ziadatalabs/FreeRealEstate100M

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 pricepersqft genuinely track the features — a model can actually learn "bigger, better-located, better-condition homes cost more" instead of memorizing noise.

Schema

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.