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Real-estate Listings (JSON, 30 records)

The property listings as a JSON array: the format twin of the CSV, for import and mapping tests.

Data importGeospatialConversion testing
Preview, first 50 linesjson
[
  {
    "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",
… 453 lines total: download for the full file.

Specifications

Records
30
Schema
listing_id, address, city, state, type, price, beds, baths, sqft, year_built, latitude, longitude, status
Domain
real estate

Testing contract

Expected to pass
Scenario
Exercise Real-estate Listings (JSON, 30 records) in its real estate workflow. The property listings as a JSON array: the format twin of the CSV, for import and mapping tests.
Expected result
array length is 30; first-record keys are listing_id, address, city, state, type, price, beds, baths, sqft, year_built, latitude, longitude, status. Declared feature checks: 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 Testaroo 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))

Generated by generation/data_domains.py. Free for any use, no attribution required, license.

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