PropAPIS for Market Researchers
Access comprehensive real estate data for housing market analysis, trend forecasting, and research publications.
Key Benefits
- Comprehensive Data: Access 60+ platforms for complete market coverage
- Historical Data: 10+ years of transaction and listing history
- Market Metrics: Median prices, inventory levels, days on market
- Geographic Granularity: National, state, metro, ZIP code level analysis
- Export Options: CSV, JSON for analysis in Excel, R, Python
Use Cases
Housing Market Analysis
Analyze market trends across regions:
from propapis import PropAPIS
api = PropAPIS(api_key='your_api_key')
def analyze_market_trends(markets):
results = []
for market in markets:
data = api.platforms.zillow.get_market_trends(location=market)
results.append({
'market': market,
'median_price': data.median_price,
'yoy_change': data.yoy_change,
'active_listings': data.active_count,
'months_supply': data.months_supply,
'avg_dom': data.avg_dom
})
return results
# Analyze major metros
metros = ['New York, NY', 'Los Angeles, CA', 'Chicago, IL', 'Houston, TX']
analysis = analyze_market_trends(metros)
for market in analysis:
print(f"{market['market']}")
print(f" Median: ${market['median_price']:,}")
print(f" YoY: {market['yoy_change']:+.1f}%")Supply and Demand Analysis
Track inventory and sales velocity:
def analyze_supply_demand(location):
market = api.platforms.zillow.get_market_trends(location=location)
# Calculate market indicators
absorption_rate = market.sold_count_30d / market.active_count
market_balance = market.months_supply
print(f"Supply & Demand Analysis - {location}")
print(f"Active Listings: {market.active_count:,}")
print(f"Sold (30d): {market.sold_count_30d:,}")
print(f"Absorption Rate: {absorption_rate:.2%}")
print(f"Months of Supply: {market_balance:.1f}")
if market_balance < 3:
print("Status: Seller's Market (low supply)")
elif market_balance > 6:
print("Status: Buyer's Market (high supply)")
else:
print("Status: Balanced Market")Price Trend Forecasting
Analyze historical trends for forecasting:
def analyze_price_trends(location):
# Get historical market data
market = api.platforms.zillow.get_market_trends(location=location)
print(f"Price Trend Analysis - {location}")
print(f"Current Median: ${market.median_price:,}")
print(f"1-Year Change: {market.yoy_change:+.1f}%")
print(f"5-Year CAGR: {market.cagr_5y:+.1f}%")
# Forecast next year (simple projection)
forecast = market.median_price * (1 + market.yoy_change / 100)
print(f"12-Month Forecast: ${forecast:,.0f}")Comparative Market Studies
Compare multiple markets:
def compare_markets(markets):
comparison = []
for market in markets:
data = api.platforms.zillow.get_market_trends(location=market)
comparison.append({
'market': market,
'median_price': data.median_price,
'affordability_index': 100000 / data.median_price * 100,
'growth_rate': data.yoy_change,
'market_health': data.months_supply
})
# Rank by growth rate
comparison.sort(key=lambda x: x['growth_rate'], reverse=True)
print("Market Rankings by Growth:")
for i, m in enumerate(comparison, 1):
print(f"{i}. {m['market']} - {m['growth_rate']:+.1f}% YoY")Quick Start
from propapis import PropAPIS
api = PropAPIS(api_key='your_api_key')
# Get market statistics
market = api.platforms.zillow.get_market_trends(location='Austin, TX')
print(f"Median Price: ${market.median_price:,}")
print(f"YoY Change: {market.yoy_change:+.1f}%")
print(f"Active Listings: {market.active_count:,}")For detailed API documentation, see our API Reference.