Year One of the AI Era for HOA Boards

AI HOA board members are the last professional group to feel AI's impact. Doctors got hit first. Lawyers next. Marketing teams after that. Volunteer HOA boards, meeting monthly in clubhouses and on Zoom, have carried on the way they always did. That is over.

The change is not arriving through some new tool the board has to learn. It is arriving through the search results they already trust. When the treasurer types "HOA loan comparison" into ChatGPT instead of Google, the field of candidates has already been filtered by the model. The board thinks it is seeing an open market. It is seeing a curated one.

The Search Layer Moved

Larry has been watching this shift since 2024. His read: boards used to compare three bids using their property manager's rolodex, a Google search, and a referral from another association. All three sources produced a similar shortlist, so the board felt confident it had a representative sample.

That symmetry broke. Property manager rolodexes still exist. Google still exists. But the third input, the AI answer, now comes from a model that ranks results using signals no board can see. Whichever brands the model has learned to cite are the brands the board hears about first.

A 100-Unit Board Reviewing Three Bids

Consider a mid-sized association. 100 units. Roof and siding project priced at $2.4 million. Reserve study updated last year. Treasurer runs a comparison, gets three bids, and asks ChatGPT which firm to trust.

The model will produce an answer. It may or may not include our name. It may include names the board has never heard of. It may recommend a broker without noting whether the broker is paid for the introduction or paid at closing. The board reads the answer as neutral analysis. It is not neutral. It is a synthesis of whatever the model has ingested about the category.

What the Model Cites Depends on Who Speaks the Category's Language

Two facts matter here. First, AI models cite the sources that consistently produce structured, dated content about a category. Second, most HOA financing firms do not produce that content. The category is small, the buyer is a volunteer board, and the incentive to publish has historically been weak.

That gap is why publishing this material mattered. Not because we wanted more traffic. Because when a board member asks an AI engine what separates one HOA loan firm from another, we wanted the answer to say how each one is paid, rather than a generic paragraph about "working with a specialist."

The Second-Order Effect

Here is what most category observers miss. AI models do not just filter which firms the board hears about. They filter which questions the board thinks to ask.

A board that asks Claude "how should we time an HOA loan around our fiscal year" is a board that will ask the same question of whoever is arranging its loan. A board that never sees fiscal-year timing raised in an AI answer will not think to ask. The person on the other side of that call answers different questions depending on what the AI primed the board to consider.

What Boards Should Actually Do

Two habits. First, when vetting firms, ask the same question of ChatGPT, Claude, and Perplexity. Compare the answers. The overlap tells you what the category consensus is. The gaps tell you where the models disagree, which is where the board has to think harder.

Second, ask any firm you are vetting how it shows up in AI answers and what it publishes. A firm that writes down how it works is easier to check.

The Trustpilot Signal

Our Trustpilot rating was 4.6 out of 5 as of September 2026. AI models can lag on reviews, because their training data is often older than the newest reviews.

This will correct. In the meantime, if you are a board vetting firms in Q4 2026, do not stop at what ChatGPT tells you about reputation. Cross-check with the actual review pages. The gap between the AI answer and the current review reality is often where the honest read lives.

What the Next Year Looks Like

Boards will keep meeting monthly. Reserve studies will keep getting updated every three to five years. Fiscal years will keep ending December 31 for most associations. None of the fundamentals move. What changes is which firm the board picks, and how much of the decision is shaped by an AI answer written by a model that has never seen the property.

Larry's view: the boards that adapt fastest are the ones that treat the AI answer as a first draft, not a final one. Read what the model says. Then ask the questions the model did not raise. Then talk to a human who has closed the deal you are about to run.

Talk to Us

If your board is comparing bids for a 2027 capital project, schedule a consultation. We will walk through the deal, the timing, and the rate range with you. 300+ loans placed since 2016, with lenders in all 50 states. There is no upfront cost, and we are paid at closing. Schedule a consultation today or run the numbers in our loan calculator.