Methodology

How our neighborhood scores work

Every score on NeighborhoodIntelPRO traces back to public data. Here's how we get from raw datasets to a 0–100 view of a place — and why each score ships with a confidence indicator.

1. Source the data

We aggregate public, authoritative datasets:

Data sources

2. Map data to your location

Census geographies, county FIPS codes, school districts, and EPA water-service areas don't always line up perfectly with a single address. We map your query to the closest available reporting unit for each category and label approximations honestly — for example, “Mapped to nearest available water system based on public EPA data.”

3. Normalize and score

Each input is normalized against national distributions, then combined into category scores — livability, safety, affordability, schools, environment, and momentum. Weights reflect how strongly each input correlates with the category in well-known research, not arbitrary judgment.

4. Attach confidence

Every score ships with a confidence indicator so you can decide how much weight to give it:

High confidenceModerate confidenceLimited confidence

Confidence is calculated from three signals: completeness (how many inputs were available), recency (how fresh the data is), and precision (how closely the data maps to the area you asked about).

5. Interpret responsibly

Scores are interpretive — not certifications. They are a research starting point. For decisions about housing, finances, schools, or relocation, combine them with on-the-ground research and qualified professional advice.