What Metro-Level Data Tells You
Understanding what each data dimension reveals — and where the numbers have limits.
Key Takeaway
Metro areas are defined by commuting patterns, not political boundaries — they capture the real economic geography. Cost of living (especially housing) is the most variable dimension, with housing costs differing 4x+ between cheap and expensive metros. Each data source measures something different: wages don't account for cost of living, crime rates don't account for reporting differences, and health data reflects communities, not individuals.
Affordability: Cost of Living vs. Income
Raw salary comparisons are meaningless without cost of living context. A $80,000 salary in Topeka, Kansas (RPP ~87) buys significantly more than $100,000 in Boston (RPP ~115). PlainCompare shows both wages and Regional Price Parities so you can calculate real purchasing power.
The formula: Real income = Nominal income ÷ (RPP / 100). An $80,000 salary at RPP 87 equals $91,954 in national-average purchasing power. A $100,000 salary at RPP 115 equals $86,957. The Topeka worker is financially better off despite a lower nominal salary.
Browse metro rankings by affordability to see which areas offer the best value.
Safety: Violent and Property Crime
PlainCompare shows FBI crime data broken into violent crime (direct threat to personal safety) and property crime (theft, burglary — financial loss without direct physical threat). These measure very different risks:
- A metro with low violent crime but high property crime is generally safe to walk around but has a car theft or package theft problem.
- A metro with high violent crime but low property crime may have concentrated violence in specific neighborhoods while most areas are safe.
Metro-level crime averages can mask neighborhood variation. Use PlainCompare for initial screening, then research specific neighborhoods within your target metros.
Jobs, Education, Health, and Environment
Jobs: BLS data shows employment levels and wages by occupation. Check whether your specific field is well-represented and well-paid in the metro — aggregate income numbers aren't useful if your industry is absent.
Education: NCES and Census data show school infrastructure and adult educational attainment. High bachelor's degree rates (40%+) correlate with stronger school systems, more professional services, and higher property values.
Health: CDC community health indicators reveal population-level health behaviors and outcomes. These affect your healthcare environment — insurance costs, provider availability, and the health culture of your community.
Environment: EPA air quality data and NOAA climate normals show the physical environment. Air quality affects long-term health, and climate affects daily comfort and energy costs. Check metro pages for the full breakdown.
Frequently Asked Questions
What is a Regional Price Parity (RPP)?
An RPP is the BEA's measure of how much goods and services cost in a metro relative to the national average (100). An RPP of 110 means prices are 10% above national average. An RPP of 90 means 10% below. RPPs cover all consumer goods and services, with separate measures for housing, which varies most dramatically.
Why is housing the biggest price differentiator?
Housing RPPs range from 60 (40% below average) to over 200 (double the average). No other expense category varies this much. A median home in San Jose costs 4x what it costs in Memphis. This is why housing affordability is typically the dominant factor in cost-of-living comparisons.
What crime data does PlainCompare show?
FBI UCR data covering violent crime (murder, assault, robbery, sexual assault) and property crime (burglary, theft, vehicle theft) rates per 100,000 residents. Rates are more meaningful than counts because they normalize for population size. Some metros don't report to the FBI program — PlainCompare notes where data is unavailable.
How should I interpret health data for a metro?
CDC PLACES data shows the prevalence of health conditions and behaviors at the community level. High obesity or smoking rates indicate population health challenges, not individual risk. These indicators correlate with healthcare demand, insurance costs, and the general health culture of a community.
What does the composite score mean?
PlainCompare's composite score combines multiple data dimensions into a single livability index. It weights affordability, safety, health, education, and environment into a normalized 0-100 scale. The score provides a quick comparison but should not replace dimension-by-dimension analysis — a high composite score can mask weakness in one area you care about deeply.
Can I trust FBI crime data?
FBI UCR data is the best available national crime dataset, but it has limitations. Not all agencies report in every year. Reporting practices differ between jurisdictions — some agencies are more aggressive about recording crimes than others. Comparing year-over-year trends within a single metro is more reliable than comparing absolute rates between different metros.
Sources
- Bureau of Economic Analysis, FBI UCR, BLS OEWS, Census ACS, NCES, CDC PLACES, EPA AQS
This content is for informational purposes only. Data represents statistical averages for metropolitan areas and may not reflect conditions in specific neighborhoods.
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.
