Real-estate Listings (JSON, 30 records)
The property listings as a JSON array — the format twin of the CSV, for import and mapping tests.
[
{
"listing_id": "L0001",
"address": "8769 Oak St",
"city": "Denver",
"state": "CO",
"type": "apartment",
"price": 1187000,
"beds": 3,
"baths": 3.0,
"sqft": 1608,
"year_built": 1987,
"latitude": 39.75292,
"longitude": -105.05308,
"status": "for_sale"
},
{
"listing_id": "L0002",
"address": "3795 Pine St",
"city": "Portland",
"state": "OR",
"type": "condo",
"price": 1059000,
"beds": 1,
"baths": 1.5,
"sqft": 2998,
"year_built": 1978,
"latitude": 45.51004,
"longitude": -122.61966,
"status": "sold"
},
{
"listing_id": "L0003",
"address": "9786 Ash St",
"city": "Portland",
"state": "OR",
"type": "condo",
"price": 262000,
"beds": 3,
"baths": 2.5,
"sqft": 3520,
"year_built": 1955,
"latitude": 45.48338,
"longitude": -122.6642,
"status": "for_sale"
},
{
"listing_id": "L0004",
"address": "7319 Willow St",
"city": "Austin",Specifications
- Records
- 30
- Schema
- listing_id, address, city, state, type, price, beds, baths, sqft, year_built, latitude, longitude, status
- Domain
- real estate
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Real-estate Listings (JSON, 30 records)” is a deterministic Novus Examples fixture for Data import, Geospatial, Conversion testing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 30 records · schema: listing_id, address, city, state, type, price, beds, baths, sqft, year_built, latitude, longitude, status. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("listings.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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- gpkgGeoPackage — Three Feature Layers in One SQLite ContainerThe territory as an OGC GeoPackage: an ordinary SQLite database with the three required metadata tables and three feature layers whose geometry columns hold GeoPackage binary blobs. Unlike a shapefile it carries several geometry types, long column names and real typed columns in one file.
- gpxGPX — Route (rte) Rather Than a TrackA `rte` element: an ordered list of waypoints describing a planned route, with no timestamps because it has not been travelled yet. Many importers only look for `trk` and report this perfectly valid file as empty.
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Generated by generation/data_domains.py. Free for any use, no attribution required — license.