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PropAPIS for Developers and Builders

Inform development decisions with comprehensive market data for site selection, feasibility analysis, and competitive intelligence.

Key Benefits​

  • Site Selection: Identify high-demand areas for new development
  • Market Feasibility: Assess absorption rates and price points
  • Competitive Analysis: Track competing new construction
  • Price Positioning: Determine optimal pricing strategy
  • Demand Analysis: Understand buyer preferences and trends

Use Cases​

Site Selection Analysis​

Identify optimal locations for new development:

from propapis import PropAPIS

api = PropAPIS(api_key='your_api_key')

def analyze_development_site(locations):
    results = []

    for location in locations:
        market = api.platforms.zillow.get_market_trends(location=location)

        # Development feasibility metrics
        score = 0
        if market.yoy_change > 5:
            score += 3  # Strong price growth
        if market.months_supply < 4:
            score += 2  # Low inventory (high demand)
        if market.avg_dom < 30:
            score += 2  # Fast sales

        results.append({
            'location': location,
            'median_price': market.median_price,
            'growth': market.yoy_change,
            'inventory': market.months_supply,
            'dom': market.avg_dom,
            'score': score
        })

    # Rank by development score
    results.sort(key=lambda x: x['score'], reverse=True)

    print("Site Selection Rankings:")
    for i, site in enumerate(results, 1):
        print(f"{i}. {site['location']} (Score: {site['score']}/7)")
        print(f"   Growth: {site['growth']:+.1f}% | Supply: {site['inventory']:.1f}mo")

    return results

Price Point Analysis​

Determine optimal price range for new homes:

def analyze_price_points(location):
    # Get recent sales
    sold = api.platforms.zillow.search_sold(
        location=location,
        sold_in_last_days=180,
        property_type='Single Family'
    )

    # Analyze price distribution
    prices = [p.sold_price for p in sold]
    prices.sort()

    quartiles = {
        'q1': prices[len(prices) // 4],
        'median': prices[len(prices) // 2],
        'q3': prices[len(prices) * 3 // 4]
    }

    print(f"Price Point Analysis - {location}")
    print(f"Lower Quartile: ${quartiles['q1']:,}")
    print(f"Median: ${quartiles['median']:,}")
    print(f"Upper Quartile: ${quartiles['q3']:,}")

    # Recommend price range
    print(f"\nRecommended Price Range: ${quartiles['median']:,} - ${quartiles['q3']:,}")

    return quartiles

Absorption Rate Analysis​

Assess market demand and sales velocity:

def analyze_absorption_rate(location):
    market = api.platforms.zillow.get_market_trends(location=location)

    # Calculate monthly absorption
    monthly_sales = market.sold_count_30d
    active_inventory = market.active_count
    absorption_rate = monthly_sales / active_inventory if active_inventory > 0 else 0

    print(f"Absorption Analysis - {location}")
    print(f"Active Listings: {active_inventory:,}")
    print(f"Sales (30d): {monthly_sales:,}")
    print(f"Monthly Absorption: {absorption_rate:.1%}")
    print(f"Months of Supply: {market.months_supply:.1f}")

    # Development recommendation
    if market.months_supply < 4:
        print("✓ HIGH DEMAND - Favorable for new development")
    elif market.months_supply < 6:
        print("○ MODERATE DEMAND - Proceed with caution")
    else:
        print("✗ LOW DEMAND - Not recommended for development")

    return absorption_rate

Competitive New Construction​

Track competing new home communities:

def analyze_competition(location):
    # Find new construction
    new_homes = api.platforms.zillow.search_listings(
        location=location,
        property_type='Single Family',
        new_construction=True,
        status='active'
    )

    print(f"Competitive Analysis - {location}")
    print(f"Active New Construction: {len(new_homes)}")

    # Analyze pricing
    if new_homes:
        prices = [h.price for h in new_homes]
        avg_price = sum(prices) / len(prices)
        avg_ppsf = sum(h.price / h.sqft for h in new_homes) / len(new_homes)

        print(f"Avg Price: ${avg_price:,.0f}")
        print(f"Avg PPSqft: ${avg_ppsf:,.0f}")

        print("\nTop Competitors:")
        for home in sorted(new_homes, key=lambda x: x.price)[:5]:
            print(f"  {home.address} - ${home.price:,}")

    return new_homes

Buyer Preference Analysis​

Understand market preferences:

def analyze_buyer_preferences(location):
    # Get recent sales
    sold = api.platforms.zillow.search_sold(
        location=location,
        sold_in_last_days=180
    )

    # Analyze preferences
    bedrooms = [s.bedrooms for s in sold]
    most_common_beds = max(set(bedrooms), key=bedrooms.count)

    bathrooms = [s.bathrooms for s in sold]
    most_common_baths = max(set(bathrooms), key=bathrooms.count)

    print(f"Buyer Preferences - {location}")
    print(f"Most Popular: {most_common_beds} bed / {most_common_baths} bath")
    print(f"Based on {len(sold)} sales in last 6 months")

Quick Start​

from propapis import PropAPIS

api = PropAPIS(api_key='your_api_key')

# Analyze development site
market = api.platforms.zillow.get_market_trends(location='Austin, TX')

print(f"Median Price: ${market.median_price:,}")
print(f"YoY Growth: {market.yoy_change:+.1f}%")
print(f"Months Supply: {market.months_supply:.1f}")
print(f"Avg DOM: {market.avg_dom:.0f}")

For detailed API documentation, see our API Reference.

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