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Generate Real Estate Synthetic Data in Python

Real estate data has patterns that random generation misses: home prices follow a lognormal distribution with a heavy right tail (the $1M+ tier), days-on-market is lognormal with a median around 23 days, and agent ratings cluster toward 4–5 stars due to platform selection effects. Misata generates a three-table real estate dataset — agents, properties, and transactions — where these distributions are built in and FK relationships are always valid.

import misata

tables = misata.generate("A real estate agency with 500 properties and 50 agents", rows=500, seed=42)
print(list(tables.keys()))   # ['agents', 'properties', 'transactions']
print(tables["properties"][["price", "bedrooms", "status"]].describe())

What Misata generates

Three tables: agents (who manage listings), properties (listed inventory), and transactions (closed sales). Every property references a valid agent; every transaction references a valid property.

Tables and columns

Table Key columns
agents agent_id, name, email, agency, rating, listings_sold, years_experience
properties property_id, agent_id, address, city, state, price, bedrooms, bathrooms, sqft, listing_date, status
transactions transaction_id, property_id, buyer_name, sale_price, close_date, commission_rate, days_on_market

Realistic distributions

  • Home prices lognormal with US median ~$410k — realistic heavy right tail for luxury properties
  • Days on market lognormal with median ~23 days — fast movers and long-stale listings both present
  • Agent ratings beta-distributed toward 4–5 stars — reflecting real platform rating dynamics
  • ~60% of listings close — remainder are active, expired, or withdrawn
  • sqft and price are correlated — larger properties cost more

Quick start

import misata

tables = misata.generate("A real estate agency with 500 properties and 50 agents", rows=500, seed=42)

# Price distribution
print(tables["properties"]["price"].describe())
# Listing status breakdown
print(tables["properties"]["status"].value_counts())
# Commission revenue
transactions = tables["transactions"]
transactions["commission"] = transactions["sale_price"] * transactions["commission_rate"]
print(f"Total commission revenue: ${transactions['commission'].sum():,.0f}")

Common use cases

  • MLS / property search platform development — seed a test database with listings across price tiers and bedroom counts for search, filter, and sort testing
  • AVMs (automated valuation models) — generate training data with realistic price, sqft, bedrooms, and location features for regression model development
  • Agent performance analytics — build conversion rate, average days-on-market, and commission dashboards on realistic agent histories
  • CRM for real estate agents — test lead management and listing pipeline workflows with realistic property and transaction data
  • Proptech demo environments — replace real MLS data exports with synthetic equivalents for vendor demos and investor presentations
  • Market report generation — validate report templates and calculations against a full year of synthetic transaction data

Advanced: market cycle narrative

tables = misata.generate(
    "Real estate market with rising prices through mid-year, rate hike slowdown in Q3, "
    "slight recovery in Q4",
    rows=1000,
    seed=42,
)

Advanced: locale-aware generation

# UK property market — GBP pricing, British address format, UK cities
tables = misata.generate("UK estate agency with 300 properties in London and Manchester", rows=300)

# Australian market — AUD pricing, Australian cities, state codes
tables = misata.generate("Australian real estate agency with 400 properties", rows=400)

Advanced: quality-guaranteed generation

tables = misata.generate(
    "Real estate market with 500 listings",
    min_quality_score=85,
    smart_correlations=True,  # auto-adds sqft↔price, bedrooms↔price correlations
    rows=500,
    seed=42,
)