Cost of Living by City
All US cities ranked by cost of living index, from most affordable to most expensive. Click any city to view detailed cost breakdowns, nearby comparisons, and more.
| # | Location | Cost Index |
|---|---|---|
| 1 | Jackson, MS | 80.5 — Very Low |
| 2 | Dayton, OH | 81.5 — Very Low |
| 3 | Memphis, TN | 82.1 — Very Low |
| 4 | Charleston, WV | 82.5 — Very Low |
| 5 | Topeka, KS | 82.5 — Very Low |
| 6 | Peoria, IL | 82.8 — Very Low |
| 7 | Tulsa, OK | 83.8 — Very Low |
| 8 | Mobile, AL | 84.2 — Very Low |
| 9 | Detroit, MI | 84.5 — Very Low |
| 10 | Wichita, KS | 84.5 — Very Low |
| 11 | Cleveland, OH | 84.8 — Very Low |
| 12 | Little Rock, AR | 84.8 — Very Low |
| 13 | Oklahoma City, OK | 85.2 — Very Low |
| 14 | El Paso, TX | 85.5 — Very Low |
| 15 | Cedar Rapids, IA | 85.5 — Very Low |
| 16 | Birmingham, AL | 85.8 — Very Low |
| 17 | Springfield, IL | 85.8 — Very Low |
| 18 | Fayetteville, AR | 86.5 — Very Low |
| 19 | Knoxville, TN | 86.5 — Very Low |
| 20 | Augusta, GA | 86.5 — Very Low |
| 21 | St. Louis, MO | 87.3 — Very Low |
| 22 | Buffalo, NY | 87.5 — Very Low |
| 23 | Huntsville, AL | 88.2 — Very Low |
| 24 | Chattanooga, TN | 88.2 — Very Low |
| 25 | Lincoln, NE | 88.2 — Very Low |
| 26 | Des Moines, IA | 88.5 — Very Low |
| 27 | Louisville, KY | 88.5 — Very Low |
| 28 | San Antonio, TX | 88.7 — Very Low |
| 29 | Grand Rapids, MI | 89.2 — Very Low |
| 30 | Fargo, ND | 89.2 — Very Low |
| 31 | Indianapolis, IN | 89.5 — Very Low |
| 32 | Columbia, SC | 89.5 — Very Low |
| 33 | Omaha, NE | 89.8 — Very Low |
| 34 | Milwaukee, WI | 89.8 — Very Low |
| 35 | Baton Rouge, LA | 89.8 — Very Low |
| 36 | Tucson, AZ | 90.2 — Below Average |
| 37 | Kansas City, MO | 90.5 — Below Average |
| 38 | Sioux Falls, SD | 90.5 — Below Average |
| 39 | Lexington, KY | 91.2 — Below Average |
| 40 | Madison, AL | 91.2 — Below Average |
| 41 | Albuquerque, NM | 91.5 — Below Average |
| 42 | Columbus, OH | 91.8 — Below Average |
| 43 | Fort Worth, TX | 91.8 — Below Average |
| 44 | Savannah, GA | 92.5 — Below Average |
| 45 | Cheyenne, WY | 92.5 — Below Average |
| 46 | Pensacola, FL | 92.5 — Below Average |
| 47 | Pittsburgh, PA | 92.8 — Below Average |
| 48 | Myrtle Beach, SC | 93.5 — Below Average |
| 49 | Jacksonville, FL | 93.8 — Below Average |
| 50 | Houston, TX | 94.5 — Below Average |
| 51 | Greenville, SC | 94.5 — Below Average |
| 52 | Billings, MT | 94.8 — Below Average |
| 53 | New Orleans, LA | 95.8 — Below Average |
| 54 | Dallas, TX | 96.2 — Below Average |
| 55 | Spokane, WA | 96.5 — Below Average |
| 56 | Fresno, CA | 97.2 — Average |
| 57 | Cape Coral, FL | 98.2 — Average |
| 58 | Charlotte, NC | 98.5 — Average |
| 59 | Las Vegas, NV | 98.5 — Average |
| 60 | Durham, NC | 99.8 — Average |
| 61 | Orlando, FL | 100.2 — Average |
| 62 | Provo, UT | 100.5 — Average |
| 63 | Raleigh, NC | 100.8 — Average |
| 64 | Nashville, TN | 101.3 — Average |
| 65 | Tampa, FL | 101.5 — Average |
| 66 | Richmond, VA | 101.5 — Average |
| 67 | Phoenix, AZ | 101.8 — Average |
| 68 | Wilmington, DE | 101.8 — Average |
| 69 | Boise, ID | 102.5 — Average |
| 70 | Madison, WI | 102.5 — Average |
| 71 | Colorado Springs, CO | 103.5 — Above Average |
| 72 | Atlanta, GA | 104.2 — Above Average |
| 73 | Baltimore, MD | 104.5 — Above Average |
| 74 | Minneapolis, MN | 104.8 — Above Average |
| 75 | Salt Lake City, UT | 105.2 — Above Average |
| 76 | Concord, NH | 105.2 — Above Average |
| 77 | Asheville, NC | 105.5 — Above Average |
| 78 | Austin, TX | 105.8 — Above Average |
| 79 | Virginia Beach, VA | 105.8 — Above Average |
| 80 | Chicago, IL | 107.3 — Above Average |
| 81 | Charleston, SC | 108.2 — Above Average |
| 82 | Eugene, OR | 108.2 — Above Average |
| 83 | Philadelphia, PA | 108.5 — Above Average |
| 84 | Reno, NV | 108.5 — Above Average |
| 85 | Manchester, NH | 108.5 — Above Average |
| 86 | Providence, RI | 108.8 — Above Average |
| 87 | Hartford, CT | 110.5 — Above Average |
| 88 | Sarasota, FL | 110.5 — Above Average |
| 89 | Burlington, VT | 112.5 — Above Average |
| 90 | Portland, ME | 115.2 — High |
| 91 | Denver, CO | 116.3 — High |
| 92 | Scottsdale, AZ | 118.5 — High |
| 93 | Sacramento, CA | 118.5 — High |
| 94 | Bozeman, MT | 118.5 — High |
| 95 | Tacoma, WA | 118.5 — High |
| 96 | Newark, NJ | 120.5 — High |
| 97 | Naples, FL | 125.5 — High |
| 98 | Portland, OR | 125.6 — High |
| 99 | Anchorage, AK | 127.2 — High |
| 100 | Miami, FL | 128.5 — High |
| 101 | Jersey City, NJ | 138.5 — Very High |
| 102 | Washington, DC | 148.7 — Very High |
| 103 | Boston, MA | 152.4 — Very High |
| 104 | Seattle, WA | 152.8 — Very High |
| 105 | San Diego, CA | 155.3 — Very High |
| 106 | Los Angeles, CA | 166.2 — Very High |
| 107 | New York City, NY | 187.2 — Very High |
| 108 | San Jose, CA | 192.1 — Very High |
| 109 | Honolulu, HI | 192.9 — Very High |
| 110 | San Francisco, CA | 196.8 — Very High |
How to Read City-Level Cost of Living Data
Understanding cost of living data begins with understanding the cost of living index. This index uses the national average as a baseline, set at 100. A city with a cost index of 85 is 15 percent cheaper than the national average, while a city with an index of 130 is 30 percent more expensive. This simple number provides an immediate snapshot of how a city's costs compare to the country as a whole, but it is only the starting point of a thorough analysis.
The overall index is calculated as a weighted composite of several spending categories: housing, groceries, utilities, transportation, healthcare, and miscellaneous goods and services. Housing typically carries the heaviest weight because it is the largest single expense for most households. This means that a city with extremely cheap housing but expensive groceries and utilities might still show a low overall index, even though your day-to-day spending feels higher than expected.
To use cost-of-living data effectively, always look beyond the headline number. Examine the category-level indexes to understand where a city is cheap and where it is expensive. A city with an overall index of 90 might have housing at 70 (very cheap) and healthcare at 115 (above average). For a young, healthy renter, that city is a bargain. For a retiree with chronic health conditions, the elevated healthcare costs could more than offset the housing savings. Your personal spending profile determines which categories matter most, and the city-level data lets you match locations to your actual financial life.
It is also important to understand what the data does not capture. Cost of living indexes do not reflect the quality of goods and services, only their price. A haircut in New York City costs more than a haircut in rural Arkansas, but the New York salon may also offer a very different experience. The indexes do not account for income taxes, property taxes, or sales taxes, which can add 5 to 15 percent to your effective cost of living depending on the state. And they do not measure quality of life factors like safety, climate, cultural amenities, or commute times, all of which affect how far your money feels like it goes.
The Wide Spectrum of American City Costs
The United States contains an extraordinary range of living costs within a single country. At one end of the spectrum, cities like San Francisco, New York, Honolulu, and San Jose have cost indexes ranging from 150 to over 190, meaning they are 50 to 90 percent more expensive than the national average. At the other end, cities like McAllen, Texas; Harlingen, Texas; Memphis, Tennessee; and Kalamazoo, Michigan have indexes in the 70s and low 80s, meaning they are 20 to 30 percent cheaper than average.
To put this in concrete terms: a household spending $5,000 per month in a city with an index of 100 would need approximately $7,500 per month to maintain the same standard of living in a city with an index of 150, and could live comparably for about $3,750 per month in a city with an index of 75. Over the course of a year, the difference between the most expensive and most affordable cities can exceed $45,000 for the same quality of life.
This spectrum is not just an academic curiosity. It has profound implications for career decisions, retirement planning, remote work, and family finances. A software engineer earning $150,000 in San Francisco might have less disposable income than the same engineer earning $95,000 in Austin, Texas, because the cost savings on housing, transportation, and everyday expenses in Austin more than compensate for the lower salary. Remote workers who are no longer tethered to an expensive metro area have an unprecedented opportunity to perform geographic arbitrage: earning a high-cost-city salary while spending at low-cost-city prices.
Metro Areas vs. City Proper
One of the most common sources of confusion in cost-of-living comparisons is the distinction between a city proper and its metropolitan statistical area (MSA). The city proper refers to the land within the official city limits, which may be geographically small and densely populated (like San Francisco, at just 47 square miles) or sprawling and diverse (like Jacksonville, Florida, at over 840 square miles). The metro area includes the central city plus all surrounding suburbs, exurbs, and satellite towns that are economically integrated with the core city.
For cost-of-living purposes, metro-area data is almost always more useful than city-proper data. Most people who say they live "in" a particular city actually live in its suburbs. A family might say they live in Atlanta but actually reside in Marietta, Roswell, or Alpharetta, each of which has different housing costs, property tax rates, and school quality than Atlanta proper. Metro-area data captures the full range of living costs across the region, providing a more representative picture of what it actually costs to live there.
That said, intra-metro variation can be significant. Living in downtown Denver is meaningfully more expensive than living in a suburb like Thornton or Arvada, even though both are within the Denver metro area. When using our data to plan a move, treat the metro-level cost index as a starting point, then research specific neighborhoods and suburbs within the metro to refine your estimate. The metro data tells you the general price range; local research narrows it down to your actual likely costs.
Population Size and Cost of Living
There is a well-documented correlation between a city's population and its cost of living, but the relationship is not as simple as "bigger means more expensive." In general, the largest metro areas in the country (New York, Los Angeles, Chicago, San Francisco, Washington D.C.) do have above-average costs, driven primarily by housing demand and land scarcity. When millions of people want to live in a geographically constrained area, competition for housing drives prices up, and everything from restaurant meals to daycare follows.
However, many mid-sized and even large metros break this pattern. Houston, the fourth-largest metro in the country, has a cost of living near or slightly below the national average, thanks to an enormous geographic footprint, pro-development zoning policies, and no state income tax. San Antonio, Indianapolis, Columbus (Ohio), and Kansas City are all among the 30 largest metros in the country yet maintain cost indexes below 100. These cities demonstrate that population size alone does not determine affordability; land-use policy, housing supply, local tax structures, and economic diversity all play critical roles.
At the small end of the spectrum, very small cities (under 100,000 population) tend to be affordable, but they may lack the healthcare infrastructure, job market, cultural amenities, and transportation options that larger cities provide. The sweet spot for many people is a mid-sized metro (250,000 to 1 million population) that is large enough to offer good hospitals, diverse restaurants, reasonable airports, and cultural events, yet small enough to avoid the congestion, housing inflation, and general intensity of a mega-city.
Fast-Growing Cities and Rising Costs
Population growth is the single biggest driver of cost-of-living increases in previously affordable cities. When a city experiences a sustained influx of new residents, especially from higher-cost metros, demand for housing outpaces supply, and prices climb. This pattern has played out dramatically in cities like Austin, Texas; Boise, Idaho; Nashville, Tennessee; and Raleigh, North Carolina over the past decade.
Austin is perhaps the most striking example. In 2015, Austin's cost of living was modestly above the national average. By 2024, a flood of tech-industry relocations, remote workers, and corporate headquarters moves (Tesla, Oracle, Samsung) had pushed median home prices above $400,000 and driven the overall cost index significantly higher. Longtime residents who praised Austin's affordability a decade ago now describe it as expensive by Texas standards, though it remains cheaper than the Bay Area or Seattle that many of its new residents came from.
For people planning a move, understanding growth trends is essential. A city that looks affordable today may not be affordable in five years if it is growing at 3 to 5 percent per year. Conversely, a city with stable or slow growth is more likely to maintain its affordability over time. Look at population growth rates, net domestic migration (how many Americans are moving in vs. out), and housing permit data (whether construction is keeping pace with demand). Cities that are building enough new housing to absorb their growth tend to experience more moderate price increases than those where NIMBYism, geographic constraints, or regulatory barriers limit new construction.
If you are considering a fast-growing city, act sooner rather than later. Housing prices in high-growth markets tend to rise steadily, and waiting even two or three years can mean paying significantly more for the same home. Alternatively, look at the secondary cities near a growth hot spot. When Austin became expensive, nearby cities like San Marcos, New Braunfels, and Round Rock absorbed spillover demand while remaining more affordable.
Using City Data to Plan Your Next Move
Having access to cost-of-living data for over 100 cities is powerful, but data alone does not make a decision. Turning numbers into a smart relocation plan requires a structured approach that combines quantitative analysis with qualitative research and personal reflection.
Step 1: Define your budget. Before comparing cities, know your financial parameters. What is your total household income (or expected retirement income)? What percentage of income are you willing to allocate to housing? How much do you need to save or invest each month? A clear budget gives you a filter: any city where your essential expenses exceed your income or leave too little cushion can be eliminated immediately.
Step 2: Identify your non-negotiables. Everyone has factors they will not compromise on. For some, it is proximity to family. For others, it is climate, access to a specific type of healthcare, a particular school district, or walkability. List your three to five non-negotiables before looking at cost data. This prevents you from falling in love with a cheap city that fails on something fundamental.
Step 3: Create a shortlist using data. Use the cost-of-living rankings on this page to identify cities that fit your budget and meet your non-negotiable criteria. Sort by overall cost index, but also examine the breakdowns for housing, healthcare, and any other category that weighs heavily in your personal spending. Aim for a shortlist of five to ten cities.
Step 4: Research beyond the numbers. For each city on your shortlist, investigate the qualitative factors that data cannot capture. Read local news, join community forums or social media groups, look at crime statistics, research school ratings if you have children, check the job market if you are still working, and review healthcare facility ratings. Google Street View and YouTube walking tours can give you a visual feel for neighborhoods without visiting in person.
Step 5: Visit before committing. No amount of online research replaces the experience of spending time in a city. If possible, visit your top two or three candidates for several days each, ideally during different seasons. Stay in the neighborhood you would actually live in, not a tourist district. Eat at everyday restaurants, drive the commute you would drive, visit the grocery stores and parks and medical offices. Pay attention to how the city feels, not just how it looks on paper.
Step 6: Model the full financial impact. Before making a final decision, build a detailed budget for your life in the new city. Account for housing costs, taxes (income, property, and sales), insurance (auto, home, health), utilities, groceries, and transportation. Compare this budget to your current expenses and your income to confirm that the move makes financial sense. If you are moving for a job, factor in the salary difference alongside the cost-of-living difference to calculate your net gain or loss in purchasing power.
The table above gives you the starting data. The rest is a process of narrowing, investigating, and confirming until you find the city that best aligns with your finances, your priorities, and your vision for the life you want to live.