point spread.
  • Mentions case studies or portfolio: 6.7% on average (ChatGPT 17.5%, Claude 2.5%, Gemini 0%) — a 18-point spread.

Trust signals

How well the models protect the hoa management buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the hoa management buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.3% of answers on average. Verifying credentials or certifications appeared in 16.7%. Warning about red flags or scams appeared in 16.7%.

On structuring the decision, a selection-criteria checklist showed up in 38.3% of answers on average and a recommendation to gather multiple quotes in 15%. The single least-reproduced protective signal for hoa management is "tells the buyer to check reviews" at 8.3% 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 Hoa Management providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 hoa management answers, a specific provider was named in 6.7% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for hoa management: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Hoa Management questions cover.

The 40 questions behind every percentage on this page were drawn from real hoa management (home 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 hoa management 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 hoa management 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 →