How Custom RAAM Works
A transparent look at how we research properties, calculate your score, and keep data reliable without depending on any single provider.
The Core Idea
Custom RAAM (Residential Asset Acquisition Model) is a 7-criterion scoring system that evaluates residential properties based on what you care about. Unlike generic property scores that use fixed weights for everyone, Custom RAAM lets you control exactly how much each factor matters to your decision.
You assign a weight (importance percentage) to each of seven factors. Those weights add up to 100%, and the system scores every property on those same seven factors. Your final score is a weighted sum: each factor's score (0–100) multiplied by its weight, then summed across all seven.
The result: a single score that reflects YOUR priorities, not a one-size-fits-all algorithm.
The Seven Factors
Every property is evaluated on these seven dimensions. You decide how much each one matters by setting your custom weights.
School Quality
District and school performance, ratings, and educational outcomes. Scored using school and district data from public education sources.
Value for Money
Price relative to size (price per square foot) and local market norms. Scored by comparing the property's price and square footage to area medians and recent comparable sales.
Lot Size and Light
Lot size relative to the home's living area, plus orientation and outdoor space. Larger lots and better ratios score higher.
Home Age
Year built and age of the structure. Newer homes typically score higher for modern systems and design, but well-maintained or historic homes (100+ years) can also score well.
Condition and Systems
Inferred condition based on age, recent renovations, and available details. Newer homes and homes with documented updates score higher; older homes without updates may need work.
Community Diversity
Diversity and demographic context of the neighborhood and town. Scored using census and community data.
Commute Access
Travel time to common destinations (downtown, employment centers, transit hubs). Lower commute times score higher. Based on mapping and commute data for the area.
Each factor is scored 0–100. Your weights determine how much each factor contributes to the final score. A factor you weight 25% has four times the impact of one you weight about 6%.
Score Calculation
Your property score is calculated in three simple steps:
1. Normalize your weights
Your seven weights are adjusted so they add up to exactly 100%. This ensures the final score is always on a 0–100 scale.
2. Score each factor
We score the property on each of the seven factors using the data we've researched. Each factor gets a score from 0 (worst) to 100 (best).
3. Calculate the weighted sum
For each factor, we multiply its score by your weight for that factor, then divide by 100. We sum those seven weighted scores to get your final property score.
Formula:
Total Score = (Factor₁ Score × Weight₁ + Factor₂ Score × Weight₂ + ... + Factor₇ Score × Weight₇) / 100
Example: If School Quality scores 80/100 and you weight it 20%, it contributes 16 points to your total score. If Value scores 60/100 and you weight it 10%, it contributes 6 points. And so on for all seven factors.
Where the Data Comes From
We use a research process that draws on multiple categories of sources, not a single feed. This approach keeps us flexible and resilient to changes in any one provider.
Property Records
County and municipal assessor sites, parcel data, tax records, and ownership information.
Listing & Sales Data
Public MLS data, recent sale prices, listing details, and market comparables.
School & District Info
School performance data, district ratings, test scores, and educational resources.
Community & Local
Census data, town meeting notes, permits, local government records, and demographic information.
Geographic & Commute
Mapping data, transit routes, travel times, and proximity to employment centers.
Additional Context
Flood zones, walkability scores, crime statistics, and other factors buyers care about where available.
Why multiple sources? If we relied on a single provider and that provider changed terms, pricing, or availability, our entire system would be at risk. By combining and cross-checking multiple sources, we stay adaptable and can replace or supplement any source that becomes unreliable.
Keeping Data Current & Reliable
Real estate data changes constantly: new listings, price updates, school ratings, and more. Here's how we keep your analyses accurate:
Geographic Prioritization
We focus research effort on the zip codes and markets where our users are actively searching. Instead of trying to keep every market up to date, we keep the areas you care about deep and current.
Cross-Referencing
We align data from multiple sources using property identifiers (address, parcel number, coordinates). This helps us catch discrepancies and use the most reliable value when sources disagree.
Transparency About Gaps
When we don't have reliable data for a factor, we don't invent a number. Your analysis will show that the factor wasn't scored or was only partially scored, and your total score will reflect only the factors we could research with confidence.
Continuous Improvement
As we learn which sources are most reliable and where gaps exist, we refine our research process. We're constantly evaluating new data sources and retiring ones that become stale or expensive.
Privacy & Security
Your property searches and preferences are private. We use industry-standard encryption for data at rest and in transit. We never sell your data to third parties.
The data we collect—your weights, requirements, buyer profile, and the properties you analyze—is used only to:
- Generate your personalized property scores
- Prioritize which geographies we research most deeply
- Improve the accuracy and usefulness of our analysis over time
We don't share your specific property searches with data providers, and we don't use your search history for advertising or third-party analytics.
Limitations & Transparency
Custom RAAM is a tool to help you make better decisions, not perfect ones. Here's what we can and cannot do:
We cannot guarantee data accuracy
Property data comes from public records and listing sources that can be outdated, incomplete, or incorrect. We cross-check when possible, but you should verify critical details independently.
We cannot inspect properties
Condition scores are inferred from age and available details, not from physical inspections. Always get a professional inspection before making an offer.
Scores are relative, not absolute
A score of 85 doesn't mean "this is an 85% perfect home." It means the property scores well on the factors YOU prioritized. A different buyer with different weights might get a very different score for the same property.
What we do well
We help you compare properties consistently based on what matters to you, and we surface strengths and concerns you might otherwise miss. Think of Custom RAAM as a personalized checklist that runs automatically for every property you consider.