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PropAPIS for Market Researchers

Access comprehensive real estate data for housing market analysis, trend forecasting, and research publications.

Key Benefits​

  • Comprehensive Data: Access 20+ 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.

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