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.
Learn how to interact with this dataset using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Get dataset metadata including name, visibility, description, and other asset properties.
import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
dataset_id = "01a05dce-ae5c-704c-8619-c0b6f3446375"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| address_line | text |
| address_line2 | text |
| attribution_text | text |
| baths | real |
| beds | real |
| brokerage_name | text |
| city | text |
| county_fips | text |
| created_at | timestamp with time zone |
| detail_url | text |
| has_3d_tour | bigint |
| has_video_tour | bigint |
| has_virtual_tour | bigint |
| home_type | text |
| id | uuid |
| is_coming_soon | bigint |
| is_contingent | bigint |
| is_foreclosure | bigint |
| is_new_construction | bigint |
| is_new_listing | bigint |
| is_pending | bigint |
| is_price_reduced | bigint |
| latitude | real |
| ldp_slug | text |
| list_date | timestamp with time zone |
| list_price | real |
| listing_id | real |
| longitude | real |
| lot_sqft | real |
| photo_count | real |
| postal_code | text |
| price_max | text |
| price_min | text |
| price_prefix | text |
| price_reduced_amount | real |
| price_reduced_label | text |
| primary_photo_url | text |
| property_id | bigint |
| region_id | text |
| sqft | real |
| state_code | text |
| status | text |
| status_dot_color | text |
| status_text | text |
| updated_at | timestamp with time zone |
Fetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.
Update dataset metadata (visibility, description, etc.) and optionally write new rows to the table. Writing new data will replace the existing data in the table. Requires write or admin permission on the dataset.
# Get column definitions for the underlying table
columns = ouro.datasets.schema(dataset_id)
for col in columns:
print(col["column_name"], col["data_type"]) # e.g., age integer, name text# Option 1: All rows as a Pandas DataFrame
df = ouro.datasets.query(dataset_id)
print(df.head())
# Option 2: Read-only SQL — pass a query string; use {{table}} as the placeholder
agg = ouro.datasets.query(
dataset_id,
"SELECT col, count(*) AS n FROM {{table}} GROUP BY col ORDER BY n DESC",
)import pandas as pd
# Update dataset metadata
updated = ouro.datasets.update(
dataset_id,
visibility="private",
description="Updated description"
)
# Update dataset data (replaces existing data)
data_update = pd.DataFrame([
{"name": "Charlie", "age": 33},
{"name": "Diana", "age": 28},
])
updated = ouro.datasets.update(dataset_id, data=data_update)