Building a Property Scoring Model: Data Sources That Matter

When real estate investors ask us to build property scoring models, the first conversation isn't about algorithms. It's about data.

The most sophisticated ML model is useless if it's trained on incomplete or unreliable data. Here's how we think about data sources for property scoring.

The Data Hierarchy

Tier 1: Essential (Must Have)

Property Characteristics

• Square footage, bed/bath count
• Year built and major renovation dates
• Lot size
• Property type (SFR, duplex, etc.)

Source: County assessor records, MLS data

Transaction History

• Last sale date and price
• Price history
• Days on market for current/recent listings

Source: MLS, county recorder

Current Listing Data

• Asking price
• Listing description
• Time on market
• Price reductions

Source: MLS, Zillow/Redfin APIs

Without Tier 1 data, you can't build a credible scoring model. Period.

Tier 2: Important (Should Have)

Comparable Sales

• Recent sales of similar properties (6-12 months)
• Price per square foot trends
• Days on market for comps

Source: MLS, PropStream, county records

Rental Data

• Current rent (if tenanted)
• Market rent estimates
• Rent per square foot in the area

Source: Rentometer, Zillow Rental Manager, local MLS

Neighborhood Metrics

• Median household income
• Crime rates
• School ratings
• Population trends

Source: Census data, GreatSchools API, local crime databases

Property Condition Indicators

• Permit history (recent updates/repairs)
• Code violations
• Visual condition from listing photos

Source: County permit records, AI image analysis

Tier 2 data separates a basic deal calculator from a real scoring model.

Tier 3: Differentiators (Nice to Have)

Market Dynamics

• Inventory levels (months of supply)
• Price trend momentum
• Investor activity (cash buyer percentage)
• New construction pipeline

Source: MLS analytics, Census building permits

Economic Indicators

• Employment growth
• Major employer news
• Infrastructure projects
• Zoning changes

Source: BLS data, local news, planning department records

Seller Motivation Signals

• Foreclosure/pre-foreclosure status
• Estate sale indicators
• Divorce or probate records
• Owner mailing address vs. property address

Source: County records, pre-foreclosure databases

Tier 3 data helps you find the deals others miss.

Data Quality Considerations

Recency Matters

• Hot markets: Comp data older than 3 months is suspect
• Stable markets: 6-month comps are generally reliable
• Rental estimates: Verify against actual current listings

Source Reliability Hierarchy

1. County records - Most reliable but can lag 30-60 days
2. MLS data - Accurate but requires licensed access
3. Aggregator sites (Zillow, Redfin) - Good for trends, may have errors in details
4. Scraped data - Use cautiously, verify against primary sources

Missing Data Strategies

For any property, you won't have complete data. Your model needs to handle this:

• Imputation: Fill missing values with reasonable estimates (median for the neighborhood, etc.)
• Confidence scoring: Reduce overall score when key data is missing
• Human review flags: Surface properties with data gaps for manual research

Building the Score

Our typical property scoring model weights data sources like this:

Financial Metrics (40%)

• Cash flow potential based on rent vs. acquisition
• Cap rate relative to market
• Price per square foot vs. comps

Market Position (25%)

• Neighborhood trajectory
• Inventory levels
• Price trend momentum

Property Quality (20%)

• Condition indicators
• Age and update history
• Layout efficiency

Risk Factors (15%)

• Days on market (prolonged = potential issues)
• Price reduction history
• Environmental or title concerns

Practical Implementation

Start simple. Your first model should use only Tier 1 data with basic calculations. Get that working reliably before adding complexity.

Automate data collection. Manual data gathering doesn't scale. Invest in API connections and scraping infrastructure early.

Validate against outcomes. Track which properties you scored highly and what actually happened. Did high-scoring properties perform as expected?

Update regularly. Market conditions change. A model trained on 2021 data may not work well in 2024 conditions.

The Human Element

No model replaces boots-on-ground knowledge. Use scoring to:

• Filter the funnel (screen out obvious non-starters)
• Prioritize research (look at high-scoring properties first)
• Identify outliers (properties scoring unexpectedly high or low)

The final decision should always incorporate information the model can't capture: your intuition, local knowledge, and strategic goals.

Data-driven doesn't mean data-only. It means data-informed.