How scores work.
A rigorous, two-level scoring system built on 43+ authoritative government sources. Every sub-score is traceable to verifiable public data.
How Composite Scores Work
Every composite score is a weighted average of 8 independent signal groups, each scored 0–100. The groups cover commercial health, population trends, demographics, economic strength, development activity, infrastructure, safety, and amenity demand.
Each signal group is itself a weighted average of 3–6 sub-scores — each derived from a specific, verifiable data source. No single data point can dominate the final score.
Composite = Σ(group_score[i] × weight[i]) / Σ(weight[i])The 8 Signal Groups
Each group captures a distinct dimension of location quality. Weights are calibrated per scoring profile to emphasize factors relevant to each CRE use case.
Net business openings, category diversity, rating trajectories, and business license activity — the pulse of commercial activity.
6 DATA SOURCESMigration flows, vacancy trends, and residential demand signals that lead economic growth by 6-12 months.
3 DATA SOURCESIncome levels, population density, daytime population, age distribution, and household composition.
3 DATA SOURCESEmployment growth, wages, GDP, banking activity, and small business lending.
4 DATA SOURCESBuilding permits, satellite-detected construction, land cover change, and opportunity zones.
5 DATA SOURCESTransit access, walkability, traffic flow, broadband coverage, EV infrastructure, and 5G cell tower density.
6 DATA SOURCESCrime rates, natural disaster risk, air quality, flood risk, 311 complaints, and environmental quality indicators.
6 DATA SOURCESJob market density, events, school quality, food access, unemployment, and Google Trends search intent.
6 DATA SOURCESScoring Profiles
Five built-in profiles adjust signal group weights for specific CRE use cases. The underlying data and sub-score calculations remain the same — only the group-level emphasis changes.
General
Balanced weights across all 8 groups. Good starting point for multi-use CRE evaluation.
QSR / Fast-Casual
Emphasizes demographics, amenity demand, and accessibility — the factors that drive foot-traffic restaurant success.
Self-Storage
Heavily weights population momentum and economic strength — the primary demand drivers for storage facilities.
Retail
Prioritizes business vitality and accessibility — key foot-traffic and co-tenancy signals that predict retail performance.
Office
Emphasizes economic strength, demographics, and accessibility for workforce-oriented locations.
Sub-Score Calculation
Each signal group aggregates 3–6 sub-scores using a weighted internal average. Sub-score weights reflect the relative importance and reliability of each data source within its group.
group_score = Σ(sub_score[j] × sub_weight[j]) / Σ(sub_weight[j])Normalization
Each sub-score is normalized to 0–100 using ranges calibrated to US metro distributions. Linear scaling maps proportionally within range; logarithmic scaling is used for metrics with wide distributions (permit values, population density) to prevent outliers from compressing the useful range.
Fixed-range normalization keeps scores comparable across all 22 metros. A 70 in Nashville means the same thing as a 70 in San Francisco.
Significant weakness — investigate before committing.
Average to solid. Review specifics for the CRE type.
Strong signal. Corroborate with site visit.
Confidence
Every score includes a confidence value (0.0–1.0):
confidence = sources_available / sources_possibleA confidence of 1.0 means all expected data sources returned data for this location. Lower confidence indicates some sources were unavailable — typically because the location is outside city-specific data coverage, or in a rural area with sparse government data.
Transparency
Every sub-score in Axiom Locus traces to a specific, verifiable data source. Our 43+ sources include federal government datasets, municipal open data portals, and established commercial providers.
If a score does not match your on-the-ground experience, drill into the sub-scores and signal groups to understand why. We believe transparency builds trust — no black-box algorithms, no proprietary foot traffic panels.