agent_id int64 1 10 | name stringclasses 10
values | brokerage stringclasses 5
values | years_experience int64 1 17 | specialty stringclasses 4
values |
|---|---|---|---|---|
1 | Allison Hill | Lake & Pine Realty | 15 | condo |
2 | Noah Rhodes | Cedar Key Residential | 17 | family-homes |
3 | Angie Henderson | Cedar Key Residential | 16 | family-homes |
4 | Daniel Wagner | Summit Street Group | 5 | luxury |
5 | Cristian Santos | Summit Street Group | 8 | investor |
6 | Connie Lawrence | Anchor MLS Partners | 10 | luxury |
7 | Abigail Shaffer | Lake & Pine Realty | 11 | investor |
8 | Gina Moore | Cedar Key Residential | 1 | luxury |
9 | Gabrielle Davis | Harbor West Homes | 12 | family-homes |
10 | Ryan Munoz | Lake & Pine Realty | 4 | family-homes |
Real Estate Listings (Synthetic) (Free Sample)
This is a free sample with 2,020 rows. The full dataset has 39,989 rows across 5 tables.
Mid-market residential property listings from a simulated US metro area spanning two years of listing activity, agent assignments, neighborhood characteristics, and price-history updates. Includes detached homes, condos, townhomes, and multifamily inventory with location-based pricing, seasonality, and listing lifecycle events that look like real MLS data.
Spring listing peaks and a market correction in Q3 of year 2 are encoded in the pricing and update patterns. Useful for PropTech demos, housing-market analytics, valuation experiments, lead-routing systems, and marketplace UX.
Sample tables
| Table | Sample Rows |
|---|---|
| agents | 10 |
| listings | 1,000 |
| neighborhoods | 10 |
| properties | 1,000 |
| Total | 2,020 |
Full dataset
The complete dataset includes all tables with full row counts:
| Table | Full Rows |
|---|---|
| agents | 48 |
| listings | 8,000 |
| neighborhoods | 10 |
| price_history | 23,931 |
| properties | 8,000 |
| Total | 39,989 |
Formats included: CSV, Parquet, SQLite
Get the full dataset on Gumroad
About
Generated by Mindweave Technologies -- realistic synthetic datasets for developers, QA teams, and data engineers.
Every dataset features:
- Enforced foreign key relationships across all tables
- Realistic statistical distributions (not uniform random)
- Temporal patterns (seasonal, time-of-day, day-of-week)
- Injected anomalies for ML training and anomaly detection
- Deterministic generation (same seed = same output)
Browse all datasets: https://mindweavetech.gumroad.com
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