PlainCompare

Guide

Data-Driven Relocation Decisions

The verdict

Stacking just three above-median non-negotiables (affordability, safety, schools) narrows PlainCompare's 387-metro field to 33 candidates (9%) - Paducah, KY-IL leads what's left.

387
metros in the field
92
clear cost + safety
33
clear cost + safety + schools
9%
of the field survives 3 filters

How fast the field narrows

All 387 metros387Above-median affordability194+ above-median safety92+ above-median schools33
Each bar adds ONE above-median (>=50) non-negotiable on top of the last, metro count from live life_scores (vintage 2026-08-29).

How to use federal data to build a shortlist, then let real experience make the final call.

The Relocation Framework

Moving to a new city is one of the highest-impact decisions in life. It affects your career, finances, relationships, health, and daily happiness. Yet most people choose where to live based on a job offer, family proximity, or a vacation impression, not systematic analysis. PlainCompare enables a more rigorous approach.

  1. Define your non-negotiables - the factors you absolutely cannot compromise on.
  2. Filter by non-negotiables - eliminate metros that fail your must-have criteria.
  3. Rank by priorities - weight remaining factors by importance to you.
  4. Compare your shortlist - deep-dive into 3-5 finalists.
  5. Visit before deciding - data confirms the shortlist, experience confirms the choice.

Step 1: Set Non-Negotiables

Non-negotiables are binary, a metro either passes or doesn't. Common non-negotiables:

  • "I need a job in [industry]": Eliminate metros without employers in your field. Check metro job data.
  • "I can't afford [housing cost]": Eliminate metros where median housing exceeds your budget.
  • "Safety is critical": Eliminate metros above your crime rate threshold.
  • "I need warm weather": Eliminate metros with cold winters. Check climate normals.

Use PlainCompare's rankings to quickly identify which metros pass your filters.

Step 2: Weight Your Priorities

After filtering, rank remaining factors by how much they matter to you. A simple 1-5 weighting works:

  • 5 = Critical: This factor dominates my decision.
  • 4 = Very important: Would strongly influence my choice.
  • 3 = Important: Matters but wouldn't be a dealbreaker.
  • 2 = Nice to have: I'd notice but could live without.
  • 1 = Don't care: Irrelevant to my decision.

PlainCompare's composite scores use a balanced weighting, but your personal weights may differ significantly. A retiree weights health and climate 5/5 while jobs might be 1/5.

Step 3: Deep Compare Finalists

With 3-5 metros remaining, use the side-by-side comparison to evaluate every dimension:

  • Calculate purchasing power: salary ÷ (RPP ÷ 100) for each metro.
  • Compare crime rates, both violent and property.
  • Check education metrics if you have or plan to have children.
  • Review health indicators, community health patterns affect your environment.
  • Compare climate normals, temperature ranges, precipitation, sunshine.

Create a simple spreadsheet with your weighted scores for each metro. The numbers will likely confirm an intuition you already have, or reveal a surprise you would have missed.

Step 4: Visit Before Deciding

Data narrows 384 metros to 3-5 candidates. But no dataset captures the feel of walking through a neighborhood, the energy of a downtown, or the friendliness of strangers. Plan 2-3 day visits to your top choices. During each visit:

  • Drive through neighborhoods at different times of day.
  • Visit the grocery stores, parks, and amenities you'd actually use.
  • Eat at local restaurants (not chains) to gauge the food culture.
  • Check commute routes during rush hour if you'll be commuting.
  • Talk to locals, ask what they love and what frustrates them.

The best relocation decisions combine data-driven shortlisting with experience-driven final selection.

Frequently Asked Questions

How many cities should I compare?

Start broad, then narrow. Use PlainCompare rankings to identify 8-10 metros that meet your baseline criteria. Then do detailed side-by-side comparisons to narrow to 3-5 finalists. Visit your top 2-3 in person before deciding. Data gets you the shortlist; in-person experience makes the final call.

Should I prioritize salary or cost of living?

Neither alone, prioritize purchasing power, which is salary adjusted for cost of living. A $70,000 salary in Omaha (RPP ~90) provides more purchasing power than $90,000 in Seattle (RPP ~118). PlainCompare shows both numbers so you can calculate real value.

How important is climate data for relocation?

More than most people expect. Climate affects daily comfort, energy bills, outdoor activity, seasonal depression, and long-term health. Someone who hates cold will be miserable in Minneapolis no matter how affordable it is. Check NOAA climate normals for temperature ranges, precipitation, snow, and sunshine days.

What if my job is remote?

Remote workers have the rare luxury of optimizing purely for quality of life and affordability. Focus on: low cost of living (maximize purchasing power), reliable internet infrastructure, time zone compatibility with your employer, and quality of life factors that matter to you personally. Remote work unlocks metros that would otherwise be off-limits due to job availability.

How do I account for state taxes?

State income tax varies from 0% (Texas, Florida, Nevada, etc.) to 13%+ (California). This directly affects take-home pay. A $100,000 salary in Texas keeps roughly $5,000-$8,000 more per year than the same salary in California after state income tax. Property taxes vary even more dramatically by county. PlainCompare shows wage data; cross-reference with state tax information for the complete picture.

When is data not enough?

Data can't capture neighborhood character, social connections, cultural fit, or "feeling at home." It also can't predict future growth, a city investing in transit and attracting employers may be much better in 5 years. Use data for the shortlist, but always visit before committing. A weekend visit reveals things no dataset can.

Sources

  • BEA, FBI, BLS, Census, NCES, CDC, EPA, via PlainCompare aggregation

This content is for informational purposes only. Relocation is a personal decision involving many factors beyond data.

A worked example

Build a shortlist from the current metro data, then use the comparison view to test the trade-offs that matter to your household. Affordability is useful only alongside the other measures, especially when a lower-cost place changes the safety, school, health, or job picture.

Where to dig deeper

The methodology page documents exactly which federal series we draw from, how we weight regional differences, and the reference period for each metric. The research section publishes original analyses derived from the same underlying database, useful when you want to see year-over-year shifts or peer-jurisdiction comparisons that the per-page detail views don't surface.

"The strongest decisions come from triangulating multiple data sources against your specific situation, not from chasing the latest headline number."

Frequently asked questions

Where does this data come from?

All figures on this page derive from official federal data, primarily the U.S. Bureau of Labor Statistics, U.S. Census Bureau, U.S. Department of Health and Human Services, and U.S. Department of Labor. We cite the underlying agency and series in the methodology section. No proprietary aggregators are used.

How often are figures updated?

Each series follows its own publication cadence. We rebuild after validated source extracts change. Specific update timestamps appear in the page footer where available; the methodology page documents the cadence per data series.

Can I use this data for my own analysis?

Yes. The underlying federal data is public domain. Our presentation, calculations, and editorial commentary are licensed for individual reference. For commercial republication or large-scale data extraction, contact us at the email listed on the contact page.

What if the figures here disagree with another source?

Different sources use different methodologies, definitions, geographic boundaries, and reference periods, disagreement is normal and informative. Our methodology page documents exactly which series and reference period we use for each metric, so you can reproduce or audit the figures against the upstream agency directly.

What to do with this

Only 33 of 387 metros clear cost, safety and schools together - most non-negotiable stacks eliminate more of the field than people expect.

  • Paducah, KY-IL leads the 33 metros clearing all three filters - open its page before assuming a bigger-name metro is the safer bet. See Paducah, KY-IL's breakdown
  • Swap in your own non-negotiables (jobs, climate, healthcare) and re-run the filter mentally before building a shortlist. Browse rankings
  • A metro clearing your filters on the field average may still miss on the ONE occupation or neighborhood that matters to you - verify on the metro page.

Filters are cost_score/crime_score/schools_score >=50 (above the national median) on live life_scores, vintage 2026-08-29; your own thresholds will narrow the field differently.

Calculator Plug your numbers in Methodology How we computed this Research Original analysis By state Drill into your jurisdiction

Every figure on PlainCompare is loaded directly from BEA, HUD, FBI UCR, BLS OEWS, NCES, DOL, EPA, and the Census Bureau, no number is typed in by an editor. This guide's figures are drawn directly from the same federal sources used across PlainCompare. See our editorial standards & corrections policy, the methodology behind these numbers, or report a data error. Data current as of 2026.