Alaska, Hawaii, and Connecticut were the states with the highest average monthly utility costs in the United States in 2023. Residents paid about ****** U.S. dollars for their electricity bills in Hawaii, while the average monthly bill for natural gas came to *** U.S. dollars. This was significantly higher than in any other state. Bigger homes have higher utility costs Despite regional variations, single-family homes in the United States have grown bigger in size since 1975. This trend also means that, unless homeowners invest in energy savings measures, they will have to pay more for their utility costs. Which are the most affordable states to live in? According to the cost of living index, the three most affordable states to live in are Mississippi, Kansas, and Oklahoma. At the other end of the scale are Hawaii, District of Columbia, and New York. The index is based on housing, utilities, grocery items, transportation, health care, and miscellaneous goods and services. To buy a median priced home in Kansas City, a prospective home buyer will have to earn an annual salary of about ****** U.S. dollars.

Hawaii is the state with the highest household electricity price in the United States. In September 2024, the average retail price of electricity for Hawaiian residences amounted to 41.27 U.S. cents per kilowatt-hour. California followed in second, with 30.221 U.S. cents per kilowatt-hour. Meanwhile, Utah registered the lowest price in the period, at around 11.4 U.S. cents per kilowatt-hour. Why is electricity so expensive in Hawaii? Fossil fuels, and specifically oil, account for approximately 80 percent of Hawaii’s electricity mix, so the electricity price in this state can be roughly brought down to the price of oil in the country. Oil was by far the most expensive fossil fuel used for electricity generation in the country. As Hawaii depends on oil imports, the cost of transportation and infrastructure must be added to the oil price. Electricity prices worldwide The U.S. retail price for electricity increased almost every year since 1990. In 2023, it stood at 12.7 U.S. cents per kilowatt-hour, almost double the charge put on electricity back in 1990. However, household electricity prices are around 25 U.S. dollar cents per kilowatt-hour lower in the U.S. when compared to European countries reliant on energy imports, such as Germany and Italy.

A comprehensive dataset of average residential, commercial, and combined electricity rates in cents per kWh for all 50 U.S. states.

A table listing the average electricity rates (kWh) of all 50 U.S. states as of March 2025.

Annual data on the average price of retail electricity to consumers. Data organized by U.S. state and by provider, i.e., total electric industry, full-service providers, restructured retail service providers, energy-only providers, and delivery-only service. Annual time series extend back to 1990. Based on Form EIA-861 data.

In 2022, industrial electricity prices were highest in the state of Hawaii, located offshore the Pacific coast of the United States. In Hawaii, electricity prices for industries amounted to ***** U.S. dollar cents per kilowatt-hour that year. Alaska, Rhode Island, and Massachusetts followed with roughly ** U.S. dollar cents per kilowatt-hour each. Hawaii accounted for the highest residential electricity price in the U.S., as well.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

Graph and download economic data for Unit Labor Costs for Utilities: Utilities (NAICS 22) in the United States (IPUCN22U101000000) from 1988 to 2024 about unit labor cost, utilities, NAICS, and USA.

In 2019, the average apartment dweller in Alaska paid 496 U.S. dollars per month for utilities, whereas monthly utilities only cost Californians 320 U.S. dollars. Southern states, in addition to Alaska and Hawaii, tended to have the highest costs for utilities in the United States.

Graph and download economic data for Average Price: Electricity per Kilowatt-Hour in U.S. City Average (APU000072610) from Nov 1978 to May 2025 about electricity, energy, retail, price, and USA.

A table showing the average monthly electricity bills for residential and commercial customers by state in the United States, along with a combined average, based on data from ElectricChoice.com as of May 2025.

United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data was reported at 12.890 USD/kWh in 2017. This records an increase from the previous number of 12.550 USD/kWh for 2016. United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data is updated yearly, averaging 7.565 USD/kWh from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 12.890 USD/kWh in 2017 and a record low of 2.200 USD/kWh in 1970. United States Electric Retail Price: Sold by Electric Utilities: Avg: Residential data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.P011: Electricity Price.

United States Electric Retail Price: Sold by Electric Utilities: Avg: Industrial data was reported at 6.880 USD/kWh in 2017. This records an increase from the previous number of 6.760 USD/kWh for 2016. United States Electric Retail Price: Sold by Electric Utilities: Avg: Industrial data is updated yearly, averaging 4.710 USD/kWh from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 7.100 USD/kWh in 2014 and a record low of 1.000 USD/kWh in 1970. United States Electric Retail Price: Sold by Electric Utilities: Avg: Industrial data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.P011: Electricity Price.

In 2022, commercial electricity prices amounted to ***** U.S. dollar cents per kilowatt-hour in the United States' state of Hawaii. This was by far the highest electricity price in the country, followed by California with over ** U.S. dollar cents per kilowatt-hours. The state of Hawaii accounted for the highest residential and industrial electricity price in the U.S., as well.

The Utility Rate Database (URDB) is a free storehouse of rate structure information from utilities in the United States. Here, you can search for your utilities and rates to find out exactly how you are charged for your electric energy usage. Understanding this information can help reduce your bill, for example, by running your appliances during off-peak hours (times during the day when electricity prices are less expensive) and help you make more informed decisions regarding your energy usage.

Rates are also extremely important to the energy analysis community for accurately determining the value and economics of distributed generation such as solar and wind power. In the past, collecting rates has been an effort duplicated across many institutions. Rate collection can be tedious and slow, however, with the introduction of the URDB, OpenEI aims to change how analysis of rates is performed. The URDB allows anyone to access these rates in a computer-readable format for use in their tools and models. OpenEI provides an API for software to automatically download the appropriate rates, thereby allowing detailed economic analysis to be done without ever having to directly handle complex rate structures. Essentially, rate collection and processing that used to take weeks or months can now be done in seconds!

NREL’s System Advisor Model (formerly Solar Advisor Model or SAM), currently has the ability to communicate with the OpenEI URDB over the internet. SAM can download any rate from the URDB directly into the program, thereby enabling users to conduct detailed studies on various power systems ranging in size from a small residential rooftop solar system to large utility scale installations. Other applications available at NREL, such as OpenPV and IMBY, will also utilize the URDB data.

Upcoming features include better support for entering net metering parameters, maps to summarize the data, geolocation capabilities, and hundreds of additional rates!

This data collection provides information on characteristics of housing units in 11 selected Metropolitan Statistical Areas (MSAs) of the United States. Although the unit of analysis is the housing unit rather than its occupants, the survey also is a comprehensive source of information on the demographic characteristics of household residents. Data collected include general housing characteristics such as the year the structure was built, type and number of living quarters, occupancy status, presence of commercial establishments on the property, and property value. Data are also provided on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air-conditioning equipment. Questions about housing quality include condition of walls and floors, adequacy of heat in winter, availability of electrical outlets in rooms, basement and roof water leakage, and exterminator service for mice and rats. Data related to housing expenses include mortgage or rent payments, utility costs, fuel costs, property insurance costs, real estate taxes, and garbage collection fees. Variables are also supplied on neighborhood conditions such as quality of roads and presence of crime, trash, litter, street noise, abandoned structures, commercial activity, and odors or smoke, as well as the adequacy of services such as public transportation, schools, shopping facilities, police protection, recreation facilities, and hospitals or clinics. In addition to housing characteristics, data on age, sex, race, marital status, income, and relationship to householder are provided for each household member. Additional data are supplied for the householder, including years of school completed, Spanish origin, and length of residence.

This spreadsheet contains information reported by over 200 investor-owned utilities to the Federal Energy Regulatory Commission in the annual filing FERC Form 1 for the years 1994-2019. It contains 1) annual capital costs for new transmission, distribution, and administrative infrastructure; 2) annual operation and maintenance costs for transmission, distribution, and utility business administration; 3) total annual MWh sales and sales by customer class; 4) annual peak demand in MW; and 5) total customer count and the number of customers by class.

Annual spending on new capital infrastructure is read from pages 204 to 207 of FERC Form 1, titled Electric Plant in Service. Annual transmission capital additions are recorded from Line 58, Column C - Total Transmission Plant Additions. Likewise, annual distribution capital additions are recorded from Line 75, Column C - Total Distribution Plant Additions. Administrative capital additions are recorded from Line 5, Column C - Total Intangible Plant Additions, and Line 99, Column C - Total General Plant Additions.

Operation and maintenance costs associated with transmission, distribution, and utility administration are read from pages 320 to 323 of FERC Form 1, titled Electric Operation and Maintenance Expenses. Annual transmission operation and maintenance are recorded from Line 99, Column B - Total Transmission Operation Expenses for Current Year, and Line 111, Column B - Total Transmission Maintenance Expenses for Current Year. Likewise, annual distribution operation and maintenance costs are recorded from Line 144, Column B - Total Distribution Operation Expenses, and Line 155, Column B - Total Distribution Maintenance Expenses. Administrative operation and maintenance costs are recorded from: Line 164, Column B - Total Customers Accounts Expenses; Line 171, Column B - Total Customer Service and Information Expenses; Line 178, Column B - Total Sales Expenses; and Line 197, Column B - Total Administrative and General Expenses.

The annual peak demand in MW over the year is read from page 401, titled Monthly Peaks and Output. The monthly peak demand is listed in Lines 29 to 40, Column D. The maximum of these monthly reports during each year is taken as the annual peak demand in MW. The annual energy sales and customer count data come from page 300, Electric Operating Revenues. The values are provided in Line 2 - Residential Sales, Line 4 - Commercial Sales, Line 5 - Industrial Sales, and Line 10 - Total Sales to Ultimate Consumers.

More information about the database is available in an associated report published by the University of Texas at Austin Energy Institute: https://live-energy-institute.pantheonsite.io/sites/default/files/UTAustin_FCe_TDA_2016.pdf

Also see an associated paper published in the journal Energy Policy:

Fares, Robert L., and Carey W. King. "Trends in transmission, distribution, and administration costs for US investor-owned electric utilities." Energy Policy 105 (2017): 354-362. https://doi.org/10.1016/j.enpol.2017.02.036

All data come from the Federal Energy Regulatory Commission FERC Form 1 Database available in Microsoft Visual FoxPro Format: https://www.ferc.gov/docs-filing/forms/form-1/data.asp

Characteristics of the United States housing inventory listed in this file include the age, size, and type of living quarters, property values, and the presence of commercial establishments on the property. Additional data focus on the presence and condition of kitchen and plumbing facilities and the type and cost of utilities, as well as housing expenses, property repair or alteration, and insurance costs. Many of the same characteristics are given for housing previously occupied by recent movers. Information on age, sex, race, marital status, and income is provided for each household member, with additional data on education, Spanish origin, and household tenure for the head of household. Indicators provided for housing quality include privacy and structural condition. For neighborhood quality, indicators assess noise, crime, air quality, and the presence of abandoned structures, along with the adequacy of neighborhood services such as police protection, parks, health care, and public transportation.

This data collection provides information on the characteristics of the housing inventory in 12 Standard Metropolitan Statistical Areas (SMSAs). Data include year the structure was built, type and number of living quarters, occupancy status, presence of commercial establishments on the property, presence of a garage, and property value. Additional data focus on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air conditioning equipment. Information about housing expenses includes mortgage or rent payments, utility costs, garbage collection fees, property insurance, and real estate taxes as well as repairs, additions, or alterations to the property. Similar data are provided for housing units previously occupied by respondents who had recently moved. Indicators of housing and neighborhood quality are also supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, presence of cracks or holes in walls, ceilings, or floor, reliability of plumbing and heating equipment, and concealed electrical wiring. The presence of storm doors and windows and insulation was also noted. Neighborhood quality variables indicate presence of and objection to street noise, odors, crime, litter, and rundown and abandoned structures, as well as the adequacy of street lighting, public transportation, public parks, schools, shopping facilities, and police and fire protection. Extensive information on the ability of handicapped persons to move around their homes is also provided. Respondents were asked if they needed special equipment, or the help of another person to move around. They were also asked about the presence or need for housing features to aid their movement, such as ramps, braille lettering, elevators, and extra wide doors. In addition to housing characteristics, demographic data for household members are provided, including sex, age, race, income, marital status, and household relationship. Additional data are available for the household head, including Hispanic origin, length of residence, and travel-to-work information.

Note: Find data at source. ・ Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies facing the challenge of above-average electric utility rates.This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.