Median household income is the income level at which half of a suburb’s households earn more and half earn less. When incomes are unevenly spread, the mean (average) can sit well above what a typical household earns. For comparing suburbs within one metro area, the median is the more reliable figure, and the mean is only useful when the high-income tail is itself what you want to measure.
Why median household income beats the average for comparing suburbs
How high-income outliers distort the mean
Household income distributions in affluent suburbs are skewed: most households cluster in a middle range, while a small group earns far more. The mean adds every household’s income and divides by the number of households, so each high earner adds directly to the result. A few very large incomes can raise the mean by a noticeable margin while changing the income of the typical household very little.
The median ignores how far the top values extend. It depends only on the household in the middle of the sorted list, so moving a top earner higher does not change it.
Mean versus median in one suburb
To compare the two measures for a single suburb:
- Sort all household incomes from lowest to highest.
- Sum all incomes and divide by the number of households to get the mean.
- Find the middle value of the sorted list to get the median.
- Note how far apart the two figures are. A large gap signals a skewed distribution.
Take a suburb where most households have similar incomes and one household earns several times more than the rest. Adding that household to the total lifts the mean, but the middle of the sorted list is still set by the typical households, so the median stays close to the cluster. The gap between the two figures is the signal that a few high earners are doing the work.
What the GOBankingRates ranking shows
The GOBankingRates ranking of the wealthiest suburb in each of the 50 largest U.S. metros used U.S. Census Bureau 2023 American Community Survey data, but it ranked suburbs by household mean income, not median. Its entry for Hinsdale, Illinois (Chicago area), about $376,000, is therefore a mean-based figure. It can be useful for identifying suburbs with large high-income groups, but it does not describe the income of the typical household there.
| Measure | How it is calculated | Effect of a few very high incomes | Best use |
|---|---|---|---|
| Mean | Total household income divided by number of households | Pulled upward, sometimes sharply | Rankings where top earners are the point of the comparison |
| Median | Income of the middle household in the sorted list | Barely changed | Comparing the typical household across suburbs |
Where can you find median household income for each suburb?
The primary source is the U.S. Census Bureau’s American Community Survey (ACS), published through data.census.gov. To pull the figure for each suburb, use the table B19013, titled Median Household Income in the Past 12 Months. Each suburb you compare needs its own row, so the work is mostly about matching geography correctly.
- Open data.census.gov and search for “B19013”.
- Open the table and select the geography level. For suburbs, choose place geography, which covers incorporated cities, towns, and Census-designated places. Places usually carry the names residents use.
- Filter to the state and the counties that make up your metro, then select the suburbs on your comparison list.
- Export the results as a spreadsheet and record the release year next to each value, so every suburb is compared against the same survey period.
County subdivisions are the other option at this level. They are administrative units, so one may contain several communities or may not match the name a suburb is known by. Use them only when a place-level match does not exist for the area you need.
Third-party aggregators republish Census figures, so check which metric and which year they actually report.
| Source | What it gives you | Check before use |
|---|---|---|
| data.census.gov, table B19013 | Median household income by place or county subdivision | Geography level and release year |
| GOBankingRates ranking (via AOL, 2025) | Wealthiest suburb per large metro, using ACS 2023 data and Zillow 2025 home values | The ranking’s method and licensing terms |
ACS 1-year or 5-year estimates: which should you use?
The 1-year estimates cover only areas of 65,000 people or more. Most large cities and their larger suburbs qualify, but many small suburbs do not, so they will not appear in 1-year tables at all. The advantage is recency: 1-year estimates reflect a single survey year, and the release lag is roughly one year after collection.
The 5-year estimates pool five years of survey responses and cover all areas, including small suburbs. Pooling gives a larger sample for each geography, which is why they are the only ACS option for many small places. The trade-off is timeliness. A 5-year figure describes an average over the whole period, so it can lag actual conditions in fast-changing suburbs.
| Factor | 1-year estimates | 5-year estimates |
|---|---|---|
| Minimum area population | 65,000 or more | All areas, including small suburbs |
| Survey years pooled | One year | Five years |
| Release lag | Roughly one year after collection | Released after collection, and the figure averages a five-year window |
| Best use | Large suburbs, recent trends | Small suburbs, complete metro coverage |
For a comparison across all suburbs of a metro area, the practical approach is to use the 5-year estimates so that every suburb is included. Use 1-year estimates only when every suburb you compare meets the 65,000-person threshold, and do not mix the two vintages in one ranking without labelling them.
How do you define the suburbs of a metro area?
A suburb has no single legal definition. In U.S. statistics, the Census Bureau groups counties into Core Based Statistical Areas (CBSAs), which come in two types: metropolitan statistical areas, built around an urban core, and micropolitan statistical areas, built around smaller urban cores. The metro boundary sets the outer limit, and the places inside it are what most people call the suburbs.
Inside a CBSA, the place-level units that matter for comparison are:
| Unit | What it is | Comparison use |
|---|---|---|
| Incorporated city | A place with its own municipal government and legal boundary | Clear borders, usually the easiest unit to name and map |
| Census designated place (CDP) | A named, unincorporated community with no municipal government | Common in unincorporated suburbs; boundaries are statistical, not legal |
| Township (county subdivision) | A civil division in some states, with its own boundary. In Census geography it is a county subdivision, not a place | Can cover several cities or unincorporated areas, so check whether a source reports it or the places inside it |
When two suburbs sit in different counties, check which CBSA each one falls in before comparing them. Membership is set by the delineation, not by the name of the place or by local perception.
Delineation revisions change the lists. The Census Bureau and the Office of Management and Budget update CBSA definitions periodically, and a place can move in or out of a metro area between versions. A suburb that appeared in one year’s ranking may be absent or relabeled in another, even though nothing changed on the ground. Before comparing suburbs across years, confirm which delineation vintage each source used.
Are the differences between suburbs statistically meaningful?
A gap in median household income between two suburbs counts as real only if it is larger than the sampling error in the American Community Survey (ACS) estimates. Census figures for small areas are sample-based, so each published value carries a margin of error (MOE), and two suburbs can look different only because of that noise.
The test runs in three steps:
- Convert each MOE to a standard error (SE). The ACS publishes its margins of error at a 90 percent confidence level by default. To get the standard error, divide the published MOE by 1.645, the z-value for 90 percent confidence. Example: an MOE of 4,000 gives an SE of about 2,432.
- Combine the two standard errors. For the difference between two suburbs, take the square root of the sum of the squared standard errors: SE_diff = square root of (SE_A squared + SE_B squared).
- Compare the gap to the combined error. Multiply SE_diff by 1.645 to get the 90 percent margin for the difference. If the absolute income gap is larger than that margin, the difference is statistically distinguishable at the 90 percent level. If it is smaller, the suburbs cannot be separated with confidence.
| Input | Source on the ACS table | Used for |
|---|---|---|
| Median income estimate | Estimate column | Value being compared |
| Margin of error | MOE column | Divided by 1.645 to get SE |
| Combined SE | Square root of summed squared SEs | Scales the test |
| Threshold | SE_diff × 1.645 | Gap must exceed this |
Rankings built from single published values, such as the GOBankingRates list of the wealthiest suburb in each of the 50 largest U.S. metros, do not report margins of error in the source data provided, so the gap between the top suburbs in such a list cannot be tested from the ranking alone.
How do income, home prices and cost of living fit together?
Income and home prices are the two inputs of the price-to-income ratio, the simplest way to test how affordable a suburb is for its typical household. The ratio shows how many years of median household income it would take to pay for a median-priced home, before financing costs. Two suburbs with the same median income can differ sharply in affordability if their home values differ, and the reverse is also true.
Calculating the price-to-income ratio
Use the same geography for both inputs, meaning the suburb’s own figures rather than metro or county averages.
- Step 1: Take the median home value for the suburb.
- Step 2: Take the median household income for the same suburb.
- Step 3: Divide the home value by the income: median home value / median household income.
- Step 4: Compare the result across suburbs. A higher number means a home costs more years of typical income.
Using the Zillow Home Value Index as a benchmark
The Zillow Home Value Index is a published home value series, and the GOBankingRates ranking used its 2025 values. It is a different measure from a Census median home value, so state which one you use and keep it the same for every suburb. A home value series and an income series are rarely collected on the same schedule, so each suburb’s ratio should be read with its source dates in view.
Where the ratio stops
The ratio captures only two numbers. Several cost of living gaps sit outside it:
| Factor | Reflected in income or home value? | Why it matters for comparison |
|---|---|---|
| Property taxes | Only indirectly | Two suburbs with the same ratio can carry different annual tax bills |
| Commuting cost | No | Distance to work changes the real cost of living in a suburb |
| Insurance and utilities | No | Local conditions shift recurring housing costs |
| Local services and schools | No | Costs and quality vary without appearing in either input |
Treat the ratio as a first filter. Shortlist suburbs with similar ratios, then compare the recurring costs in the table before drawing a conclusion about affordability.
Common mistakes when comparing suburbs
Most errors in suburb income comparisons come from mixing figures that look alike but measure different things or cover different years. The three errors below are the most frequent, and each has a direct check.
Reusing mean income figures from rankings as median
Before adding a ranking figure to a median column, check the metric label in the original table. If the label says “mean,” keep it in a mean column or drop it from the comparison.
Mixing single-year and multi-year estimates in one table
A table that places a single-year estimate next to a multi-year average compares different time windows. Those two sources cover different dates, so a ratio built from them mixes a 2023 figure with a 2025 one. The sources provided do not say whether the ACS data were 1-year or 5-year estimates.
To avoid this, give every row in a table the same reference period. If a suburb has only a multi-year estimate and the others have single-year figures, either mark the difference in a note or leave that suburb out of the direct comparison.
Skipping the source year printed on each table
Some tables show the statistic and place without the year of the underlying survey. Without the year, readers cannot tell whether a gap between two suburbs reflects real change or two different survey vintages. Record the source name and year for each column before comparing values, and keep them in the table header or a footnote so they travel with the figures when the table is reused.
