AI-assisted homebuying in Florida is squeezing the buyer's agent commission
Florida buyers are quietly closing homes without agents, leaning on AI to search, price, and draft contracts. Homa, a new platform built for self-represented buyers, says at least 10 end-to-end deals have closed with more in escrow. The draw is simple: skip the buyer's agent commission-typically 2.5% to 3%-and keep the savings or apply them to closing costs.
Real savings are showing up on HUDs. DJ, a 32-year-old pharmacist, bought a $420,000 home in Tampa Bay and saved $10,500 by self-representing through Homa. Vicki Lynn, a physical therapist assistant who moved to Vero Beach, purchased a $313,000 home and redirected roughly $8,000 straight to closing costs.
What Homa actually does
The platform bundles home search, instant tour scheduling, AI-driven pricing analysis, and automated contract creation. Buyers can tour quickly and submit offers the same day-hours instead of days.
There's a free version and a $1,995 "Homa Pro" tier that assigns a licensed transaction broker and protects the buyer's rebate. Without that layer, some listing agents try to capture both sides of the commission when unrepresented buyers show up.
Why this is happening now
The March NAR settlement forced upfront buyer agreements before touring and put compensation in plain view. Once buyers see the number, some look for alternatives. Details from NAR are here: NAR Settlement FAQ.
Buyers already use AI across the funnel
On Redfin, consumers describe what they want in plain language-quiet street, room to grow, short commute-and discover options they wouldn't have searched. Zillow's estimates are a first pass on pricing fairness before a call. In fast markets, Opendoor's algorithmic pricing sets timelines with little haggling. Research-heavy buyers use Flyhomes' AI to ask questions they might avoid with a human.
The pushback: context still wins deals
Not everyone is sold. Some agents argue these tools simplify a process built on people, psychology, and hyperlocal intel that never hits public records. Miami broker Ivan Chorney puts it bluntly: real buyer representation is about motivation, pressure, ego, and timing-the hidden context that makes or breaks a deal.
What this means for real estate and construction pros
- Value proposition shifts: Buyers who self-represent expect fees to track clear value. Speed, access, and negotiation skill are the differentiators that justify compensation.
- Listing strategy: Expect more unrepresented buyers. Clarify commission instructions in MLS remarks and offer docs. Plan for buyer credits or price offsets tied to representation status.
- Deal velocity: Same-day offers compress response times. Tighten SLA on inquiry-to-tour and tour-to-offer to stay competitive.
- Risk controls: Unrepresented buyers raise disclosure, fair housing, and contract-quality risks. Use checklists and offer-intake frameworks to reduce fallout.
- Builder sales: Direct-to-buyer traffic may rise. Have a clean path for credits, rebates, or upgrades when no buyer agent is involved-without violating advertising or inducement rules.
- Commission capture: If your market relies on concessions to close, codify how credits are disclosed and approved to avoid disputes at CD time.
A practical playbook for brokerages, teams, and builders
- Codify pre-tour steps: Use simple, plain-language buyer agreements and scripts explaining compensation options.
- Upgrade intake: Add AI-augmented pricing comps and offer-ready docs so buyers can move the same day.
- Tighten offer hygiene: Standardize terms, proof of funds, inspection timelines, and appraisal gaps for cleaner acceptance.
- Negotiation edge: Train agents on reading seller psychology and local context-what AI misses-so your offers stick.
- Protect credits: Specify in writing how any buyer-side commission or credit is allocated (rate buydown, closing costs, price).
- Tool stack: Instant tour scheduling, e-sign, templated clauses, and prompt libraries for comps and contract drafting.
- Team enablement: Build AI fluency so staff can move faster without losing accuracy. If you need structured training, see AI courses by job role.
Metrics to watch this quarter
- Share of self-represented buyers contacting your listings
- Inquiry-to-tour and tour-to-offer conversion time
- Fallout rate tied to contract errors or missing disclosures
- Double-ending frequency vs. buyer-credit utilization
- Price-to-list ratio on AI-assisted offers vs. traditional
- Concessions mix (rate buydowns, closing credits, price)
Inside the Homa pitch
Arman Javaherian, a former senior product leader at Zillow and Homa's co-founder, says the platform is built for buyers who already do the legwork: search online, find homes, compare comps. The promise is speed and clarity-tour fast, price with data, and send clean offers without waiting on someone's calendar.
He argues the industry doesn't need manual process for every step anymore. Whether that holds at scale will depend on how these offers perform against well-prepped, agent-led submissions in tight inventory.
Bottom line
AI is compressing the buy-side workflow and pressuring the middle of the fee stack. Expect a mixed model: self-represented buyers on straightforward deals and seasoned agents winning complex ones where context and negotiation drive outcomes.
The winners will pair data speed with human judgment. Move fast, but don't skip the local intel that keeps deals together at the closing table.
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