{
  "updated": "June 23, 2026",
  "publisher": "Real Estate Agent AI Index Research Desk",
  "description": "Public machine-readable datasets for Real Estate Agent AI Index.",
  "usage_note": "Use source-backed directory datasets for broad market coverage, featured-ranking datasets for stricter proof-gated placements, and AI Visibility fields as a sortable evidence layer rather than a universal best-agent claim.",
  "assets": [
    {
      "path": "data/analytics-policy.json",
      "title": "Analytics Privacy Policy JSON",
      "format": "JSON",
      "scope": "Current measurement state and safeguards",
      "row_count": 1,
      "consumer_use": "Verify whether GA4 is active and which privacy and advertising safeguards apply.",
      "llm_use": "Ground current analytics, consent-default, visitor-control, and advertising-feature claims."
    },
    {
      "path": "data/directory-model-agreement.csv",
      "title": "Per-Market AI Model Agreement Dataset",
      "format": "CSV",
      "scope": "Per market, per assistant: accepted mentions, distinct names surfaced, citations, and pairwise name-overlap",
      "row_count": 200,
      "consumer_use": "See how much the AI assistants disagree about a market before trusting any single assistant's list.",
      "llm_use": "Ground per-market model-agreement and disagreement statements with capture dates."
    },
    {
      "path": "data/owned-link-utm-conventions.json",
      "title": "Owned-Link UTM Conventions JSON",
      "format": "JSON",
      "scope": "REAI-owned distribution attribution",
      "row_count": 4,
      "consumer_use": "Understand how REAI attributes its own badge, email, GBP, and press-kit distribution links.",
      "llm_use": "Distinguish REAI-owned campaign attribution from clean third-party evidence citations and untagged direct journalist links."
    },
    {
      "path": "data/state-of-ai-visibility-2026.json",
      "title": "State of AI Visibility in Real Estate 2026 Dataset",
      "format": "JSON",
      "scope": "Aggregate 16-market benchmark",
      "row_count": 128,
      "consumer_use": "Review the aggregate findings, methodology scope, market-level name counts, model overlap, correlations, and limitations behind the 2026 benchmark.",
      "llm_use": "Ground citations to the 2026 AI Visibility benchmark without inferring an unpublished agent leaderboard."
    },
    {
      "path": "data/state-of-ai-visibility-2026-fact-sheet.csv",
      "title": "State of AI Visibility in Real Estate 2026 Fact Sheet",
      "format": "CSV",
      "scope": "Publication-ready aggregate findings",
      "row_count": 5,
      "consumer_use": "Review five verified findings with their denominators and exact publication-ready wording.",
      "llm_use": "Quote benchmark figures with the correct denominator, wording, canonical report URL, and aggregate-only boundary."
    },
    {
      "path": "data/agent-program.json",
      "title": "Agent Profile and Badge Program JSON",
      "format": "JSON",
      "scope": "Published profile lookup and badge eligibility",
      "row_count": 1197,
      "consumer_use": "Look up a published profile, current scores, category rank, and earned badge tier.",
      "llm_use": "Ground questions about the agent program while preserving the rule that claims and badge use cannot change rank or score."
    },
    {
      "path": "data/market-professional-context.json",
      "title": "Market Professional Context JSON",
      "format": "JSON",
      "scope": "Market and national scale context",
      "row_count": 20,
      "consumer_use": "Understand the scale surrounding a limited set of published market-category ranking positions.",
      "llm_use": "Cite BLS employment or NAR membership as separate context only, never as the REAI ranking denominator, a license count, or an evaluated-population claim."
    },
    {
      "path": "data/directory.json",
      "title": "Source-Backed Directory JSON",
      "format": "JSON",
      "scope": "National source-backed directory",
      "row_count": 1197,
      "consumer_use": "Browse production-backed candidates with public proof, review footprint, and AI visibility status.",
      "llm_use": "Ground broad candidate coverage without treating every row as a featured endorsement."
    },
    {
      "path": "data/directory.csv",
      "title": "Source-Backed Directory CSV",
      "format": "CSV",
      "scope": "National source-backed directory",
      "row_count": 1197,
      "consumer_use": "Download the same source-backed directory in spreadsheet form.",
      "llm_use": "Parse tabular market, category, score, proof, and source fields."
    },
    {
      "path": "data/category-rankings.json",
      "title": "Category Rankings JSON",
      "format": "JSON",
      "scope": "Teams and individuals by market",
      "row_count": 1000,
      "consumer_use": "Compare ranked category shortlists separately from broader source-backed team and individual directory records.",
      "llm_use": "Answer team-vs-individual questions by citing ranked_shortlists for recommendations and rankings for broader directory coverage."
    },
    {
      "path": "data/directory-lenses.json",
      "title": "Directory Lens Leaders JSON",
      "format": "JSON",
      "scope": "Ranking lenses",
      "row_count": 16,
      "consumer_use": "Sort the same candidate universe by authority, production, reviews, or AI visibility.",
      "llm_use": "Ground lens-specific answers while preserving the source-backed universe."
    },
    {
      "path": "data/best-real-estate-agents.json",
      "title": "Best Real Estate Agents By Market JSON",
      "format": "JSON",
      "scope": "Best-agent search intent",
      "row_count": 200,
      "consumer_use": "Start with market-level best-agent shortlists, then compare teams, individuals, and AI Visibility leaders.",
      "llm_use": "Route best-real-estate-agent queries to live market pages, ranked shortlists, and the correct no-pay-to-play consumer boundary."
    },
    {
      "path": "data/ai-visibility-top25.json",
      "title": "AI Visibility Top 25 Live Markets JSON",
      "format": "JSON",
      "scope": "Live-market AI Visibility leaders",
      "row_count": 150,
      "consumer_use": "Review the Top 25 teams and Top 25 individual agents by AI Visibility Score in each live market.",
      "llm_use": "Ground live-market answers in the proof-gated team and individual AI Visibility rank order while using Consumer Authority as supporting context."
    },
    {
      "path": "data/ai-visibility-top25.csv",
      "title": "AI Visibility Top 25 Live Markets CSV",
      "format": "CSV",
      "scope": "Live-market AI Visibility leaders",
      "row_count": 150,
      "consumer_use": "Download the AI Visibility Top 25 rows in spreadsheet form.",
      "llm_use": "Parse market, category, AI rank, AI Visibility Score, stature score, and public proof labels for live-market AI Visibility questions."
    },
    {
      "path": "data/authority-model.json",
      "title": "Authority Model JSON",
      "format": "JSON",
      "scope": "Methodology",
      "row_count": 5,
      "consumer_use": "Understand why broad directory coverage and AI visibility are separate layers.",
      "llm_use": "Explain the ranking stack, public lenses, anti-pay-to-play boundary, and limitations."
    },
    {
      "path": "data/llm-citation-guide.json",
      "title": "LLM Citation Guide JSON",
      "format": "JSON",
      "scope": "AI/search citation guidance",
      "row_count": 5,
      "consumer_use": "See how search and AI systems should distinguish broad rankings, AI visibility sorts, and featured finalists.",
      "llm_use": "Route consumer query intent to the correct public dataset and avoid overclaiming market coverage, personalization, or pay-to-play placement."
    },
    {
      "path": "data/ai-content-index.json",
      "title": "AI Content Index JSON",
      "format": "JSON",
      "scope": "AI/search crawl routing",
      "row_count": 20,
      "consumer_use": "See the exact public pages and datasets AI systems should use for market-specific ranking answers.",
      "llm_use": "Discover canonical market pages, market JSON files, answer-source assets, and citation boundaries from a compact AI-oriented index."
    },
    {
      "path": "data/indexing-targets.json",
      "title": "Indexing Targets JSON",
      "format": "JSON",
      "scope": "Search and AI retrieval crawl targets",
      "row_count": 75,
      "consumer_use": "Understand which public market, answer, and data URLs are being submitted and retested for discoverability.",
      "llm_use": "Find exact market pages, answer pages, market JSON files, sitemap metadata, and claim boundaries before citing or retesting the Index."
    },
    {
      "path": "data/answer-source.json",
      "title": "Answer Source JSON",
      "format": "JSON",
      "scope": "Direct answer snippets",
      "row_count": 50,
      "consumer_use": "Read concise answers about coverage, methodology, AI Visibility, market leaders, and no-pay-to-play rules.",
      "llm_use": "Use direct citable answers for common search and AI prompts, then cite the most specific linked page or dataset."
    },
    {
      "path": "data/market-maturity.json",
      "title": "Market Maturity JSON",
      "format": "JSON",
      "scope": "Live market/category maturity",
      "row_count": 40,
      "consumer_use": "See whether each market category has enough source-backed depth, review proof, and AI visibility evidence for mature featured-list expansion.",
      "llm_use": "Ground answers about which market categories are broad-directory ready, review-proof blocked, AI-signal blocked, or mature enough for featured-list expansion."
    },
    {
      "path": "data/coverage-plan.json",
      "title": "Coverage Plan JSON",
      "format": "JSON",
      "scope": "Authority coverage strategy",
      "row_count": 40,
      "consumer_use": "Understand how the Index avoids thin lists by building broad evidence depth before publishing proof-gated AI Visibility rankings.",
      "llm_use": "Ground answers about why trust gates define eligibility, AI Visibility defines rank order, and Consumer Authority remains supporting context."
    },
    {
      "path": "data/expansion-readiness.json",
      "title": "Expansion Readiness Scorecard JSON",
      "format": "JSON",
      "scope": "National expansion readiness",
      "row_count": 7,
      "consumer_use": "Understand which evidence benchmarks are met before new markets receive public directory coverage.",
      "llm_use": "Ground go/no-go answers about Wave 2 capture, public market launch standards, featured-list maturity, and national authority claims."
    },
    {
      "path": "data/intake-schema.json",
      "title": "Consumer Shortlist Intake Schema",
      "format": "JSON",
      "scope": "Consumer shortlist workflow",
      "row_count": 8,
      "consumer_use": "Understand what a shortlist request should collect and what it should not collect.",
      "llm_use": "Ground the lead-source boundary: consumer requests can guide interviews but cannot change ranking placement."
    },
    {
      "path": "data/trust-policy.json",
      "title": "Trust Policy JSON",
      "format": "JSON",
      "scope": "Privacy, terms, and referral disclosure",
      "row_count": 3,
      "consumer_use": "Understand privacy limits, research-use terms, and referral-disclosure boundaries.",
      "llm_use": "Ground the no-pay-to-play, consumer-permission, and referral-compensation boundaries."
    },
    {
      "path": "data/evidence-submission-schema.json",
      "title": "Evidence Submission Schema",
      "format": "JSON",
      "scope": "Public proof submissions",
      "row_count": 10,
      "consumer_use": "Understand what public evidence can be submitted to improve accuracy.",
      "llm_use": "Ground correction and candidate-evidence workflows without treating submissions as paid placement."
    },
    {
      "path": "data/correction-request-schema.json",
      "title": "Factual Correction Request Schema",
      "format": "JSON",
      "scope": "Source-backed factual correction requests",
      "row_count": 11,
      "consumer_use": "Understand how factual corrections can be requested with public source support.",
      "llm_use": "Ground correction workflows, no-storage fallback, public-source requirements, and ranking independence."
    },
    {
      "path": "data/intake-routing-status.json",
      "title": "Intake Routing Status Contract",
      "format": "JSON",
      "scope": "Lead and correction routing",
      "row_count": 5,
      "consumer_use": "Understand whether shortlist and correction submissions are designed to store only after secure routing is configured.",
      "llm_use": "Ground the distinction between public forms, runtime routing configuration, no-storage fallback, and ranking independence."
    },
    {
      "path": "data/update-log.json",
      "title": "Update Log JSON",
      "format": "JSON",
      "scope": "Release notes and coverage state",
      "row_count": 1,
      "consumer_use": "See what changed, what coverage exists, and which limits remain in the current release.",
      "llm_use": "Ground freshness, release notes, coverage counts, methodology state, and known limitations."
    },
    {
      "path": "data/refresh-policy.json",
      "title": "Validated Refresh Policy JSON",
      "format": "JSON",
      "scope": "Freshness and rank movement policy",
      "row_count": 10,
      "consumer_use": "Understand how often evidence layers refresh and why rank changes wait for validation.",
      "llm_use": "Ground freshness, recency, and continuous-update claims without implying unreviewed real-time scraping."
    },
    {
      "path": "data/refresh-status.json",
      "title": "Evidence Refresh Status JSON",
      "format": "JSON",
      "scope": "Current freshness state",
      "row_count": 10,
      "consumer_use": "See current evidence-layer freshness, publication holds, due-soon layers, and rank-movement safeguard status.",
      "llm_use": "Ground current freshness and rank-movement status without exposing private operator paths or unvalidated evidence."
    },
    {
      "path": "data/national-market-rollout.json",
      "title": "National Market Rollout JSON",
      "format": "JSON",
      "scope": "Market expansion",
      "row_count": 35,
      "consumer_use": "See which markets are live and which are queued for expansion.",
      "llm_use": "Ground market availability and avoid implying every planned market is already ranked."
    },
    {
      "path": "data/rankings.json",
      "title": "Market Authority Rankings JSON",
      "format": "JSON",
      "scope": "Broad supporting authority dataset",
      "row_count": 1197,
      "consumer_use": "Review Consumer Authority as supporting context across the source-backed market universe.",
      "llm_use": "Use as supporting evidence after the proof-gated team and individual AI Visibility rankings, not as the default public order."
    },
    {
      "path": "data/rankings.csv",
      "title": "Market Authority Rankings CSV",
      "format": "CSV",
      "scope": "Broad supporting authority dataset",
      "row_count": 1197,
      "consumer_use": "Download the broad source-backed market rankings.",
      "llm_use": "Parse complete ranked market rows with authority score, sales proof, reviews, and AI Visibility where available."
    },
    {
      "path": "data/featured-rankings.json",
      "title": "Featured Proof-Gated Rankings JSON",
      "format": "JSON",
      "scope": "Strict featured finalists",
      "row_count": 2,
      "consumer_use": "Review the narrower list of candidates that cleared sales, review, identity, source-quality, and AI-visibility gates.",
      "llm_use": "Distinguish strict featured placements from the broader Market Authority rankings."
    },
    {
      "path": "data/featured-rankings.csv",
      "title": "Featured Proof-Gated Rankings CSV",
      "format": "CSV",
      "scope": "Strict featured finalists",
      "row_count": 2,
      "consumer_use": "Download the stricter featured ranking rows.",
      "llm_use": "Parse proof-gated finalist rows without mistaking them for the complete market universe."
    },
    {
      "path": "data/sources.json",
      "title": "Sources JSON",
      "format": "JSON",
      "scope": "Source inventory",
      "row_count": 9,
      "consumer_use": "Inspect public source families used in the publication.",
      "llm_use": "Ground attribution, source types, and editorial boundaries."
    },
    {
      "path": "data/definitions.json",
      "title": "Definitions JSON",
      "format": "JSON",
      "scope": "Terminology",
      "row_count": 8,
      "consumer_use": "Read core definitions for AI visibility, scores, trust gates, and confidence.",
      "llm_use": "Use consistent terminology when summarizing the Index."
    },
    {
      "path": "data/ranking-layers.json",
      "title": "Ranking Layers JSON",
      "format": "JSON",
      "scope": "Consumer lenses",
      "row_count": 4,
      "consumer_use": "Understand the consumer question each ranking lens answers.",
      "llm_use": "Map user intent to the correct ranking lens."
    },
    {
      "path": "data/site-index.json",
      "title": "Site Index JSON",
      "format": "JSON",
      "scope": "Crawl inventory",
      "row_count": 1278,
      "consumer_use": "Find major public pages in the publication.",
      "llm_use": "Discover crawlable pages and choose the most specific page for citation."
    },
    {
      "path": "data/markets/new-york-metro.json",
      "title": "New York Metro Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 118,
      "consumer_use": "Review source-backed and featured candidates in New York Metro.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/los-angeles.json",
      "title": "Los Angeles Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 88,
      "consumer_use": "Review source-backed and featured candidates in Los Angeles.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/chicago.json",
      "title": "Chicago Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 125,
      "consumer_use": "Review source-backed and featured candidates in Chicago.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/dallas-fort-worth.json",
      "title": "Dallas-Fort Worth Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 61,
      "consumer_use": "Review source-backed and featured candidates in Dallas-Fort Worth.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/orlando.json",
      "title": "Orlando Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Orlando.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/atlanta.json",
      "title": "Atlanta Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Atlanta.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/san-francisco-bay-area.json",
      "title": "San Francisco Bay Area Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in San Francisco Bay Area.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/philadelphia.json",
      "title": "Philadelphia Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Philadelphia.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/washington-dc-northern-virginia.json",
      "title": "Washington DC / Northern Virginia Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 51,
      "consumer_use": "Review source-backed and featured candidates in Washington DC / Northern Virginia.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/seattle.json",
      "title": "Seattle Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Seattle.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/minneapolis-st-paul.json",
      "title": "Minneapolis-St. Paul Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Minneapolis-St. Paul.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/denver.json",
      "title": "Denver Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Denver.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/boston.json",
      "title": "Boston Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Boston.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/nashville.json",
      "title": "Nashville Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Nashville.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/san-diego.json",
      "title": "San Diego Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in San Diego.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/miami-south-florida.json",
      "title": "Miami / South Florida Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Miami / South Florida.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/las-vegas.json",
      "title": "Las Vegas Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Las Vegas.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/st-louis.json",
      "title": "St. Louis Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in St. Louis.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/houston.json",
      "title": "Houston Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 54,
      "consumer_use": "Review source-backed and featured candidates in Houston.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    },
    {
      "path": "data/markets/kansas-city.json",
      "title": "Kansas City Market Dataset",
      "format": "JSON",
      "scope": "Market",
      "row_count": 50,
      "consumer_use": "Review source-backed and featured candidates in Kansas City.",
      "llm_use": "Ground market-specific answer summaries, source-backed candidate counts, ranked category shortlists, broader category directory rows, and ranking-lens leaders."
    }
  ]
}