Realtors datasets with 400,000+ US residential property listings from Realtor.com, covering all 50 states and Washington D.C. Listing-level records, cleaned, typed and deduplicated — ready to load into pandas, Postgres, BigQuery or Excel.
Schema (45 columns)
IDs: property_id, listing_id, region_id, ldp_slug, detail_url Location: address_line, address_line2, city, state_code, postal_code, county_fips, latitude, longitude Pricing: list_price, price_min, price_max, price_prefix, price_reduced_label, price_reduced_amount Attributes: home_type, beds, baths, sqft, lot_sqft Status: status, status_text, status_dot_color, list_date, created_at, updated_at Flags: is_new_listing, is_price_reduced, is_pending, is_contingent, is_foreclosure, is_new_construction, is_coming_soon Media: primary_photo_url, photo_count, has_3d_tour, has_video_tour, has_virtual_tour Source: brokerage_name, attribution_text, raw (full JSONB payload)
Prices are numeric(14,2), baths numeric(4,1), timestamps timezone-aware, booleans default false — no type coercion needed on import.
Use cases
AVM and price-prediction models — beds, baths, sqft, lot size and precise lat/long give a usable feature set immediately Distressed-property screening via is_foreclosure and price_reduced_amount New-supply tracking via is_new_construction and is_coming_soon Clean joins to Census, HUD, FEMA and BLS data through county_fips — no fuzzy string matching Brokerage market-share analysis by metro or state Listing-quality research: photo_count and tour flags vs. days on market Geospatial dashboards in PostGIS, Kepler or Mapbox
Built for proptech teams, ML engineers, real estate investors and housing researchers who need listing-level granularity at national scale.