The Explore Cities tool provides five distinct operation modes to help you research metropolitan cost of living, compare cities, estimate required salaries, analyze housing affordability, and benchmark income distributions. Each mode is powered by public data from the Bureau of Labor Statistics (BLS), Census Bureau, Department of Housing and Urban Development (HUD), and regional price parities published by the Bureau of Economic Analysis (BEA).
Operation Mode 1: City Profile
City Profile mode displays a comprehensive snapshot of a single metro area. You select one city and view its cost-of-living index (relative to the national average = 100), median household income, median rent for 1BR/2BR units, rent-to-income ratio, price parity (PPP), and inflation-adjusted trends over multiple years. This mode is ideal when you want to understand a single city's affordability without comparison, or when you're considering relocation and need baseline statistics for budgeting, salary negotiation, or housing search.
The cost-of-living index aggregates housing, food, transportation, healthcare, and utilities into a single composite score. A value of 120 means the metro is 20% more expensive than the U.S. average; 90 means 10% cheaper. Median household income shows the middle point of all household earnings—half earn more, half earn less—giving you context for salary expectations. Median rent values are drawn from HUD Fair Market Rent (FMR) data and reflect mid-range rental prices (not luxury or subsidized housing). The rent-to-income ratio (rent ÷ income × 100) reveals housing affordability: ratios above 30% indicate cost burden, common in high-demand metros like San Francisco, New York, or Boston.
Operation Mode 2: Compare Cities
Compare Cities mode lets you select two or more metros and view side-by-side statistics: cost-of-living index, median income, median rent, rent burden, price parity, and year-over-year changes. This mode is designed for relocation research—comparing your current city to target destinations—or for employers benchmarking regional compensation, or for investors analyzing rental market dynamics across multiple metros.
The tool highlights deltas between cities: if City A has a COL index of 95 and City B has 115, the delta is +20 points (21% more expensive). Similarly, rent differentials show absolute dollar differences (e.g., $1,200 vs $2,400 = +$1,200/month) and percentage changes. Income gaps are also displayed, revealing whether higher costs are offset by higher wages—critical for assessing real purchasing power after relocation.
You can toggle the renter filter to view only renter households (excluding homeowners) and adjust the inflation year to see historical trends. For example, setting the year to 2020 shows pre-pandemic prices, while 2024 reflects post-inflation adjustments. This temporal comparison helps identify cities where costs have surged (e.g., Boise, Austin, Phoenix) versus those with stable or declining prices (e.g., Detroit, Cleveland, Pittsburgh).
Operation Mode 3: Salary Equivalent (COLA Calculator)
Salary Equivalent mode computes the gross annual salary you would need in City B to maintain the same purchasing power and standard of living you have with a given salary in City A. This is a cost-of-living adjustment (COLA) calculator tailored for job seekers, remote workers negotiating location-based pay, or employers setting regional compensation bands.
You enter your current salary in City A (e.g., $80,000 in Dallas), select City B (e.g., San Francisco), and the tool applies the cost-of-living ratio to estimate the required salary (e.g., $130,000). The calculation uses the formula: Required Salary = Current Salary × (COL Index B ÷ COL Index A). If Dallas has a COL index of 95 and SF has 165, the multiplier is 165 ÷ 95 ≈ 1.74, so $80,000 × 1.74 ≈ $139,200.
The tool optionally includes rent adjustments: if you toggle "Include Rent," the calculator factors in the median rent differential between City A and City B, applying a weighted adjustment (since housing typically represents 30–35% of total spending). This produces a more personalized estimate than the composite COL index alone. For example, if you currently live in a low-rent neighborhood in City A but City B has uniform high rents, the rent-adjusted required salary will be significantly higher than the simple COL ratio suggests.
The output includes a breakdown: base salary equivalent (COL-adjusted), rent adjustment (if enabled), and total required salary. It also shows the percentage increase or decrease relative to your current salary, helping you evaluate whether a job offer in City B meets the threshold for maintaining your current lifestyle. Always add a 10–15% buffer for moving costs, lifestyle changes, and market timing (e.g., arriving during peak rental season).
Operation Mode 4: Housing Focus (Rent Burden & Affordability)
Housing Focus mode isolates rent and income data to calculate rent-to-income ratios, affordable rent thresholds (based on the 30% rule), and housing cost burden prevalence across metros. This mode is optimized for renters planning a move, landlords analyzing market competitiveness, or policymakers studying housing affordability crises.
You select one or more cities and view median rent for 1BR and 2BR units, median household income, and the resulting rent burden ratio. A ratio below 25% indicates affordable housing; 25–30% is moderate burden; 30–40% is cost-burdened (HUD standard); above 40% is severely cost-burdened. The tool color-codes these thresholds: green for affordable, yellow for moderate, orange for burdened, red for severe.
The mode also calculates the minimum income required to afford median rent without exceeding 30% of gross income. For example, if median 2BR rent is $2,400/month, the required annual income is $2,400 × 12 ÷ 0.30 = $96,000. If the metro's median household income is $70,000, there is a $26,000 income gap—indicating that median earners cannot afford median-priced housing without cost burden. This gap metric reveals structural affordability crises in metros like Los Angeles, Miami, and Seattle.
You can filter by renter households only (excluding homeowners) to focus on the rental market. This is useful because median household income includes homeowners who may have fixed-rate mortgages or paid-off homes, which skews the affordability picture for new renters entering the market. The renter-only filter isolates the subset of households competing for rental units, providing a more accurate view of rental affordability.
Operation Mode 5: Income Benchmarks (Percentile Distributions)
Income Benchmarks mode displays household income distribution by percentiles: 10th, 25th, 50th (median), 75th, and 90th percentiles. This mode helps job seekers gauge where their salary falls within a metro's income spectrum, employers benchmark compensation against local market rates, or individuals assess whether their income qualifies them for housing assistance, tax credits, or other income-based programs.
For example, if you earn $60,000 in Austin and the 50th percentile is $78,000, you're below the median—placing you in the bottom half of earners. If the 25th percentile is $45,000, you're above that, meaning you earn more than 25% of households but less than 50%. This context is critical for understanding your relative purchasing power: a $60,000 salary in Austin (high COL, median $78K) has less buying power than $60,000 in Wichita (low COL, median $55K).
The tool displays income benchmarks for each selected metro, allowing you to compare how the same salary ranks across different cities. You can also input your current or target salary and see which percentile bracket it falls into. This is especially useful for remote workers choosing where to live: earning a San Francisco salary ($150K, 50th percentile) while living in Boise ($150K, 90th+ percentile) dramatically increases purchasing power and financial security.
Income benchmarks are sourced from Census Bureau American Community Survey (ACS) data and are updated annually. The tool adjusts for inflation using the Consumer Price Index (CPI), so historical year selections reflect real (inflation-adjusted) income, not nominal values. This ensures apples-to-apples comparisons across years and prevents misleading conclusions from nominal income growth that is offset by inflation.