PlainCompare

Data-Driven Relocation Decisions

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

Key Takeaway

Data eliminates bad choices. A structured comparison across affordability, safety, jobs, education, health, and climate narrows 384 metros to 3-5 strong candidates. From there, visit in person — data gets you the shortlist, experience makes the decision. PlainCompare combines 7 federal data sources to enable this filtering at scale.

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.

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.

A worked example

Consider a household earning $75,000 per year facing an annual cost of $18,000 for the service this guide covers. Their cost-to-income ratio is 24% — below the 30% red-line that federal affordability frameworks use to flag burden. By comparison, a household at $45,000 facing the same $18,000 cost lands at 40% — well into severely-burdened territory under the same definitions.

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.

ThresholdFederal definitionPractical meaning
Below 7%AffordableComfortable margin for unexpected expenses
7-30%Moderate burdenManageable but constrains discretionary spending
Above 30%BurdenedHUD definition — qualifies for federal subsidy programs
Above 50%Severely burdenedTrade-offs with food, healthcare, savings

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 refresh our database within 30 days of each upstream release. 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.

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