Cost of food in the United States increased 2.90 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Value chain disruptions are expected to trigger sudden price changes and increase in price volatility. This is data from the FAO Daily Prices pages which monitors consumer prices of 14 main food products in all countries and compiles the average price change for each product since 14 February 2020.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

This dataset is comprised of data submitted to HCAI by prescription drug manufacturers for wholesale acquisition cost (WAC) increases that exceed the statutorily-mandated WAC increase threshold of an increase of more than 16% above the WAC of the drug product on December 31 of the calendar year three years prior to the current calendar year. This threshold applies to prescription drug products with a WAC greater than $40 for a course of therapy. Required WAC increase reports are to be submitted to HCAI within a month after the end of the quarter in which the WAC increase went into effect. Please see the statute and regulations for additional information regarding reporting thresholds and report due dates.

Key data elements in this dataset include the National Drug Code (NDC) maintained by the FDA, narrative descriptions of the reasons for the increase in WAC, and the five-year history of WAC increases for the NDC. A WAC Increase Report consists of 27 data elements that have been divided into two separate Excel data sets: Prescription Drug WAC Increase and Prescription Drug WAC Increase – 5 Year History. The datasets include manufacturer WAC Increase Reports received since January 1, 2019. The Prescription Drugs WAC Increase dataset consists of the information submitted by prescription drug manufacturers that pertains to the current WAC increase of a given report, including the amount of the current increase, the WAC after increase, and the effective date of the increase. The Prescription Drugs WAC Increase – 5 Year History dataset consists of the information submitted by prescription drug manufacturers for the data elements that comprise the 5-year history of WAC increases of a given report, including the amount of each increase and their effective dates.

There are 2 types of WAC Increase datasets below: Monthly and Annual. The Monthly datasets include the data in completed reports submitted by manufacturers for calendar year 2025, as of July 8, 2025. The Annual datasets include data in completed reports submitted by manufacturers for the specified year. The datasets may include reports that do not meet the specified minimum thresholds for reporting.

The Quick Guide explaining how to link the information in each data set to form complete reports is here: https://hcai.ca.gov/wp-content/uploads/2024/03/QuickGuide_LinkingTheDatasets.pdf

The program regulations are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/CTRx-Regulations-Text.pdf

The data format and file specifications are available here: https://hcai.ca.gov/wp-content/uploads/2024/03/Format-and-File-Specifications-version-2.0-ada.pdf

DATA NOTES: Due to recent changes in Excel, it is not recommended that you save these files to .csv format. If you do, when importing back into Excel the leading zeros in the NDC number column will be dropped. If you need to save it into a different format other than .xlsx it must be .txt

DATA UPDATES: Annual datasets of reports from the preceding year are reviewed in the second half of the current year to identify if any revisions or additions have been made since the original release of the datasets. If revisions or additions have been found, an update of the datasets will be released. Datasets will be clearly marked with 'Updated' in their titles for convenient identification. Not all datasets may require an updated release. The review of previously released datasets will only be conducted once to determine if an updated release is necessary. Datasets with revisions or additions that may have been made after the one-time review can be requested. These requests should be sent via email to ctrx@hcai.ca.gov. Due to regulatory changes that went into effect April 1, 2024, reports submitted prior to April 1, 2024, will include the data field "Unit Sales Volume in US" and reports submitted on or after April 1, 2024, will instead include "Total Volume of Gross Sales in US Dollars".

Gasoline rose to 2.19 USD/Gal on July 11, 2025, up 1.65% from the previous day. Over the past month, Gasoline's price has risen 1.03%, but it is still 12.72% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on July of 2025.

It has long been suspected, given the salience of gasoline prices, that fluctuations in gasoline prices shift households' 1-year inflation expectations. Assessing this view empirically requires the use of dynamic structural models to quantify the cumulative effect of gasoline price shocks on household inflation expectations at each point in time. We find that, on average, gasoline price shocks account for 42% of the variation in these expectations. The cumulative increase in household inflation expectations from early 2009 to early 2013, in particular, is almost entirely explained by unexpectedly rising gasoline prices. However, there is no support for the view that the improved fit of the Phillips curve augmented by household inflation expectations during 2009 2013 is mainly explained by rising gasoline prices.

Abstract

Food 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 food 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.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

Consumer Price Index CPI in the United States increased to 321.47 points in May from 320.80 points in April of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monthly average retail prices for food, household supplies, personal care items, cigarettes and gasoline. Prices are presented for the current month and previous four months. Prices are in Canadian current dollars.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Price. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Price. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Price, householders within the 25 to 44 years age group have the highest median household income at $59,052, followed by those in the 45 to 64 years age group with an income of $51,968. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $30,972. Notably, householders within the under 25 years age group, had the lowest median household income at $27,850.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Price median household income by age. You can refer the same here

August 2022 update

1. The updated database has monthly prices from over 400 supermarkets across six chains (all Shufersal stores, plus ~20 per chain for the other chains), from March 2022-August 2022. Before march there is a steady increase in the number of stores.
2. The current database has combined all the supermarket chains, which required adding two columns - 'ChainName' is the supermarket chain, and 'store_code' is a concatenation of the store_id and the beginning of the chain name, to solve the duplicate store_ids.
3. A new table was constructed to try and identify the manufacturers of each product, given the significant discrepancies in the retailers' reporting. This table ("manufacturer_finder") has identified the likely supplier of around half of the products in the database, and can be used to track specific suppliers (producers/importers).

This dataset was created to analyze changes in prices in the Israeli grocery retail market. It was created based on the files retailers are legally required to upload, available here: https://www.gov.il/he/departments/legalInfo/cpfta_prices_regulations

The data is not complete and downloads increased gradually. Beginning in May 2020 there are sporadic files for three specific Shufersal stores. Starting in November 2021 Downloads increased, ~20-50 stores downloaded at various times from Shufersal, and ~5-10 stores downloaded from a few other retailers.

Different table for each retailer. The table "snifim" specifies the names for stores for Shufersal (in the main table you can find store_id which can be joined to the names).

Description of columns in the Prices tables: Filename - original file name (without the xml extension) store_id - ID of the store upload_date - date of file download. Upload dates before 2020 - unclear what they are, probably of stores which shut down.
PriceUpdateDate - Last date of price change of the item. ItemCode - a unique ID of the item. ItemName - name. ManufacturerName - manufacturer. These data are messy. ManufactureCountry - country of production. ManufacturerItemDescription - similar to ItemName UnitQty - unit of measure Quantity - quantity. UnitOfMeasure - also unit of measure ItemPrice - price (NIS) UnitOfMeasurePrice - price divided by quantity AllowDiscount - boolean/dummy variable.

Supplementary data can be found here: https://docs.google.com/spreadsheets/d/1LYyCt3BTJ-QInja-4iN1vqZ91xV6TAwhywgJxecSOkM/edit?usp=sharing Including: - Analysis of suppliers - different labels associated with each supplier - A table linking Shufersal stores with their store_id - A table with details on how many price files (stores) were downloaded each date.

What are we looking for? - Price collusion - producers raising prices at the same time. - Which producers saw the greatest price increase? - Which is the most expensive store? - Which products are most promoted? You can go to the source and find "promo" tables. - Can you create a user-friendly tool to analyze these data for non-data scientists?

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.

This table includes the average increase of rent paid for dwellings by region. There is a breakdown regarding the rent change in- and excluding rent harmonisation. There is also a division by province and the four large cities Amsterdam, The Hague, Rotterdam and Utrecht. The rent change is given on an annual basis and is significant input for the housing price movements in the consumer price index. Data available from: 1999 Status of the figures: All values are definite. Frequency: Discontinued on 10 October 2011.

Abstract

Food 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 food 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.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Akkar, Mount Lebanon, Baalbek-El Hermel, North, Beirut, Bekaa, El Nabatieh, South, Market Average

This dataset contains bike share data and the cost of rental bikes between 2021 and 2022 in the Capital bike share system with the corresponding weather and seasonal information.

About this dataset

Context

The dataset tabulates the Price population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Price across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Price was 8,261, a 0.12% increase year-by-year from 2022. Previously, in 2022, Price population was 8,251, an increase of 0.87% compared to a population of 8,180 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Price decreased by 244. In this period, the peak population was 8,716 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Price is shown in this column.
• Year on Year Change: This column displays the change in Price population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Price Population by Year. You can refer the same here

Flight prices can vary dramatically depending on a range of factors. In fact, prices can change up to 100 times a day for the same route. ☎️+1 (855) 217-1878 While it might seem like the more you search, the higher the prices climb, this perception isn't always due to actual price changes. ☎️+1 (855) 217-1878 Instead, it's often driven by how airline algorithms work, using cookies and user behavior to personalize pricing or trigger urgency.

Airlines use dynamic pricing algorithms that take into account your search history, location, time of day, and even the device you're using. ☎️+1 (855) 217-1878 These algorithms are designed to maximize revenue, which can lead to the illusion that repeatedly searching raises the price. ☎️+1 (855) 217-1878 In some cases, this may be true if the site uses your activity to suggest urgency or simulate demand. But it's not always a hard rule.

For example, when you search multiple times for a flight to New York from Los Angeles within a short period, you may notice the fare jump slightly. ☎️+1 (855) 217-1878 This doesn’t necessarily mean the system is punishing you; rather, it may reflect changing inventory, competitor pricing, or new bookings. ☎️+1 (855) 217-1878 Airlines operate with a tiered pricing model, so as cheaper seats sell out, more expensive ones take their place, which also explains rising fares.

Browser cookies may play a small role. Some travel booking websites use cookies to track your behavior. ☎️+1 (855) 217-1878 While many deny price manipulation, clearing your cookies or browsing in incognito mode can sometimes help you see different fares. ☎️+1 (855) 217-1878 However, experts argue that inventory and timing are far bigger factors than user behavior.

Another element is the time and day you search. Studies show that flight prices are often lower midweek and higher on weekends. ☎️+1 (855) 217-1878 Searching Tuesday or Wednesday morning may yield better deals than browsing on a Saturday evening. ☎️+1 (855) 217-1878 Timing plays a big role not only in pricing but also in seat availability and routing options.

Using price tracking tools like Google Flights, Skyscanner, or Hopper can help monitor price trends over time. ☎️+1 (855) 217-1878 These tools analyze historical data and notify users when it's the best time to book based on trends. ☎️+1 (855) 217-1878 This strategy helps you avoid impulsive purchases based on artificially inflated fares or perceived scarcity.

Airlines also increase fares based on seasonal demand. For example, flights during the December holiday season or summer breaks tend to rise steeply. ☎️+1 (855) 217-1878 If your repeated searches coincide with high-demand periods, you’re more likely to see fare increases regardless of your search behavior. ☎️+1 (855) 217-1878 This is due to genuine supply and demand dynamics, not necessarily algorithmic manipulation.

In reality, flight prices do change frequently—but not always because of your search behavior. ☎️+1 (855) 217-1878 They change due to availability, demand, competitor actions, and many external economic factors. ☎️+1 (855) 217-1878 Airlines aim to sell each seat at the highest price someone is willing to pay, using powerful tools to make that happen.

In short, it’s wise to compare prices across multiple platforms, use incognito mode, and track fares before booking. ☎️+1 (855) 217-1878 By understanding the logic behind price fluctuations, you can better time your purchase and avoid overpaying. ☎️+1 (855) 217-1878 Strategic planning is your best ally when navigating airfare volatility.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.