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 dataset, compiled by NREL using data from ABB, the Velocity Suite and the U.S. Energy Information Administration dataset 861, provides average residential, commercial and industrial electricity rates by zip code for both investor owned utilities (IOU) and non-investor owned utilities. Note: the file includes average rates for each utility, but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database. A more recent version of this data is also available through the NREL Utility Rate API with more search options. This data was released by NREL/Ventyx in February 2011.

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

The retail price for electricity in the United States stood at an average of 12.72 U.S. dollar cents per kilowatt-hour in 2023. This is the highest figure reported in the indicated period. Nevertheless, the U.S. still has one of the lowest electricity prices worldwide. As a major producer of primary energy, energy prices are lower than in countries that are more reliant on imports or impose higher taxes. Electricity prices in the U.S. by consumer group On average, retail electricity prices in the U.S. grew by over 85 percent since the beginning of the century. However, not every sector has been affected equally by the said price increase. U.S. electricity prices for residential customers saw a much steeper increase in the period, while transportation prices increased by approximately 50 percent. Reasons for increases in electricity prices The rising prices are justified by the costs of power production and power grid maintenance. Although the production cost of electricity generated from coal, natural gas, and nuclear sources remained relatively stable, the integration of renewable energy sources, investments in smart grid technologies, growing peak demand, power blackouts caused by natural disasters, and the global energy crisis in 2022 continued to trouble the electric utility industry in recent years. Average U.S. electricity prices per state can also vary widely, with Hawaii residents experiencing some of the highest rates in the country.

These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office.

The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from EIA's Annual Energy Outlook (AEO) Reference Case projections across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.

The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:

• 'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from AEO Summary Table A2.
• 'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.
• 'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each American Institute of Architects (AIA) climate zone. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.
• 'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).
• 'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.

This dataset summarizes the utility use and expenditures data for FY19 through FY23 for Executive Branch agencies. The data was pulled from the state's utility tracking system, EnergyCAP, on February 10, 2025, with the exception of the waste disposal data, which was pulled from Core-CT, the state's human resource management and financials system. The data below may be incomplete and will be updated with more current data as it becomes available.

One in a series of studies on customer response to utility regulatory pricing in early 1975, the Arkansas demonstration project was carried out by the Federal Energy Administration (FEA), the Arkansas Public Service Commission, and Torche Ross and Company, spanning 12 months from February 1976 to January 1977. The study was originally titled the Arkansas Demand Management Study and was an experiment to generate and analyze data on the effects of peak-load pricing on residential electricity consumption. The experimental design featured a time of day peak-load pricing test as well as a seasonal pricing test. Five sets of data resulted from the demonstration: questionnaire survey data from the customers, summary demographic information, utility load reports, weather data, and customer usage records. All five sets are available in this data collection. The questionnaire survey data in Part 1 consists of information gathered from a post experimental survey that includes both control and experimental customers. Parts 3-5 each contain 28 days of data, with Parts 3 and 5 including hourly data. Parts 3-5 also contain identifying information that links their data to the pertinent customer/participant's demographic data in Part 2.

This data set includes the levelized cost of charging (LCOC) and lifetime fuel cost savings (LFCS) values as reported in "Levelized Cost of Charging of Electric Vehicles in the United States." Values are reported at the state and national levels for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). The data set also includes the four annual direct current fast charging (DCFC) station load profiles used to approximate the levelized cost of DCFC charging. Each profile provides 15-min resolved power requirements for one full year. Borlaug, B., Salisbury, S., Gerdes, M., and Muratori, M., Levelized Cost of Charging Electric Vehicles in the United States, Joule (2020), https://doi.org/10.1016/j.joule.2020.05.013.

Wholesale electricity prices in the European Union (EU) increased in 2024 after recovering from the global energy crisis in 2023. This was the result of a myriad of factors, including increased demand in the “post-pandemic” economic recovery, a rise in natural gas and coal prices, and a decline in renewable power generation due to low wind speeds and drought. Nuclear power's critical role In 2023, nuclear and wind were among the leading sources of electricity generation in the EU, accounting for more than one-third of the output. Nuclear energy continues to play a crucial role in the European Union's electricity mix, generating approximately 619 terawatt-hours in 2023, which accounted for about 20 percent of the region's power production. However, the future of nuclear power in Europe is uncertain, with some countries like Germany phasing out their nuclear plants while others maintain their reliance on this energy source. The varied approaches to nuclear power across EU member states contribute to the differences in electricity prices and supply stability throughout the region.

Renewable energy's growing impact As Europe strives to decarbonize its energy sector, renewable sources are gaining prominence. Wind power in Europe, in particular, has seen significant growth, with installed capacity in Europe reaching 257.1 gigawatt hours in 2023. This expansion of renewable energy infrastructure is gradually reshaping the electricity market, potentially leading to more stable prices in the long term. However, the intermittent nature of some renewable sources, such as wind and solar, can still contribute to price fluctuations, especially during periods of low output.

These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treats grid assets as fixed. The Levelized LRMER worksheet within each workbook is set up to produce a levelized long-run marginal emission rate based on user-provided inputs. These levelized LRMER values are intended for analysts to use when estimating the emissions induced (or avoided) by a long-term change in end-use electricity demand. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2021 project at https://cambium.nrel.gov/. For more details on the inputs into the scenarios available in the workbooks, see the Standard Scenarios 2021 Report (https://www.nrel.gov/docs/fy22osti/80641.pdf). This data was produced as part of the Cambium project. For more details about the methodology, see the Cambium Documentation: Version 2021 (https://www.nrel.gov/docs/fy22osti/81611.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.

This data collection provides information on the characteristics of a national sample of housing units. Data include the year the structure was built, type and number of living quarters, presence of a garage, occupancy status, access, number of rooms and bedrooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, and heating and air conditioning equipment. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Similar data are provided for housing units previously occupied by respondents who had recently moved. Supplemental sections provide data on energy-related characteristics, such as the presence of storm doors, storm windows, and other types of insulation, and use of supplemental heating equipment. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, cracks or holes in walls, ceilings, and floors, breakdowns of plumbing facilities and equipment, use of exterminator service, and respondent's overall opinion of structure. Respondent's overall opinion of the quality of the neighborhood is also provided. In addition to housing characteristics, demographic data are provided on the household members, such as sex, age, race, marital status, relationship to the household head, and income. Additional data are provided on the head of the household including years of school completed, Hispanic origin, length of residence, and travel-to-work data, such as principal means of transportation and time and distance from home to work.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

According to Cognitive Market Research,the global smart electricity meter market size was valued at USD XX in 2023 and is expected to grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2029.

The smart electricity meter market will grow significantly by XX% CAGR between 2024 to 2029
Indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market.
Asia Pacific dominated the market and accounted for the highest revenue of XX% in 2023 and is projected that it will grow at a CAGR of XX% in the future.
The report includes an analysis of the regional as well as Smart electricity meter market key players, application areas, and market growth strategies.
Detailed analysis of Market Drivers, Restraints, and Opportunities.

Market Dynamics of Smart Electricity Meter

Key Drivers

Monitoring Utility systems in real-time is a driving factor for Smart meter

The acceptance of smart electricity meter is to monitor utility systems in real time is on the rise due to several benefits they provide. Real-time communication between smart electricity meters and the utility provider enables precise and current data on energy consumption. This can assist the utility company in detecting and responding to outages faster, as well as in better managing its power grid. Furthermore, smart meters give users access to more precise information about how much energy they use, which can empower them to make more intelligent decisions and possibly reduce their utility costs. Smart electricity meter real-time monitoring facilitates the integration of renewable energy sources, increases grid reliability, and helps users optimize operational efficiency. Utilities that have access to timely and accurate data are able to identify inefficient areas, take care of maintenance problems, and enhance overall system performance. They have built-in technology that assists in identifying power theft and meter tampering, two issues that utility companies are very concerned about. Overall, the power grid's cost-effectiveness, dependability, and efficiency have all improved with the use of smart meters for real-time utility system monitoring. enhancing its future suitability.

Technological Upgradations in smart electricity meter to boost the market growth

Utilities can now gather real-time data on energy consumption more efficiently thanks to the development of Advanced Metering Infrastructure, which combines smart meters with data management and communication networks. This results in improved outage management, increased operational efficiency, and more precise billing. Zingbee, Wi-Fi, and cellular networks are examples of communication protocol innovations that have made it possible for smart meters to securely and reliably communicate with utility backend systems. By enabling two-way communication, these protocols improve grid management capabilities by enabling utilities to send commands to meters and receive data instantly. For Instance, in march 2024, Utildata and Nvidia announced the start of cooperation on their partner network module. The first manufacturer of meters to incorporate technology that combines Nvidia's chip and Jetson's platform with Utildata distributed artificial intelligence software is Aclara Technologies, which was acquired by electronics giant Hubbell Inc. in 2017 (source https://www.latitudemedia.com/news/aclara-joins-nvidia-and-utilidata-in-bringing-ai-to-the-smart-meter ) With this announcement, Nvidia's distributed artificial intelligence platform, powered by Utildata, enters into its first hardware partnership. The platform will eventually enable real-time visibility into web activity.

Restraints

Market expansion is hampered by the absence of a standard smart electricity meter

The smart electricity meter is widespread in areas, but then also security and monetary concerns among end users regarding smart electricity meters are expected to act as a barrier to the growth of the industry. The ambiguity and lack of knowledge regarding smart power meters is causing end customers to be reluctant to adopt this technology. Additionally, regulatory agencies and governments are asked to cover the early expenses of research and development for smart power meters due to their novelty. This means that administrative authorities in the relevant nations must bear this additional cost. For Instan...

The NYSERDA-funded Integrated Energy Data Resource (IEDR) provides a single statewide platform to securely collect, integrate, analyze, and make accessible a large and diverse set of energy-related information from New York's electric, gas, and steam utilities and other sources. Useful access to useful energy data provided by the IEDR enables analyses that informs investment decisions, identifies operational inefficiencies, monitors the effectiveness of policy objectives, promotes innovation, and encourages new business models. The IEDR includes analytic tools to enable energy stakeholders to design and run useful queries and calculations that can operate across all data types in the IEDR. Those tools' number and functionality should increase over time to align with, and support the use cases, that become operational as part of the IEDR. Additionally, relational information that describes the relationships among the various data elements in the IEDR materially affects the depth potential of users' ability to find, analyze, and generate useful information. User access to the IEDR data and analytic tools will be governed by the access controls that reflect and align with each type of user's legitimate needs while preventing unwarranted access to information that does not serve those legitimate needs. Public, utility-managed, and commercial datasets processed by the platform and made available or planned to be made available to approved users in various forms include: • Feeder and sub-feeder hosting capacity • Installed and queued DER projects • Utility Rates and Tariffs • Customer billing and usage • Aggregated building usage • Disadvantaged Community Characteristics • Land, Parcel, and Terrain attributes The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

The New York Power Authority provides low-cost power to help support jobs statewide while reducing public-sector costs. The Authority’s customer base includes large and small businesses, not-for-profit organizations, community-owned electric systems and rural electric cooperatives and government entities. This data includes the electric supply rates that the Authority offers to its Business Customers under different power programs.

Monthly electric consumption data in kWh and customer counts within the City of Mesa's electric service territory broken down by Rate Classification (i.e. Residential, Commercial, Other Public Authority, Interdepartmental). Consumption totals are for the month in which they are billed to the customer (“Billing Month”). Due to the constant reading of utility meters throughout the month, much of the consumption occurs in the month prior to the Billing Month. Data reporting may have a 14 day lag.

Italy Electricity decreased 10.78 EUR/MWh or 7.83% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for Italy Electricity Price.

France Electricity decreased 6.42 EUR/MWh or 9.19% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for France Electricity Price.

The goal of Alaska Energy Authority's (AEA) Power Cost Equalization program is to provide economic assistance to customers in rural areas of Alaska where the kilowatt-hour charge for electricity can be three to five times higher than the charge in more urban areas of the state. Approximately 30% of all kWh’s sold by the participating utilities are eligible for PCE. PCE fundamentally improves Alaska’s standard of living by helping small rural areas maintain the availability of communications and the operation of basic infrastructure and systems, including water and sewer, incinerators, heat and light. PCE is a core element underlying the financial viability of centralized power generation in rural communities. The Legislature established different functions for AEA and the Regulatory Commission of Alaska (RCA) under Alaska Statutes 42.45.100-170, which govern PCE program responsibilities.AEA determines eligibility of community facilities and residential customers and authorizes payment to the electric utility. Commercial customers are not eligible to receive PCE credit. Participating utilities are required to reduce each eligible customer’s bill by the amount that the State pays for PCE.RCA determines if a utility is eligible to participate in the program and calculates the amount of PCE per kWh payable to the utility. More information about the RCA may be found at www.state.ak.us/rca .Power Cost Equalization Program GuidePCE Program Statutes AS 42.45.110PCE Eligibility and Certification Determination Request FormFor more information and for questions about this data, see: AEA Power Cost Equalization.Source data: PCE Statistical Reports By Utility FY2015

Energy price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations.
This data set includes energy price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.