README.md

---
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

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

Or with the `datasets` library:

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

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

```sql
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.