Setup checklist for a real estate AI receptionist

Deploying an AI receptionist for a real estate team takes about a week of elapsed time, with the bulk of the work going into MLS integration, team roster configuration, and building out neighborhood knowledge that makes the AI sound like a local expert.

Before the kickoff call

• CRM / platform — Follow Up Boss, Chime, kvCORE, BoomTown, Real Geeks, LionDesk, Top Producer, or similar (with admin credentials)

• MLS access — MLS login or IDX feed credentials for real-time listing data

• Team roster — agents, their specialties (buyer / seller / both, luxury / first-time / investor), territories, calendars

• Listing portfolio — current active listings and their key details

• Service area — zip codes, neighborhoods, cities you serve

• Neighborhood knowledge base — for each major neighborhood: school district, commute, typical price range, key amenities, HOA communities

• Showing rules — showing windows per agent, drive-time preferences, lockbox/key access methods

• Qualification criteria — what makes a "motivated" buyer in your market (pre-approved, specific timeline, etc.)

• Seller lead routing — which agent or listing coordinator gets seller leads

• Top 20–30 FAQs

Decisions you'll need to make

• Voice — Nova (warm, enthusiastic, knowledgeable) is our default for real estate

• Disclosure — we recommend proactive disclosure

• After-hours coverage — AI all hours including evening / weekend (when most real estate calls actually come in)

• Seller lead handling — direct transfer to a listing agent, or appointment booking, or both depending on time

• Buyer qualification depth — how many questions to ask before transferring vs. book appointment for agent to qualify

• Fair housing guardrails — explicit list of topics the AI should not characterize (demographics of neighborhoods, typical buyers, etc.)

• Rental inquiry handling — do you service rentals, or decline / refer

MLS / IDX integration

• Authorize IDX feed access for real-time listing data

• Confirm listing data refresh frequency (ideally every 15 minutes or better)

• Map MLS fields to the AI's listing-answer templates

• Set up listing-agent assignment (so the AI books showings on the correct person)

• Configure coming-soon, pending, and sold status handling

Fair housing considerations

• The AI should never characterize the demographics of a neighborhood

• The AI should not steer callers based on protected characteristics

• Neighborhood answers should be factual (schools, commute, amenities) — not opinion-based ("family-friendly," "up-and-coming," etc. depending on context)

• Your team should review the AI's standard neighborhood responses with an eye toward fair housing compliance before go-live

• The AI can recommend specific agents for specific territories but should not imply demographic preferences

Team roster configuration

• For each agent: name, territories, specialties, showing availability, seller-vs-buyer split

• Calendar integration for each agent (Google or Outlook)

• Round-robin rules for new buyer leads if no specific territory match

• Escalation path when the assigned agent is unavailable

Neighborhood knowledge base

• One-page factsheet per major neighborhood you serve

• School district info (factual — district name, elementary/middle/high school names)

• Commute info (time to downtown, nearest highway access)

• Price range (MLS-derived median/range, not opinions)

• HOA info for communities with HOAs

• Key factual amenities (parks, shopping, transit)

Call forwarding

• Current phone system — setup guides in our Call forwarding collection

• Forwarding rules — always, after-hours, overflow

• Urgent escalation for motivated buyers — direct line or SMS + call

Go-live sequence

• Day 1: kickoff, requirements, MLS integration, team roster setup

• Day 2–3: neighborhood knowledge base loaded, showing rules configured

• Day 3–5: internal test calls, listing scenario testing

• Day 5–7: soft launch (after-hours first), full rollout

• Weeks 2–4: refinement based on real call patterns; MLS listing updates tested

What to measure after go-live

• Speed-to-lead — time from first inquiry to first agent contact

• Showing booking rate

• Motivated buyer capture

• Seller lead capture

• Fair housing review — sample transcripts to confirm neighborhood answers stay factual

• Lead qualification accuracy

Related articles

• How 365agents works for real estate teams

• Common calls a real estate AI handles

• Can AI really book appointments?

Start the process

Book a demo and our team will walk through this checklist, configure a live Residential Agent against your MLS feed, and give you a clear go-live plan.