How AI represents the brokers buyers find.
When a buyer asks an AI engine about a market, a neighborhood, or a property platform, who does it recommend?
The current state of AI visibility in real estate.
Buyers, sellers, and investors use AI engines for market intelligence, brokerage comparison, and location-specific recommendation. Engines weight local-press coverage, MLS authority signals, and platform integrations more than national brand. Geographic specificity is the dominant determinant of how a brokerage or platform surfaces.
Buyer and investor decisions moved into AI answers.
Buyer leads originate increasingly from AI metro queries. Metro-level visibility is now a measurable lead-generation channel.
Mis-attributed neighborhood or listing characteristics carry fair-housing risk. Brokerages remain responsible for how their content is restated.
Local-press coverage and MLS authority outweigh national brand. Confidence in AI framing tracks local-press depth.
Star-agent attraction filters brokerages through AI search. Visibility-thin brokerages compress agent recruiting pipelines.
The categories that carry the most decision weight here.
All ten AIVS measurement categories apply to every sector — that is what makes scores comparable across industries. But in real estate, six categories carry disproportionate decision weight. We emphasize these in your dashboard, your reporting, and your competitive analysis.
The contribution model is proprietary. Sector emphasis is editorial — we tell you which categories matter most for real estate decision-makers and why. We do not publish the numeric weighting.
How often you surface in metro-level and segment-level buyer questions. Local presence is the geographic version of share-of-voice.
See your AI Presence evidence →When you surface in buyer questions, the strength of the framing. 'Top-rated' converts; 'available' does not.
See your Recommendation Strength evidence →Which sources AI cites for your market position — local press and MLS authority outweigh paid placements.
See your Citation Authority evidence →How accurately AI resolves your brokerage — agents, markets served, transaction history.
See your Entity Authority evidence →Your win rate against named brokerages when buyers ask AI 'who should I work with in {market}'.
See your Competitive Position evidence →Breadth of metro, segment, and price-band questions where you surface. Coverage gaps map to expansion targets.
See your Prompt Coverage evidence →Why these categories matter more here.
Real estate visibility is geographic before it is anything else. AI engines answering 'who should I work with in {market}' lean on local-press citation, MLS authority, and platform integrations — the same sources matter very differently at the metro, neighborhood, and price-band levels. Coverage breadth across these geographic dimensions is more diagnostic than any single market score, which is why prompt coverage matters more here than in other sectors.
Industry authority compounds locally. National rankings — RealTrends, T3 Sixty — set baseline credibility; local press and MLS records determine which name surfaces in which market. Brokerages strong nationally but thin locally are systematically under-recommended in the markets that matter most. We instrument both.
Patterns we observe at sector scale.
Across the six engines and millions of real estate prompts in our universe, recommendation behavior follows distinctive patterns. These are the patterns most consequential to how your institution surfaces.
Engines weight metro-level press coverage more than national brand in market-specific recommendations.
Evidence available to customers →Brokerages with strong Zillow, Redfin, or comparable platform presence are surfaced disproportionately in buyer-side queries.
Evidence available to customers →Engines often surface star agents in brokerage-level answers; brokerages can be under-credited for the visibility of their top agents.
Evidence available to customers →Luxury, mid-market, and starter-home recommendations diverge sharply — a single brokerage may be top-3 in one segment and absent in another.
Evidence available to customers →Brokerages entering new markets take 9–12 months to surface in metro-specific recommendations.
Evidence available to customers →What AI engines actually read about real estate.
Across our prompt universe, AI engines cite a recurring set of sources when answering real estate questions. We track these continuously and weight your visibility analysis to the sources that move the needle.
We do not publish the relative weights we assign to these sources — that is part of the proprietary engine. We do publish the sources, so you can audit which inputs we read.
| RealTrends rankings | National brokerage and team production rankings. | High |
| T3 Sixty | Brokerage influence and segment analysis. | High |
| MLS authority records | Listing-attribution and market-share signals. | High |
| Local-press coverage | Metro-level market and transaction reporting. | Medium |
| Zillow / Redfin profile | Platform-mediated visibility and review density. | Medium |
| Regional broker associations | Association directories and market reports. | Medium |
| Awards lists | Local and national industry awards. | Recurring |
| Self-published market reports | Brokerage-produced market intelligence. | Recurring |
Six ways real estate institutions go invisible in AI.
These are the patterns we encounter most often in real estate client engagements. Each maps to a specific category in your AIVS readout.
National brokerages with shallow local-press coverage in specific metros are systematically under-recommended in market-specific queries.
Brokerages without strong Zillow or Redfin profile authority are deprioritized in buyer-side AI surfaces, regardless of MLS share.
Star agents surface in brokerage-level answers without their brokerage receiving recommendation credit.
Brokerages strong in mid-market can be invisible in luxury or starter-home AI surfaces without dedicated segment evidence.
Engines take 9–12 months to surface brokerages in newly entered metros even when transaction volume is competitive.
Brokerages without distinctive local authority are described as 'available in the area' rather than recommended — language flatness costs lead volume.
Who AI is recommending in real estate, right now.
Sector ranking, refreshed hourly. Methodology, confidence, and movement transparent on every list.
| # | Organization | Score | Δ 7d | Strength |
|---|---|---|---|---|
| 01 | Real estate brand A | 918 | -5 | |
| 02 | Real estate brand B | 908 | -2 | |
| 03 | Real estate brand C | 898 | +0.1 | |
| 04 | Real estate brand D | 888 | +1.2 | |
| 05 | Real estate brand E | 878 | +2.1 | |
| 06 | Real estate brand F | 868 | +3.8 | |
| 07 | Real estate brand G | 864 | -4.3 | |
| 08 | Real estate brand H | 854 | -1.2 | |
| 09 | Real estate brand I | 844 | +1.2 | |
| 10 | Real estate brand J | 834 | +2.5 |
Where real estate competitors win — and how to close the gap.
Every loss to a competitor in an AI answer maps to one of six gap categories. We surface the category, the evidence, and the prioritized remediation.
Peer surfaced in a metro-level question; you did not.
Both surfaced — peer was 'top-rated'; you were 'available in the area'.
Engines cited peer's local-press and Zillow profile — not yours.
Engines describe the peer with stronger market-leader language.
RealTrends and T3 Sixty favor the peer in your segments.
Engines do not yet reflect your new-market entry.
Three real estate playbooks our research team runs.
Construct metro-specific authority signals in target markets.
Read playbook brief →Close coverage gaps across luxury, mid-market, and starter price bands.
Read playbook brief →Capture agent-level visibility as brokerage authority.
Read playbook brief →What our real estate research team published this quarter.
Engine-divergence is highest at the metro level — local-press density is the dominant predictor of which engine recommends which firm.
Brokerages entering new metros do not surface for nearly a year, regardless of transaction volume.
Mid-market brokerages frequently invisible in luxury or starter-home questions; segment-specific evidence required.
Quarterly intelligence built for real estate leadership.
Metro-by-metro benchmark across the top 60 US markets with segment-band breakdowns for luxury, mid-market, and starter-home AI surfaces.
Request the report →Metro and segment movement across 281 tracked firms and platforms.
Request →Catalog of mis-attributed listings, neighborhoods, and agent affiliations.
Request →Brokerage-scoped remediation by metro and segment band.
Request →How the AIVS methodology applies in real estate.
The AI Visibility Score is calculated identically across every sector — same ten categories, same composite scale, same governance. What differs in real estate is which categories carry more decision weight, which evidence sources we monitor most heavily, and which patterns we surface as priority remediation. This page is our editorial mapping. The underlying methodology is published at our Methodology page.
Sector emphasis is editorial — we do not publish numeric weighting per sector. The contribution model is part of the proprietary engine.
Real Estate questions we answer most often.
The real estate research roadmap.
Sector intelligence improves continuously. These are the rankings, reports, and analyses scheduled for release.
Brokerage and platform recommendation breakdown by metro.
Dedicated luxury-segment recommendation ranking.
How AI engines describe proptech platforms vs traditional brokerages.