Understanding Metro Scores
How PlainCompare evaluates cities across 7 dimensions — and how to use the scores without being misled.
Key Takeaway
No metro wins on every dimension. The city with the highest composite score may not be your best fit. PlainCompare's value is not in declaring a "best city" but in showing you exactly where each metro excels and falls short — so you can make the trade-offs that match your life.
Why Multi-Dimensional Scoring Matters
Most city ranking lists collapse complex, multi-dimensional data into a single number and declare one city "the best." This is fundamentally misleading because different people have different priorities. A young professional optimizing for career growth and nightlife has completely different needs than a family optimizing for school quality and affordable childcare.
PlainCompare does calculate a composite score, but its primary value is in the dimensional breakdown — showing you exactly how a metro performs on each of the seven factors drawn from federal data. The composite is a starting point for exploration, not a verdict.
This guide explains how each dimension is calculated, what the percentile scores mean, and how to use the data effectively for relocation research. Browse all metros with their dimensional scores on the metro explorer.
How Percentile Scoring Works
Each metro is ranked against all other metros on each dimension using the relevant federal dataset. Rankings are then converted to percentiles on a 0-100 scale, where 100 is the best performer and 0 is the worst.
What it tells you: A metro scoring 85 on safety means it has lower crime rates than 85% of U.S. metros. A metro scoring 30 on cost of living means 70% of metros are more affordable. Percentiles make dimensions with completely different units (dollars, crime rates, student counts) directly comparable.
What it doesn't tell you: Percentiles show relative position, not absolute values. A metro scoring 50 on safety is average, but "average" crime in a generally safe country is different from "average" in a high-crime country. Always check the underlying data (actual crime rates, actual premiums, actual wages) on the metro detail page for context.
How to use it: Use percentiles for quick screening — identify metros that score above 60 on your top 3 priorities. Then drill into the actual data on each metro page to see what those scores translate to in real-world terms.
The Seven Dimensions Explained
PlainCompare evaluates metros across seven dimensions, each sourced from a different federal agency:
1. Cost of Living (BEA): Based on Regional Price Parities. Measures the overall price level of goods, services, and housing relative to the national average. A metro with an RPP of 90 is 10% cheaper than average. Lower costs earn higher scores.
2. Wages (BLS): Based on median wages from the Occupational Employment and Wage Statistics program. Higher median wages earn higher scores. Best used alongside cost of living to assess purchasing power.
3. Rent (HUD): Based on Fair Market Rents — the 40th percentile rent for standard-quality units. Lower rents earn higher scores. Most relevant for renters; homeowners may weight this dimension lower.
4. Crime (FBI): Based on UCR violent and property crime rates per 100,000 residents. Lower crime earns higher scores. Note that FBI data depends on voluntary reporting, so some metros have incomplete data.
5. Schools (NCES): Based on student-teacher ratios, enrollment levels, and school counts from the Common Core of Data. Lower student-teacher ratios and more school options earn higher scores.
6. Childcare (DOL): Based on median childcare costs for infants, toddlers, and preschoolers. Lower costs earn higher scores. Most relevant for families with young children.
7. Environment (EPA): Based on regulated facility counts, water system data, and Superfund site proximity. Fewer environmental hazards earn higher scores.
Common Mistakes When Comparing Cities
Understanding what the data can and cannot tell you prevents the most common comparison errors:
Mistake 1: Comparing only the composite score. Two metros with the same composite of 65 may have completely different profiles — one excels at cost and safety but has weak schools; the other has great schools and wages but is expensive. Always look at the dimensional breakdown.
Mistake 2: Ignoring the cost-wage interaction. A metro with a high wage score but also a high cost score may deliver the same purchasing power as a metro with moderate scores on both. PlainCompare's comparison tool accounts for this when you compare two metros side by side.
Mistake 3: Treating percentiles as absolute measures. A metro scoring 95 on safety is safer than 95% of metros, but that does not mean it is crime-free. Check the actual crime rate on the metro detail page to calibrate your expectations.
Mistake 4: Over-weighting dimensions that don't affect you. If you have no children, school quality and childcare costs may be irrelevant to your decision. Mentally discount those dimensions when scanning composite scores.
Mistake 5: Assuming data captures everything. Climate, culture, proximity to family, career networks, and community character are not in any federal dataset. Use data to narrow 384 metros to 5-10, then use qualitative research and in-person visits for the final selection.
A Practical Comparison Framework
Step 1 — Identify your top 3 priorities. Of the seven dimensions, which three matter most to your situation? Rank them. This is the lens through which you should evaluate every metro.
Step 2 — Use rankings for initial screening. Browse PlainCompare's best schools, most affordable metros, or cleanest environment pages to identify metros scoring well on your priorities.
Step 3 — Compare your shortlist head-to-head. Use the comparison tool to see exactly where each metro wins and loses relative to the others on your list.
Step 4 — Verify with primary sources. For your top 2-3 candidates, check the original agency websites for the most current data. Our data-driven relocation guide walks through how to do this efficiently.
Frequently Asked Questions
How are PlainCompare metro scores calculated?
Each metro is ranked on 7 dimensions (cost of living, wages, rent, crime, schools, childcare, environment) using federal government data. Rankings are converted to percentiles from 0-100, where higher is always better. The composite score is a weighted average of all dimensions. You can explore how each metro scores on the metro detail pages.
Why does a metro with higher wages sometimes score lower than one with lower wages?
The composite score considers all 7 dimensions, not just wages. A metro with very high wages but also very high costs, high crime, or poor environmental scores may have a lower composite than a mid-wage metro that scores well on all other dimensions. The composite reflects overall livability, not any single metric.
Can I change the dimension weights?
PlainCompare uses balanced default weights, but different dimensions matter more to different households. A family with young children might weight schools and childcare higher. A retiree might weight healthcare and cost of living higher. Use the dimension breakdowns on each metro page to apply your own priorities qualitatively.
What is a metropolitan statistical area (MSA)?
An MSA is a geographic region defined by the Census Bureau around a core urban area of 50,000+ population plus surrounding counties with strong commuting ties. MSAs capture the full economic area where people live and commute, not just the city proper. "Dallas-Fort Worth-Arlington" includes all the counties that function as part of that metro economy.
How current is the data in PlainCompare?
Each data source has its own update cycle. BLS wages are updated annually with a 12-month lag. FBI crime data lags by 1-2 years. BEA cost of living is annual. Census ACS is a rolling 5-year average. CDC health data is annual. We use the most recent available data from each source and update within 30 days of new releases.
Sources: Bureau of Economic Analysis (BEA), Regional Price Parities; Bureau of Labor Statistics, OEWS; FBI UCR; NCES CCD; HUD FMR; DOL; EPA ECHO.
Last updated: April 2026
Understanding the Data
The information presented throughout this guide is informed by publicly available public records published by federal and state government agencies. Our database aggregates and standardizes these records to make them more accessible and easier to interpret for general audiences. When we reference specific statistics or trends, they are drawn directly from these authoritative sources unless explicitly noted otherwise.
It is important to understand the limitations of any large-scale data dataset. Records may contain errors from the original data collection process, some fields may be incomplete for older entries, and classification systems may have changed over time. Our analysis accounts for these factors by clearly labeling data vintage, flagging records with missing critical fields, and noting when temporal comparisons span methodology changes in the source data.
For readers who want to conduct their own research, we recommend going directly to the source whenever possible. federal and state government agencies provides detailed documentation on collection methodology, sampling frames, and known data quality issues. Our goal is not to replace primary sources but to make them more approachable and to highlight patterns that may not be immediately obvious when browsing raw records.
How We Analyze Data Records
Our analytical approach involves several steps designed to surface meaningful insights from large datasets. First, we clean and standardize the raw data, handling variations in naming conventions, date formats, and categorical labels. Then we compute summary statistics, distributions, and comparative benchmarks across relevant dimensions such as geography, time period, and category type.
Key metrics we examine include statistical records, geographic distributions, temporal trends. These indicators provide a multi-dimensional view of each entity in our database, allowing users to understand not just individual records but how they compare to peers, regional averages, and national benchmarks. We believe this contextual approach is far more valuable than presenting raw numbers in isolation.
