How to Use Data for Relocation Decisions

Why Most Relocation Decisions Are Made on Bad Data

The average American moves 11 times in their lifetime. Yet few of those moves involve systematic data analysis. Most people choose cities based on: a job offer, family proximity, a vacation impression, or word of mouth from one person. These inputs are useful but incomplete. A job offer in Austin tells you nothing about whether Austin's cost-of-living growth has outpaced its salary growth, or whether its school quality matches your expectations, or whether its air quality will aggravate your allergies.

PlainCompare aggregates seven federal data sources, BEA, HUD, FBI, BLS, NCES, EPA, and CDC, across 384 metros. That breadth means you can compare every significant U.S. metro on the same set of dimensions, using data that is consistent, reproducible, and government-verified. The framework below converts that data into a decision process.

The Eight Key Factors

Not all relocation factors carry equal weight. Below are the eight dimensions that most reliably predict long-term satisfaction with a move, along with the federal data source behind each:

Weight guidance for a typical household. Adjust based on personal circumstances: remote workers weight job market lower; retirees weight climate and healthcare higher.

Step 1, Calculate Real Purchasing Power

Before anything else, compute purchasing power. This is the most important number in any city comparison. The formula:

Real purchasing power = Nominal income ÷ (RPP ÷ 100)

Example: You earn $85,000 and are considering three cities:

• Austin, TX - RPP ~105. Real purchasing power: $85,000 ÷ 1.05 = $80,952
• Phoenix, AZ - RPP ~98. Real purchasing power: $85,000 ÷ 0.98 = $86,735
• Columbus, OH - RPP ~91. Real purchasing power: $85,000 ÷ 0.91 = $93,407

Same salary, same person, but Columbus delivers 15% more real purchasing power than Austin. That gap compounds significantly over a career. Use metro pages to look up RPP for any metro.

Step 2, Set Hard Filters

Hard filters eliminate metros before you invest time in analysis. Common filter thresholds:

• Housing: Eliminate metros where median 2BR rent exceeds 35% of your gross monthly income.
• Crime: Eliminate metros where violent crime rate exceeds 600 per 100K (roughly 1.5x national average).
• Climate: Eliminate metros where average January low falls below your cold tolerance, or July high above your heat tolerance.
• Jobs: For non-remote workers, eliminate metros without employers in your field, check BLS employment concentration by occupation.

Use PlainCompare rankings to quickly identify which metros clear each threshold. Filter aggressively, you can always relax a filter if too few metros remain.

Step 3, Score Your Shortlist

With hard filters applied, you should have 10-30 metros remaining. Now score each on a 1-5 scale for each dimension, weighted by personal importance. A simple scoring grid works:

1. List your top 10 candidate metros across columns.
2. List the 8 factors down rows.
3. Assign a 1-5 score for each cell (5 = best for you).
4. Multiply each score by your personal weight (1-5).
5. Sum the weighted scores per metro.

The top 3-5 scorers become your finalist list for deeper research. Use side-by-side comparisons to examine finalists dimension by dimension without manual data collection.

Step 4, Research Neighborhoods Within Finalists

Metro-level data is perfect for narrowing the field but too coarse for final decisions. A metro's average crime rate may be acceptable, but a specific neighborhood could be significantly higher or lower. Once you have 3-5 finalist metros:

• Research neighborhood-level crime data through local police department portals.
• Check school ratings at the district and individual school level (GreatSchools, NCES school finder).
• Walk or drive through target neighborhoods using Google Street View for initial screening.
• Check commute time from candidate neighborhoods to your likely workplace using Google Maps at peak hours.
• Research local employer concentration, LinkedIn and Glassdoor show job density by city.

Step 5, Visit Before Committing

Data eliminates bad options. Experience confirms good ones. Before signing a lease or making an offer, spend 2-3 days in each finalist city. Prioritize during your visit:

• Drive your likely commute route during rush hour, traffic patterns are invisible in data.
• Visit grocery stores, gyms, and restaurants in target neighborhoods to gauge cost and culture.
• Spend time in the neighborhoods you are considering at different times of day and on a weekend.
• Attend a local event, visit a park, explore the downtown, assess whether the city's energy fits your lifestyle.
• If possible, meet people who live there. Ask what they genuinely dislike, locals give honest answers that no dataset can.

The best relocation decisions combine rigorous data screening with a boots-on-the-ground reality check. No spreadsheet captures whether you will feel at home.

Remote Work Changes the Calculus

For fully remote workers, the job market dimension drops from "High" to near-zero weight, and cost of living becomes the dominant factor. Remote workers can unlock metros that would be inaccessible for in-person workers, college towns with excellent quality of life, Sun Belt metros with high growth and moderate cost, or smaller Midwest cities with exceptional purchasing power. If your income is decoupled from geography, optimize relentlessly for purchasing power, climate preference, and quality of life. The financial advantage over 10 years can compound to hundreds of thousands of dollars in savings.

Frequently Asked Questions