README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - tabular-regression
  - tabular-classification
language:
  - en
tags:
  - synthetic
  - test-data
  - real-estate
  - housing
  - price-prediction
  - tabular
size_categories:
  - 100M<n<1B
pretty_name: Free Synthetic Real Estate Listings (100M)

Free Synthetic Real Estate Listings — 100M Rows

A free, fully synthetic dataset of 100,000,000 US real estate listings, generated for developers and builders working on price-prediction models, real-estate analytics, search/filter UIs, and BI pipelines — realistic property data without touching any real listing, address, or owner.

Every value in this dataset is artificially generated. No real properties, no scraped listings, no real addresses or PII. What makes it useful: listing prices are computed honestly from square footage × a metro-level price-per-square-foot, then adjusted for property type, condition, and age. So price and price_per_sqft genuinely track the features — a model can actually learn "bigger, better-located, better-condition homes cost more" instead of memorizing noise.

Schema

Column	Type	Description
listing_id	string	Unique listing identifier
city	string	US city (40 major metros)
state	string	US state abbreviation
zip_code	string	5-digit postal code (synthetic)
property_type	string	single_family, condo, townhouse, multi_family, or land
bedrooms	int32	Number of bedrooms (0 for land)
bathrooms	float32	Number of bathrooms (0 for land)
square_footage	int64	Interior square footage (0 for land)
lot_size_sqft	int64	Lot size in square feet
year_built	int32	Year constructed
condition	string	excellent, good, fair, or needs_work
garage_spaces	int32	Number of garage spaces
hoa_fee_monthly	int64	Monthly HOA fee in USD (0 if none)
listing_price	int64	List price in USD
price_per_sqft	float64	Derived price per square foot
days_on_market	int64	Days the listing has been active
listing_status	string	active, pending, sold, or off_market

Format

• Apache Parquet, Snappy compression
• One file, ~2 GB, 100,000,000 rows
• Loads cleanly with pandas, polars, DuckDB, PyArrow, or the datasets library

Quick start

import pandas as pd
df = pd.read_parquet("realestate_100M.parquet")
print(df.head())

Or with the datasets library:

from datasets import load_dataset
ds = load_dataset("ziadatalabs/FreeSyntheticRealEstate100M")

Or with DuckDB (great for querying without loading it all into memory):

SELECT city, avg(price_per_sqft) AS avg_ppsf
FROM 'realestate_100M.parquet'
WHERE square_footage > 0
GROUP BY city
ORDER BY avg_ppsf DESC;

Notes

• All data is synthetic and generated programmatically. Any resemblance to real properties, addresses, or listings is coincidental.
• Prices are derived from square footage and a metro-level price-per-sqft, adjusted for property type, condition, and age — so listing_price and price_per_sqft correlate with the underlying features rather than being random.
• Metro price levels reflect realistic relative differences (coastal/high-cost metros run higher per square foot than inland/low-cost ones), giving location a real signal.
• land listings have zero interior square footage and are priced off lot size.

License & Usage

Released under CC BY-NC 4.0 — free for personal, research, and educational use, with attribution, no commercial use. See the license for details.

Published by Zia Data Labs. We create synthetic data — and we give some of it away free, because good test data shouldn't be hard to find.

Want more free datasets? Hit the ❤️ and follow. And we take requests — tell us what synthetic data you need, and we'll build it. Reach us at zia.data.team@protonmail.com.