PropAPIS for Lenders and Appraisers

from propapis import PropAPIS
 
api = PropAPIS(api_key='your_api_key')
 
def get_property_valuation(address):
    prop = api.platforms.zillow.get_property(address)
 
    # Multiple valuation sources
    valuations = {
        'zestimate': prop.zestimate,
        'zestimate_range': (prop.zestimate_low, prop.zestimate_high)
    }
 
    print(f"Valuation Report - {address}")
    print(f"Zestimate: ${valuations['zestimate']:,}")
    print(f"Range: ${valuations['zestimate_range'][0]:,} - ${valuations['zestimate_range'][1]:,}")
 
    return valuations

def find_appraisal_comps(subject_address):
    # Get subject property
    subject = api.platforms.zillow.get_property(subject_address)
 
    # Find comparable sold properties
    comps = api.platforms.zillow.search_sold(
        location=subject_address,
        radius=0.5,
        sold_in_last_days=180,
        min_bedrooms=subject.bedrooms - 1,
        max_bedrooms=subject.bedrooms + 1,
        min_sqft=subject.sqft * 0.8,
        max_sqft=subject.sqft * 1.2
    )
 
    print(f"Subject: {subject.address}")
    print(f"  Bedrooms: {subject.bedrooms} | Sqft: {subject.sqft:,}")
    print(f"\nComparable Sales ({len(comps)}):")
 
    for comp in comps[:5]:
        price_per_sqft = comp.sold_price / comp.sqft
        print(f"{comp.address}")
        print(f"  Sold: ${comp.sold_price:,} on {comp.sold_date}")
        print(f"  Sqft: {comp.sqft:,} | PPSqft: ${price_per_sqft:,.0f}")
 
    # Calculate average price per sqft
    avg_ppsf = sum(c.sold_price / c.sqft for c in comps) / len(comps)
    estimated_value = subject.sqft * avg_ppsf
 
    print(f"\nEstimated Value: ${estimated_value:,.0f}")
    print(f"Based on ${avg_ppsf:,.0f}/sqft")
 
    return comps

def calculate_ltv(address, loan_amount):
    prop = api.platforms.zillow.get_property(address)
 
    ltv = (loan_amount / prop.zestimate) * 100
 
    print(f"LTV Analysis - {address}")
    print(f"Property Value: ${prop.zestimate:,}")
    print(f"Loan Amount: ${loan_amount:,}")
    print(f"LTV Ratio: {ltv:.1f}%")
 
    if ltv <= 80:
        print("Risk Level: Low (≤80% LTV)")
    elif ltv <= 90:
        print("Risk Level: Moderate (80-90% LTV)")
    else:
        print("Risk Level: High (>90% LTV)")
 
    return ltv

def assess_market_risk(location):
    market = api.platforms.zillow.get_market_trends(location=location)
 
    print(f"Market Risk Assessment - {location}")
    print(f"Median Price: ${market.median_price:,}")
    print(f"YoY Change: {market.yoy_change:+.1f}%")
    print(f"Months Supply: {market.months_supply:.1f}")
    print(f"Avg DOM: {market.avg_dom:.0f}")
 
    # Risk scoring
    risk_score = 0
 
    if market.yoy_change < -5:
        risk_score += 3
        print("⚠️ Declining prices")
 
    if market.months_supply > 9:
        risk_score += 2
        print("⚠️ Oversupply")
 
    if market.avg_dom > 90:
        risk_score += 1
        print("⚠️ Slow market")
 
    if risk_score == 0:
        print("✓ Market risk: LOW")
    elif risk_score <= 3:
        print("⚠️ Market risk: MODERATE")
    else:
        print("❌ Market risk: HIGH")
 
    return risk_score

from propapis import PropAPIS
 
api = PropAPIS(api_key='your_api_key')
 
# Get property valuation
prop = api.platforms.zillow.get_property('123 Main St, Austin, TX')
 
print(f"Zestimate: ${prop.zestimate:,}")
print(f"Range: ${prop.zestimate_low:,} - ${prop.zestimate_high:,}")
 
# Find comps
comps = api.platforms.zillow.search_sold(
    location='123 Main St, Austin, TX',
    radius=0.5,
    sold_in_last_days=180
)
 
print(f"Found {len(comps)} comparable sales")