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Information architecture

RentalAnalytics is organized around top-level sections. Calculators do the work. The tax hub is educational literacy, kept apart from market math. Markets and Dashboard surface the data. Blog explains the methodology. AI Agents, MCP, and the Muse connector expose everything as tools an agent can call. Legal makes the trust posture explicit.

Top-level navigation

Tools (8 pages)

Live today (8):

Tax literacy (educational, client-side)

Not tax prep, not e-file, not a 1031 qualified intermediary. Worksheets run in the browser from user-typed numbers and do not require a live HUD fetch.

Games

  • Cap Rate — five-hand underwriting table. Buy HUD FMR, vacancy, OpEx, and market cap, then bid closest to true value without going over. Challenge links use ?t= seed.

Markets — city reports (15)

City pages live under /markets/<city-slug>.html. Each follows the template documented in scripts/gen-city-pages.js.

    Template covers: H1 with city + HUD FY2026 FMR vintage, summary KPIs by bedroom, YoY growth, vacancy, avg cap rate, inline prefilled rent estimator, market analysis block, 5-question FAQ with FAQPage schema, regulation snapshot, related cities, related blog posts, Dataset + FAQPage JSON-LD.

    Resource center (5 starter articles)

    MCP API (1 docs page + 7 endpoints)

    • LandlordIQ docs
    • GET /manifest.json
    • GET /get_market_rent
    • GET /get_vacancy_rate
    • GET /get_rent_growth
    • GET /get_cap_rate_benchmark
    • GET /get_lease_renewal_recommendation
    • GET /get_landlord_market_summary
    • GET /get_regulations

    AI agents & machine-readable files

    RentalAnalytics is built so an AI agent or MCP client can consume it directly. Playbooks pair prompt templates with copyable API/MCP calls; the machine-readable files let an agent discover tools without reading the HTML.

    Legal & trust

    Datasets exposed at /assets/data/

    Crawler-facing