How We Rank
Last updated: June 21, 2026
We help real estate professionals in the US and Canada — particularly team leaders and established listing agents — identify AI tools that deliver real return on investment. This page explains exactly how we evaluate, score, and update our recommendations.
Who we evaluate for
Our analysis prioritizes the needs of working agents: team leaders managing 3 to 15 agents who need scalable solutions, and top-producing solo agents who value time over the cheapest option. Tools that only suit hobbyist agents or non-real-estate use cases are excluded from our recommendations, even when they're popular in general AI rankings.
Our scoring criteria
Each tool gets a single score out of 100 — our editorial verdict on how well it serves a working real estate team. To show where that verdict comes from, we break it into six weighted dimensions, chosen because they reflect what actually matters on the job. The table below is that breakdown, not a formula: each weight is the most a dimension can add to the total.
| Criterion | Weight | What we look at |
|---|---|---|
| Real estate specialization | 20% | Is the tool built for real estate workflows, or is it a generic AI repurposed? Does it understand MLS data, listings, and agent terminology? |
| Time saved & team scalability | 20% | How many hours per week does it save a typical agent? Does it support multiple users, team workflows, and role-based access? |
| Pricing transparency & value | 20% | Are prices public? Are there hidden fees, annual lock-ins, or per-seat surprises? How does cost-per-lead or cost-per-listing compare? |
| Ease of use & onboarding | 15% | Can a team adopt it within a week, or does it require a paid consultant? Quality of the onboarding experience for non-technical users. |
| Customer support & training | 15% | Live support availability, response time, training library quality, dedicated success manager on team plans. |
| Integrations & ecosystem | 10% | Native integrations with major CRMs (Follow Up Boss, Sierra Interactive, kvCORE), MLS systems, and Zapier. |
Two of these dimensions can't be judged fairly from a vendor's own marketing — ease of use & onboarding, and customer support & training. We weigh both primarily from recurring themes in independent user reviews on G2 and Capterra, read for what keeps coming up rather than for an average star rating. Together they can move the score by up to 30 points out of 100.
Our AI stack finder starts from what you tell us you need — not from these scores. You describe your business, and our code, not the AI, matches each tool to your needs; the REAT Score only breaks ties between tools that fit you equally well. The AI only reads what you wrote; it never picks the tools. Try it →
What the REAT Score means
The REAT Score is a single number out of 100: our editorial rating of how well a tool serves working real estate teams in the US and Canada. It is our own judgment — not a user rating, and not an average of star reviews. The six weighted dimensions above are how we break that judgment down and show our reasoning; they aren't a calculation that runs behind the number. Two tools with the same score can get there differently — which is why the breakdown, and the review itself, matter more than the number alone.
- 80–100 — Top pick · A tool we recommend with confidence for its category: strong value, few serious drawbacks.
- 65–79 — Solid pick · A capable tool that does its job well, with trade-offs worth weighing before you commit.
- Below 65 — Situational pick · Useful for a specific need, with notable limits. Read the "Not for" list before you sign up.
On each review, the score badge is shaded to match these bands rather than colour-coded: the higher the band, the more solid the badge.
What we count as AI
- AI-native — the product only exists because of AI. Remove the model and there is no product.
- AI-enhanced — an established product with AI features added. The AI helps; the product works without it.
- AI wrapper — a thin layer on top of a third-party model, with little product of its own.
- Automation — rules and workflows marketed as AI. Useful, but not AI.
We label every tool, because “AI-powered” on a pricing page tells you nothing.
Our research process
We don't claim to test every tool hands-on for hundreds of hours — that wouldn't be realistic for an independent editorial site, and we believe transparency about our method matters more than overpromising.
Our analysis combines:
- the vendor's own product documentation and public pricing pages
- the published terms of the vendor's partner and affiliate programmes
- independent user reviews on software directories such as G2 and Capterra, read for recurring themes rather than average scores
- our own 12-month price projections, built only from the vendor's published rates — shown with the arithmetic, the date checked, and a link to the vendor's pricing page. When a vendor does not publish its prices, we say so and project nothing; we never substitute a third-party estimate.
When data is unavailable or contradictory, we say so in the relevant review rather than guess.
Editorial independence
Some of the tools we recommend pay us a commission when readers sign up through our links — see our Affiliate Disclosure. This affects which programs we apply to; it does not affect our ranking, our scoring, or our willingness to flag a weakness.
Concretely: every tool is evaluated on the same six criteria above. We do not boost a tool's score because it pays a higher commission. We do not exclude a strong tool because it doesn't have an affiliate program. If a tool stops meeting our standards, we remove it from our recommendations, regardless of revenue impact. See how we make money for the full funding rules.
How often we update
We review our rankings every six months, or sooner if a major product change, pricing shift, or new market entrant warrants it. Each review page shows the date of its last update.
Questions about our method?
We welcome scrutiny. Email us at realestai@outlook.com and we'll respond.
Represent this tool? If something here is inaccurate, tell us — we re-check every claim against the vendor's own sources and correct it, with the date of the change.