The online price changes for a selection of food and drink products from several large UK retailers. These data are experimental estimates developed to deliver timely indicators to help better understand real time economic activity and social change in the UK.

Real time pricing (RTP) is often promoted as a mechanism to improve the economic efficiency of the electricity system. However, many regulators have been hesitant to adopt RTP due to concerns about exposing customers to extreme price swings. To balance these concerns, this paper proposes a methodology for establishing price controls, based on the supply of demand-side flexibility in the system. As an illustrative example, we measure price responsiveness using an agent-based simulation model that is representative of the ERCOT market. The model is composed of a distribution feeder that has 250 customers with active agents controlling their HVAC systems in response to the historical ERCOT RTP with an artificially added high-price event. These agents are subjected to increasing electricity prices during the event, which we then use to create a supply curve for demand-side resources in our modeled scarcity event. We set potential price caps at points on the supply curve where customers’ have exhausted their flexible capacity. Using historical prices, we examine the systemic costs of these price caps, and present regulatory options for recouping them. Utilities and regulators interested in limiting consumer risk from dynamic pricing can utilize these methods to develop rate structures and encourage conservation. The attached data upload allows for the duplication or modification of the analysis performed in this study.

Producer Prices in Fiji increased 6.70 percent in March of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Fiji Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

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".

Gold rose to 3,354.76 USD/t.oz on July 11, 2025, up 0.92% from the previous day. Over the past month, Gold's price has fallen 0.92%, but it is still 39.14% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

About this dataset

Context

The dataset tabulates the Price town 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 town 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 2022, the population of Price town was 226, a 0.89% increase year-by-year from 2021. Previously, in 2021, Price town population was 224, an increase of 0.45% compared to a population of 223 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Price town decreased by 17. In this period, the peak population was 250 in the year 2007. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Price town is shown in this column.
• Year on Year Change: This column displays the change in Price town 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 town Population by Year. You can refer the same here

This table includes the average increase of rent paid for dwellings in the Netherlands. It shows a breakdown regarding the rent change in- and excluding rent harmonisation. Another breakdown is for the commercial and non-commercial rent movements of dwellings. 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: 2009

Status of the figures: All values are definite.

Frequency: Discontinued on 10 October 2011.

Between 2024 and 2025, the data center construction costs in Australia, Indonesia, and India were estimated to increase by about five percent. In comparison, Hong Kong's construction cost for data center was forecasted to grow by around 2.1 percent during this period.

This dataset includes data on China's egg market transaction prices from 2014-2021 and consists of 2 parts: (1) text data including the national egg market retail price statistics table (weekly), the national egg market wholesale price statistics table (daily), the national egg market wholesale price change information, the national and 12 provinces (autonomous regions and municipalities directly under the central government) monthly average wholesale prices and information on the rate of change; (2) picture data sets include monthly average wholesale prices and weekly retail prices and their rate of change line graphs for the national egg market from 2014-2021.

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

Key information about House Prices Growth

• Canada house prices grew 0.1% YoY in Jan 2025, following an increase of 0.1% YoY in the previous month.
• YoY growth data is updated monthly, available from Jan 1982 to Jan 2025, with an average growth rate of 1.8%.
• House price data reached an all-time high of 16.5% in Mar 1989 and a record low of -9.7% in Apr 1991.

The Potential Impacts of Climate Change on World Food Supply: Datasets from a Major Crop Modeling Study contain projected country and regional changes in grain crop yields due to global climate change. Equilibrium and transient scenarios output from General Circulation Models (GCMs) with three levels of farmer adaptations to climate change were utilized to generate crop yield estimates of wheat, rice, coarse grains (barley and maize), and protein feed (soybean) at 125 agricultural sites representing major world agricultural regions. Projected yields at the agricultural sites were aggregated to major trading regions, and fed into the Basic Linked Systems (BLS) global trade model to produce country and regional estimates of potential price increases, food shortages, and risk of hunger. These datasets are produced by the Goddard Institute for Space Studies (GISS) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

New housing price index (NHPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (201612=100).

House Price Index YoY in the United States decreased to 3 percent in April from 3.90 percent in March of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

Overview \r This report examines Australian and international experience in monitoring farmgate and retail prices for food products. It also outlines a simple methodology to monitor farm shares and farm-to-retail price spreads for food products, and investigates the potential to apply the methodology to Australian data. \r \r Key Points \r • The food retail sector in Australia is highly concentrated while there is increasing consolidation in the food processing sector. There is some concern that this could lead to farmers receiving lower prices and consumers paying higher prices than would be the case in a perfectly competitive market. \r • The paper reviews local and international research in monitoring movements in farm and retail prices for food products, outlines a simple methodology to monitor farm shares and farm-to-retail price spreads for food products, and investigates the potential to apply the methodology to Australian data. \r • The review of international research found significant variation across countries in the importance they place on food price monitoring and analysis. Research has consistently found that the more processed food products are, the lower the farm share, and that farm shares have generally been declining over time. \r • The review also found that the United States Department of Agriculture Economic Research Service (USDA ERS) is a world leader in analysing prices in food supply chains. The paper outlines a relatively simple methodology used by the USDA ERS to monitor changes in farm shares and farm-to-retail price spreads for food products. \r • While there are limitations with the USDA ERS approach, an increase in farm-to-retail price spread or a decrease in farm share of the retail price could be a useful early indicator that competition issues are emerging within a supply chain. However, additional analysis will always be required to confirm whether the cause was an increase in market power because these changes can occur for a number of reasons, including differences in productivity in different sectors or input prices increasing at a faster rate in the retail sector than in the farm sector. Unfortunately, there is generally a lack of data that will allow a breakdown in marketing costs to facilitate this analysis. \r • One option for additional research is to replicate another methodology developed by the USDA ERS, which uses input-output data to decompose costs and profits between different sectors within a supply chain and to estimate returns to primary factors, including capital and labour. This type of analysis would be more expensive than the high-level analysis described in this paper but it would also be more informative than the farm share/price spread analysis in identifying the range of factors influencing prices, and lead to a more informed debate about the various factors influencing prices, including market power. \r

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.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What happens to gold if CPI increases?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/HUBRpnUDxZ5rCnOYYCw