LoopyLuci Real Estate Market Analysis
Analyzing real estate markets, property valuations, comparable sales, and investment opportunities.
When to Use
- Pricing a property for listing or offer
- Evaluating investment opportunities
- Analyzing neighborhood and market trends
- Preparing CMAs for clients
- Making data-driven real estate decisions
Comparative Market Analysis (CMA)
class CMA:
@staticmethod
def analyze(subject: Dict, comps: List[Dict]) -> Dict:
if not comps: return {}
prices_per_sqft = [c.get('sold_price', 0) / max(c.get('sqft', 1), 1) for c in comps]
avg_pps = sum(prices_per_sqft) / len(prices_per_sqft)
estimated = avg_pps * subject.get('sqft', 0)
# Adjust for differences
adjustments = 0
for comp in comps:
adjustments += (subject.get('beds', 0) - comp.get('beds', 0)) * 10000
adjustments += (subject.get('baths', 0) - comp.get('baths', 0)) * 7000
avg_dom = sum(c.get('days_on_market', 30) for c in comps) / len(comps)
return {
'estimated_value': round(estimated + adjustments / len(comps), 0),
'value_range': {
'low': round(estimated * 0.95, 0),
'high': round(estimated * 1.05, 0),
},
'avg_days_on_market': round(avg_dom, 1),
'comps_used': len(comps),
}
Market Trend Analysis
def analyze_trends(data: List[Dict]) -> Dict:
if not data: return {}
prices = [d.get('median_price', 0) for d in sorted(data, key=lambda x: x.get('date', ''))]
doms = [d.get('days_on_market', 30) for d in data]
avg_dom = sum(doms) / len(doms)
change = ((prices[-1] - prices[0]) / max(prices[0], 1)) * 100
return {
'current_median': prices[-1],
'price_change_pct': round(change, 1),
'avg_days_on_market': round(avg_dom, 1),
'market_type': "Seller's Market" if avg_dom < 30 else "Balanced" if avg_dom < 60 else "Buyer's Market",
}
Investment Analysis
def analyze_rental(value: float, down_pct: float, rate: float,
rent: float, expenses: float) -> Dict:
down = value * down_pct
loan = value - down
monthly_rate = rate / 12
payments = 30 * 12
mortgage = loan * (monthly_rate * (1+monthly_rate)**payments) / ((1+monthly_rate)**payments - 1)
noi = rent * 12 - expenses * 12
cash_flow = noi - mortgage * 12
return {
'down_payment': round(down, 0),
'monthly_mortgage': round(mortgage, 2),
'annual_cash_flow': round(cash_flow, 2),
'cap_rate': round(noi / value * 100, 2),
'cash_on_cash': round(cash_flow / down * 100, 2),
}
Common Pitfalls
- Outdated comps — use only last 3-6 months
- No adjustments — every property differs; adjust for beds, baths, condition
- Too few comps — need 5+ for reliable analysis
- Over-relying on AVMs — Zestimates are starting points, not definitive
Verification Checklist
- 5+ comps from last 6 months
- Adjustments calculated for differences
- Market type identified
- Investment metrics (cap rate, cash-on-cash)
See Also
- real-estate-crm-leads — managing property leads
- crm-sales-pipeline — tracking deals to close
- business-metrics-kpis — real estate business metrics