Historical option sample data at 2022-08-24, dataset files in CSV format.

Smart Insider’s Global Share Buyback Database offers invaluable insights to investors on public equity market data. We provide detailed, up-to-date share buyback data covering over 55,000 companies globally including Africa, that’s every company that reports Buybacks through regulatory processes.

Our Share buyback data includes detailed information on all major buyback transactions including source announcements and derived analysis fields. Our platform adds a visual representation of the data, allowing investors to quickly identify patterns and make decisions based on their findings.

Get detailed share buyback insights with Smart Insider and stay ahead of the curve with accurate, historical buyback insight that helps you make better investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as CSV, XML or XLSX via SFTP, API or Snowflake.

Sample dataset for Desktop Service has been provided with limited fields. Upon request, we can provide a detailed Quant sample.

Tags: Equity Market Data, Stock Market Data, Corporate Actions Data, Corporate Buyback Data, Company Financial Data, Insider Trading Data, Africa

Separate tables for the last ten years of Community Data Snapshot releases (2015-2025) are provided for three geographic levels:The seven counties in the CMAP region (with regional total)The 284 municipalities in the CMAP regionThe 77 Chicago community areas (CCAs) There is limited geographic availability (particularly at the CCA level) for some variables. Additional information on availability and data sources are found in the CDS Data Dictionary. Looking to match human-friendly labels to field names? Use the CDS Data Dictionary Crosswalk.When using multiple releases of the snapshots, please don’t compare overlapping ACS 5-Year Estimates. The Census Bureau provides specific guidance for when it is appropriate to compare ACS data across time. CMAP uses the most recently available 5-Year Estimates, which are usually available on a two year lag:CDS yearACS 5-Year Estimates data vintageCompare to previous CDS year20252019-20232020, 201520242018-2022201920232017-2021201820222016-2020201720212015-2019201620202014-2018201520192013-2017 20182012-2016 20172011-2015 20162010-2014 20152009-2013 NOTE: Much of the data is from five-year American Community Survey, which is a sample-based data product. This means users must exercise caution when interpreting data from low-population municipalities, as the margins of error are often large compared to the estimate. Not sure which municipality or Chicago community area you want? Explore a community's data in the interactive dashboard.Are you looking for the PDF versions? Find and download the print-friendly Community Data Snapshots from the agency website.

Link Function: information

This digital dataset contains historical geochemical and other information for 200 samples of produced water from 182 sites in 25 oil fields in Los Angeles and Orange Counties, southern California. Produced water is a term used in the oil industry to describe water that is produced as a byproduct along with the oil and gas. The locations from which these historical samples have been collected include 152 wells. Well depth and (or) perforation depths are available for 114 of these wells. Sample depths are available for two additional wells in lieu of well or perforation depths. Additional sample sites include four storage tanks, and two unidentifiable sample sources. One of the storage tank samples (Dataset ID 57) is associated with a single identifiable well. Historical samples from other storage tanks and unidentifiable sample sources may also represent pre- or post-treated composite samples of produced water from single or multiple wells. Historical sample descriptions provide further insight about the site type associated with some of the samples. Twenty-four sites, including 21 wells, are classified as "injectate" based on the sample description combined with the designated well use at the time of sample collection (WD, water disposal or WF, water flood). Historical samples associated with these sites may represent water that originated from sources other than the wells from which they were collected. For example, samples collected from two wells (Dataset IDs 86 and 98) include as part of their description “blended and treated produced water from across the field”. Historical samples described as formation water (45 samples), including 38 wells with a well type designation of OG (oil/gas), are probably produced water, representing a mixture of formation water and water injected for enhanced recovery. A possible exception may be samples collected from OG wells prior to the onset of production. Historical samples from four wells, including three with a sample description of "formation water", were from wells identified as water source wells which access groundwater for use in the production of oil. The numerical water chemistry data were compiled by the U.S. Geological Survey (USGS) from scanned laboratory analysis reports available from the California Geologic Energy Management Division (CalGEM). Sample site characteristics, such as well construction details, were attributed using a combination of information provided with the scanned laboratory analysis reports and well history files from CalGEM Well Finder. The compiled data are divided into two separate data files described as follows: 1) a summary data file identifying each site by name, the site location, basic construction information, and American petroleum Institute (API) number (for wells), the number of chemistry samples, period of record, sample description, and the geologic formation associated with the origin of the sampled water, or intended destination (formation into which water was to intended to be injected for samples labeled as injectate) of the sample; and 2) a data file of geochemistry analyses for selected water-quality indicators, major and minor ions, nutrients, and trace elements, parameter code and (or) method, reporting level, reporting level type, and supplemental notes. A data dictionary was created to describe the geochemistry data file and is provided with this data release.

Smart Insider’s Global Share Buyback Database offers invaluable insights to investors on corporate actions data. We provide detailed, up-to-date share buyback data covering over 55,000 companies globally and over 20K+ from Asia, that’s every company that reports Buybacks through regulatory processes.

Our Share buyback data includes detailed information on all major buyback transactions including source announcements and derived analysis fields. Our platform adds a visual representation of the data, allowing investors to quickly identify patterns and make decisions based on their findings.

Get detailed share buyback insights with Smart Insider and stay ahead of the curve with accurate, historical buyback insight that helps you make better investment decisions.

We provide full customization of reports delivered by desktop, through feeds, or alerts. Our quant clients can receive data in a variety of formats such as CSV, XML or XLSX via SFTP, API or Snowflake.

Sample dataset for Desktop Service has been provided with limited fields. Upon request, we can provide a detailed Quant sample.

Tags: Equity Market Data, Stock Market Data, Corporate Actions Data, Corporate Buyback Data, Company Financial Data, Insider Trading Data

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.

Free historical options data, dataset files in CSV format.

CoinAPI's crypto OHLCV and trade data give you the complete picture of market activity across more than 350 exchanges worldwide. Our candlestick data covers everything from 1-second intervals for scalping to monthly timeframes for trend analysis, ensuring you have the right level of detail for your trading approach.

Each candlestick provides the essential price information traders rely on - open, high, low, and close prices - along with corresponding volume data that shows the market strength behind each move. This combination of price action and trading volume creates the foundation for effective technical analysis and trading decisions.

Getting this data is straightforward - use our WebSocket streams for real-time market monitoring when every second counts, or access historical candlesticks through our REST API when you're conducting deeper market research or backtesting strategies. We maintain comprehensive historical records, giving you the ability to analyze patterns across different market cycles.

Why work with us?

Market Coverage & Data Types: - Full Cryptocurrency Data - Real-time and historical data since 2010 (for chosen assets) - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Market indexes (VWAP, PRIMKT) - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume

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Whether you're building algorithmic trading systems, conducting research, or creating visualization tools, our real-time and historical candlesticks from exchanges worldwide provide the reliable market data you need

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➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing our APIs.

Coresignal's employee data enables you to create and improve innovative data-driven solutions and extract actionable business insights. These datasets are popular among companies from different industries, including HR and sales technology and investment.

Employee Data use cases:

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Disclaimer: This dataset is distributed by Daniel Gayo-Avello, an associate professor at the Department of Computer Science in the University of Oviedo, for the sole purpose of non-commercial research and it just includes tweet ids.

The dataset contains tweet IDs for all the published tweets (in any language) bettween March 21, 2006 and July 31, 2009 thus comprising the first whole three years of Twitter from its creation, that is, about 1.5 billion tweets (see file Twitter-historical-20060321-20090731.zip).

It covers several defining issues in Twitter, such as the invention of hashtags, retweets and trending topics, and it includes tweets related to the 2008 US Presidential Elections, the first Obama’s inauguration speech or the 2009 Iran Election protests (one of the so-called Twitter Revolutions).

Finally, it does contain tweets in many major languages (mainly English, Portuguese, Japanese, Spanish, German and French) so it should be possible–at least in theory–to analyze international events from different cultural perspectives.

The dataset was completed in November 2016 and, therefore, the tweet IDs it contains were publicly available at that moment. This means that there could be tweets public during that period that do not appear in the dataset and also that a substantial part of tweets in the dataset has been deleted (or locked) since 2016.

To make easier to understand the decay of tweet IDs in the dataset a number of representative samples (99% confidence level and 0.5 confidence interval) are provided.

In general terms, 85.5% ±0.5 of the historical tweets are available as of May 19, 2020 (see file Twitter-historical-20060321-20090731-sample.txt). However, since the amount of tweets vary greatly throughout the period of three years covered in the dataset, additional representative samples are provided for 90-day intervals (see the file 90-day-samples.zip).

In that regard, the ratio of publicly available tweets (as of May 19, 2020) is as follows:

• March 21, 2006 to June 18, 2006: 88.4% ±0.5 (from 5,512 tweets).
• June 18, 2006 to September 16, 2006: 82.7% ±0.5 (from 14,820 tweets).
• September 16, 2006 to December 15, 2006: 85.7% ±0.5 (from 107,975 tweets).
• December 15, 2006 to March 15, 2007: 88.2% ±0.5 (from 852,463 tweets).
• March 15, 2007 to June 13, 2007: 89.6% ±0.5 (from 6,341,665 tweets).
• June 13, 2007 to September 11, 2007: 88.6% ±0.5 (from 11,171,090 tweets).
• September 11, 2007 to December 10, 2007: 87.9% ±0.5 (from 15,545,532 tweets).
• December 10, 2007 to March 9, 2008: 89.0% ±0.5 (from 23,164,663 tweets).
• March 9, 2008 to June 7, 2008: 66.5% ±0.5 (from 56,416,772 tweets; see below for more details on this).
• June 7, 2008 to September 5, 2008: 78.3% ±0.5 (from 62,868,189 tweets; see below for more details on this).
• September 5, 2008 to December 4, 2008: 87.3% ±0.5 (from 89,947,498 tweets).
• December 4, 2008 to March 4, 2009: 86.9% ±0.5 (from 169,762,425 tweets).
• March 4, 2009 to June 2, 2009: 86.4% ±0.5 (from 474,581,170 tweets).
• June 2, 2009 to July 31, 2009: 85.7% ±0.5 (from 589,116,341 tweets).

The apparent drop in available tweets from March 9, 2008 to September 5, 2008 has an easy, although embarrassing, explanation.

At the moment of cleaning all the data to publish this dataset there seemed to be a gap between April 1, 2008 to July 7, 2008 (actually, the data was not missing but in a different backup). Since tweet IDs are easy to regenerate for that Twitter era (source code is provided in generate-ids.m) I simply produced all those that were created between those two dates. All those tweets actually existed but a number of them were obviously private and not crawlable. For those regenerated IDs the actual ratio of public tweets (as of May 19, 2020) is 62.3% ±0.5.

In other words, what you see in that period (April to July, 2008) is not actually a huge number of tweets having been deleted but the combination of deleted *and* non-public tweets (whose IDs should not be in the dataset for performance purposes when rehydrating the dataset).

Additionally, given that not everybody will need the whole period of time the earliest tweet ID for each date is provided in the file date-tweet-id.tsv.

For additional details regarding this dataset please see: Gayo-Avello, Daniel. "How I Stopped Worrying about the Twitter Archive at the Library of Congress and Learned to Build a Little One for Myself." arXiv preprint arXiv:1611.08144 (2016).

If you use this dataset in any way please cite that preprint (in addition to the dataset itself).

If you need to contact me you can find me as @PFCdgayo in Twitter.

The Civil Rights Data Collection (CRDC), formerly administered as the Elementary and Secondary School Civil Rights Survey, is an important part of the U.S. Department of Education's (Department) Office for Civil Rights (OCR) strategy for administering and enforcing civil rights laws in the nation’s public school districts and schools. The CRDC collects a variety of information including student access to rigorous courses, programs, resources, instructional and other school staff, and school climate factors such as student discipline and harassment and bullying. Much of the data is disaggregated by race/ethnicity, sex, disability and whether students are English Learners.Since the 2011–12 school year, OCR has collected data from all public districts and their schools in the 50 states and Washington, DC. Over time the CRDC’s collection universe has grown to include long-term secure justice facilities, charter schools, alternative schools, and special education schools that focus primarily on serving students with disabilities. OCR added the Commonwealth of Puerto Rico to the CRDC, beginning with the 2017-18 CRDC. From 1968 to 2010, civil rights data were collected from a sample of public districts and their schools, except for the 1976 and 2000 collections, which included data from all public schools and districts.The purpose of the CRDC Archival Download Tool (Archival Tool) is to make the Department’s civil rights data from 1968 to 1998 publicly available. The Archival Tool organizes civil rights data by year, and provides users with access to the data, survey forms, and other relevant documentation. The tool also includes documentation on key historical CRDC data changes from 1968 to 1998. Users may extract district-level civil rights data. For instructions and information on using the Archival Data Download Tool, please view this page.Important Consideration: Past collections and publicly released reports may contain some terms that readers may consider obsolete, offensive and/or inappropriate. As part of the Department’s goal to be open and transparent with the public, we are providing access to all civil rights data in its original format.Privacy notice:The Department of Education’s Disclosure Review Board determined that the CRDC files for 1968-1998 are safe for public “re-release” under the Family Educational Rights and Privacy Act (FERPA) (20 U.S.C. § 1232g; 34 CFR Part 99).Data were collected via a sample of school districts and all individual schools within these districts.Related Projects:CRDC 2000: https://www.datalumos.org/datalumos/project/218422/viewCRDC 2004: https://www.datalumos.org/datalumos/project/218423/viewCRDC 2006: https://www.datalumos.org/datalumos/project/218424/viewCRDC 2009-2010: https://www.datalumos.org/datalumos/project/218425/viewCRDC 2013-2014: https://www.datalumos.org/datalumos/project/100445/viewCRDC 2015 - 2016: https://www.datalumos.org/datalumos/project/103004/view

Download Historical E Mini S&P Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

Dataset historical price data for XAU/USD (gold vs USD) from 2004 to Feb 2025, captured across multiple timeframes including 5-minute, 15-minute, 30-minute, 1-hour, 4-hour, daily, weekly, and monthly intervals. Dataset includes Open, High, Low, Close prices, and Volume data.

This U.S. Geological Survey (USGS) Data Release provides analytical data from samples and measurements completed at the National Crude Oil Spill Fate and Natural Attenuation Research Site near Bemidji, Minnesota (Site) between 1984 and 2010. Included are inorganic and organic chemistry data from water, oil, and sediment samples, hydraulic conductivity data from well slug tests, and sediment grain-size distribution data from core samples. Most of these data sets have been described in previously published peer-reviewed reports, however the tabular data sets were not available with these publications. This data release provides the data in a tabular, database-ready format. Each result value in the data sets is coded to describe the kind of sample collected, the material that was analyzed, the method of analysis, and the publication where the value was originally published. Some sample codes are taken from the U.S. Geological Survey's National Water Information System (NWIS, https://waterdata.usgs.gov/nwis) and the remaining codes were developed specifically for Site data. Data dictionaries containing code definitions are available at a companion data release titled "Sampling site information, well construction details, and data dictionaries for data sets associated with the National Crude Oil Spill Fate and Natural Attenuation Site near Bemidji, Minnesota", available at https://doi.org/10.5066/F7736PDR. In 1979, a high-pressure pipeline carrying crude oil burst near the city of Bemidji, Minnesota and spilled approximately 1.7 million liters (10,700 barrels) of crude oil into glacial outwash deposits. Since 1983, scientists with the U.S. Geological Survey, in collaboration with scientists from academic institutions, industry, and the regulatory community have conducted extensive investigations of multiphase flow and transport, volatilization, dissolution, geochemical interactions, microbial populations, and biodegradation with the goal of providing an improved understanding of the natural processes limiting the extent of hydrocarbon contamination. Long-term field studies at Bemidji have illustrated that the fate of hydrocarbons evolves with time, and a snap-shot study of a hydrocarbon plume may not provide information that is of relevance to the long-term behavior of the plume during natural attenuation. The research at the site has been supported primarily by the U.S. Geological Survey's Toxic Substances Hydrology Program.

The International Polar Year Historical Data and Literature collection (formerly known as the Discovery and Access of Historic Literature from the IPYs (DAHLI) project) is an online data collection consisting primarily of photographs, publications, and observational data records from, and relating to, the first two International Polar Years (IPY) 1882-83 and 1932-33 and the International Geophysical Year (IGY)1957-58. Examples of data contained in observational records include, but are not limited to: air magnetic vertical intensity, air conductivity, atmospheric-electric observations, auroral log data, potential-gradient electrographic data, dust counts, and meteorological observations. Photographs within the collection include those from the Wilkes Station in Antarctica, the USGS survey of Fletcher's Ice Island, and the DTM Geophysical Laboratory Library. Publications within this collection primarily consist of government (national and international) bulletins and reports on activities during the International Polar and International Geophysical years. Other data include audio files of interviews recorded during NCAR's Oral Histories Project, and a video on Drifting Station Alpha during the IGY, published by NSIDC.

Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Mobility/Location data is gathered from location-aware mobile apps using an SDK-based implementation. All users explicitly consent to allow location data sharing using a clear opt-in process for our use cases and are given clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure only the highest quality of data is made available for analysis.

Record Count:90 Billion+ Capturing Frequency: Once per Event Delivering Frequency: Once per Day Updated: Daily

Mobility Data Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited interval (daily/weekly/monthly/quarterly).

Business Needs: Consumer Insight: Gain a comprehensive 360-degree perspective of the customer to spot behavioral changes, analyze trends and predict business outcomes. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape. Advertising: Create campaigns and customize your messaging depending on your target audience's online and offline activity. Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas.

Here's the data attributes: maid latitude longitude horizontal_accuracy timestamp id_type ipv4 ipv6 user_agent country state_hasc city_hasc postcode geohash hex8 hex9 carrier

When deciding on how to estimate future prices, due to influences that are likely to affect a product, we should consider two factors: the expected inflation and the real price change. The rate of real price change allows us to plot a trend line based on time series reflecting existing or past market price, that is, on "facts". Usually, many potential users are not going to use sophisticated forecasting techniques to estimate future prices, preferring to rely on simple approximation techniques. If acceptable time price series is available, then the simplest approach is to evidence a trend line over time that can be extended into the future. This can be done with regression analysis. In working with historical data, we could arrive at a medium-term trend estimate, which excludes the effects of inflation. Although the real price of forest products does not usually vary in an exponential way, the normal practice in investment analyses is often simplified by compounding price using a real price change rate. We can get the annual rate of real price change (r) from a linearized model that allows us to keep the statistical robustness of a linear regression model (with statistics, confidence indicators and tests), but applying the compound rate approach used in mathematics of finance. To do that, the well-known basic formula for compounding Pn=P0 (1+r)^n, where: Pn = estimated price in year n P0= price in year 0 r = annual rate of real price change (the real compound rate) n = number of years from year 0

is transformed into that of a straight line by making a change of variables (linearization).

The proposed method is easy to reproduce and seems more orthodox than apply projections made using a simple straight-line model. Even though the straight-line represents an average variation over the years, from a mathematics of finance approach we should discuss price variation in terms of the annual compound rate. In Figure 1, you can see the differences between these approaches. If we have a clear trend in past real prices and the likelihood of a real price variation, we could make future price assumptions. If you agree with this statement and believe that price trend based on historical patterns is a significative information, then you should use r value gotten from the linearized model here proposed to project the price according to the previous compounding equation, where P0 is any real price calculated through the linearized compounding model (Table I). In Catalonia, most of forest products prices have not kept up with inflation and reflect a declining trend. A few others have just barely kept up with inflation. This is means that, despite moderate growth in nominal terms, the real price of almost all Catalan forest products presents a negative trend. For example, Scots pine sawlogs -the most representative harvested species in Catalonia (the 27% of the total volume yearly logged)- have dropped by an average of almost 2% per year since 1980.