Cost of food in the United States increased 3.20 percent in August 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.

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

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.

United States CS: Expected Gasoline Prices: Next 5 Yrs: Increase data was reported at 69.000 % in May 2018. This records an increase from the previous number of 61.000 % for Apr 2018. United States CS: Expected Gasoline Prices: Next 5 Yrs: Increase data is updated monthly, averaging 71.000 % from Apr 1983 (Median) to May 2018, with 311 observations. The data reached an all-time high of 86.000 % in Feb 2011 and a record low of 48.000 % in May 2003. United States CS: Expected Gasoline Prices: Next 5 Yrs: Increase data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H034: Consumer Sentiment Index: Vehicle Buying Conditions. The question was: Do you think that the price of gasoline will go up during the next five years, will gasoline prices go down, or will they stay about the same as they are now?About how many cents per gallon do you think gasoline prices will (increase/decrease) during the next five years compared to now?

Core consumer prices in the United States increased 3.10 percent in August of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States CS: Expected Gasoline Prices: Next 5 Yrs: Remain the Same data was reported at 24.000 % in May 2018. This records a decrease from the previous number of 34.000 % for Apr 2018. United States CS: Expected Gasoline Prices: Next 5 Yrs: Remain the Same data is updated monthly, averaging 22.000 % from Apr 1983 (Median) to May 2018, with 311 observations. The data reached an all-time high of 37.000 % in Mar 2017 and a record low of 9.000 % in May 2001. United States CS: Expected Gasoline Prices: Next 5 Yrs: Remain the Same data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H034: Consumer Sentiment Index: Vehicle Buying Conditions. The question was: Do you think that the price of gasoline will go up during the next five years, will gasoline prices go down, or will they stay about the same as they are now?About how many cents per gallon do you think gasoline prices will (increase/decrease) during the next five years compared to now?

Context

The US Bureau of Labor Statistics monitors and collects day-to-day information about the market price of raw inputs and finished goods, and publishes regularized statistical assays of this data. The Consumer Price Index and the Producer Price Index are its two most famous products. The former tracks the aggregate dollar price of consumer goods in the United States (things like onions, shovels, and smartphones); the latter (this dataset) tracks the cost of raw inputs to the industries producing those goods (things like raw steel, bulk leather, and processed chemicals).

The US federal government uses this dataset to track inflation. While in the short term the raw dollar value of producer inputs may be volatile, in the long term it will always go up due to inflation --- the slowly decreasing buying power of the US dollar.

Content

This dataset consists of a packet of files, each one tracking regularized cost of inputs for certain industries. The data is tracked-month to month with an index out of 100.

Acknowledgements

This data is published online by the US Bureau of Labor Statistics.

Inspiration

• How does the Producer Price Index compare against the Consumer Price Index?
• What have the largest spikes in input costs been, historically? Can you determine why they occurred?
• What is the overall price index trend amongst US producers?

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This dataset displays the Motor Gasoline Prices and Expenditures for all 50 US States plus the District of Columbia. This data is available for the Year 2005, and includes information on Prices in nominal dollars per million Btu. Expenditures in Million Nominal dollars, and expenditures per person in nominal dollars.

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This dataset displays the energy prices and expenditures for each of the 50 United States, plus the District of Columbia. Included in the dataset are figures on the prices on a scale with nominal dollars per million Btu. Expenditures in millions of nominal dollars. Expenditures per person in nominal dollars. Hawaii pays the highest in prices, with Texas paying the most in expenditures.

United States CS: Expected Gasoline Prices: Next 5 Yrs: Decrease data was reported at 3.000 % in Sep 2018. This records a decrease from the previous number of 7.000 % for Aug 2018. United States CS: Expected Gasoline Prices: Next 5 Yrs: Decrease data is updated monthly, averaging 5.000 % from Apr 1983 (Median) to Sep 2018, with 315 observations. The data reached an all-time high of 28.000 % in Jul 2000 and a record low of 0.000 % in Jun 1997. United States CS: Expected Gasoline Prices: Next 5 Yrs: Decrease data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H037: Consumer Sentiment Index: Vehicle Buying Conditions. The question was: Do you think that the price of gasoline will go up during the next five years, will gasoline prices go down, or will they stay about the same as they are now?About how many cents per gallon do you think gasoline prices will (increase/decrease) during the next five years compared to now?

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Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

Crude Oil rose to 65.19 USD/Bbl on September 26, 2025, up 0.32% from the previous day. Over the past month, Crude Oil's price has risen 1.62%, but it is still 4.39% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on September of 2025.

Second-Hand Fashion Dataset

Update Sep. 19th, 2024

• Some problematic and duplicate images have been removed from version 2.- All "gold dataset" data from station1 and station3 has been moved to a single test100 folder.- JSON errors have been fixed - all JSON files should be parsed correctly now.

The new dataset has 31,638 items (+ about 100 items in test100 folder) instead of the 31,997 items in version 2.

Overview

The dataset originates from projects focused on the sorting of used clothes within a sorting facility. The primary objective is to classify each garment into one of several categories to determine its ultimate destination: reuse, reuse outside Sweden (export), recycling, repair, remake, or thermal waste.

The dataset has 31,638 clothing items, a massive update from the 3,000 items in version 1. The dataset collection started under the Vinnova funded project "AI for resource-efficient circular fashion" in Spring, 2022 and involves collaboration among three institutions: RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB. The dataset has received further support through the EU project, CISUTAC (cisutac.eu).

Project page

• Webpage: second-hand-fashion- Contact: farrukh.nauman@ri.se

Dataset Details

• The dataset contains 31,638 clothing items, each with a unique item ID in a datetime format. The items are divided into three stations: station1, station2, and station3. The station1 and station2 folders contain images and annotations from Wargön Innovation AB, while the station3 folder contains data from Myrorna AB. Each clothing item has three images and a JSON file containing annotations.

• Three images are provided for each clothing item: 1. Front view. 2. Back view. 3. Brand label close-up. About 4000-5000 brand images are missing because of privacy concerns: people's hands, faces, etc. Some clothing items did not have a brand label to begin with.

• Image resolutions are primarily in two sizes: 1280x720 and 1920x1080. The background of the images is a table that used a measuring tape prior to January 2023, but later images have a square grid pattern with each square measuring 10x10 cm.

• Each JSON file contains a list of annotations, some of which require nuanced interpretation (see labels.py for the options): - usage: Arguably the most critical label, usage indicates the garment's intended pathway. Options include 'Reuse,' 'Repair,' 'Remake,' 'Recycle,' 'Export' (reuse outside Sweden), and 'Energy recovery' (thermal waste). About 99% of the garments fall into the 'Reuse,' 'Export,' or 'Recycle' categories. - trend: This field refers to the general style of the garment, not a time-dependent trend as in some other datasets (e.g., Visuelle 2.0). It might be more accurately labeled as 'style.' - material: Material annotations are mostly based on the readings from a Near Infrared (NIR) scanner and in some cases from the garment's brand label. - Damage-related attributes include: - condition (1-5 scale, 5 being the best) - pilling (1-5 scale, 5 meaning no pilling) - stains, holes, smell (each with options 'None,' 'Minor,' 'Major'). Note: 'holes' and 'smell' were introduced after November 17th, 2022, and stains previously only had 'Yes'/'No' options. For station1 and station2, we introduced additional damage location labels to assist in damage detection:

    "damageimage": "back",
    "damageloc": "bottom left",
    "damage": "stain ",
    "damage2image": "front",
    "damage2loc": "None",
    "damage2": "",
    "damage3image": "back",
    "damage3loc": "bottom right",
    "damage3": "stain"
  
  Taken from `labels_2024_04_05_08_47_35.json` file. Additionally, we annotated a few hundred images with bounding box annotations that we aim to release at a later date.  - `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage. 
  

• Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., pilling).

• Gold dataset: 100 garments were annotated multiple times by different annotators for annotator agreement comparisons. These 100 garments are placed inside a separate folder test100.

• The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB.

• Some attributes, such as price, should be considered with caution. Many distinct pricing models exist in the second-hand industry: - Price by weight - Price by brand and demand (similar to first-hand fashion) - Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK) Wargön Innovation AB does not set the prices in practice and their prices are suggestive only (station1 and station2). Myrorna AB (station3), in contrast, does resale and sets the prices.

Comments

• We received feedback on our version 1 that some images were too blurry or had poor lighting. The image quality has slightly improved, but largely remains similar to release 1. - Some users did not prefer a tar.gz format that we uploaded in version 1 of the dataset. We have now switched to .zip for convenience.- Extra care was taken not to leak personal information. This is why you will not see any entries for annotator attribute in the JSON files in station1/sep2023 since people used their real names. Since then, we used internally assigned IDs. - Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons. - Please inform us immediately if you find any personal information revelations in the dataset: - Farrukh Nauman (RISE AB): farrukh.nauman@ri.se, - Susanne Eriksson (Wargön Innovation AB): susanne.eriksson@wargoninnovation.se, - Gabriella Engstrom (Wargön Innovation AB): gabriella.engstrom@wargoninnovation.se.

We went through 100k images four times to ensure no personal information is leaked, but we are human and can make mistakes.

Partners

The data collection for this dataset has been carried out in collaboration with the following partners:

1. RISE Research Institutes of Sweden AB: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.

2. Wargön Innovation AB: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.

3. Myrorna AB: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused.

License

CC-BY 4.0. Please refer to the LICENSE file for more details.

Acknowledgments

This dataset was made possible through the collaborative efforts of RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB, with funding from Vinnova and support from the EU project CISUTAC. We extend our gratitude to all the expert second-hand sorters and annotators who contributed their expertise to this project.

This dataset provides financial accouting data for US company stocks along with per-share earnings & price metrics, liquidity ratios, management efficiency measures, margins and stock price data.

Companies are predominantly from the S&P 500 index, with a small number of additions made. The accounting data is on Fiscal Year basis, but most companies have had their stock price sampled up to 3 times in any given year. The time period covers the 10 most recent fiscal years, either 2013-2023 or 2014-2024 depending on when a company's fiscal year ends.

Data was collected from multiple sources, with some fields calculated from various other data points collected. There is no pre-defined target variable, and no directed goal to achieve using this dataset. Please explore and take your own unique approach in terms of how this data can be used, supplementing it with additional data if necessary.

This dataset was created as part of a college research project focused on stock valuation using machine learning, and I am sharing this here so that others may also benefit. I do not intend to maintain this dataset over time. Regardless I do believe that this will be a very valuable and useful dataset for anyone looking to carry out research or just looking to learn more about the area of stock investing using machine learning or other forms of analytics.