This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate system from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

NOTICE: The standard set of attributes changed to the MN Parcel Data Transfer Standard on 1/1/2019.
https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

See section 5 of the metadata for an attribute summary.

Detailed information about the attributes can be found in the Metro Regional Parcel Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = https://gis-hennepin.hub.arcgis.com/pages/open-data
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

In 2024, parcel shipping volume in the United States (U.S.) reached **** billion parcels. This represents a 3.4 percent increase compared to 2022.

ATTOM provides the most extensive US parcel boundary data for more than 158 million properties nationwide. This is infused with ATTOM's proprietary database to provide a comprehensive look at the boundaries and specific attributes of such properties.

What is Land Parcel Data?

Land parcel data refers to a collection of spatially referenced information about individual land parcels or lots within a specified area. It includes attributes such as parcel ID, owner information, legal descriptions, acreage, zoning classifications, tax assessments, and geographic coordinates. This data is typically sourced from government agencies, cadastral surveys, and private entities, then compiled and organized into a structured dataset suitable for analysis and visualization.

Land Parcel Data Details:

• 157 Million nationwide parcel records and geometries
• Approximately 100 attributes (standard schema)
• Tax assessment fields
• Universal parcel ID
• Monthly rolling updates
• Standardized land use codes
• USPS validated address data with residential property & vacancy indicators
• Building counts & footprint square footage attributes
• Right-of-way (ROW) parcel indicator
• Placekey - Location identifier
• Flood zones and school districts
• Homesteads exemption
  • Crop data layer fields
  • Public access status

As Esri’s commercial partner for parcel data, Regrid invites you to enjoy this free tile layer of parcel boundaries covering 100% of the United States. Complete parcel attributes are also available from an integrated Data Store."I think it’s fantastic that this layer exists. It's really helpful for my staff to see parcel boundaries in a quick and accessible layer."- Kate Berg, Geographic Information Systems (GIS) Manager | Department of Environment, Great Lakes, and EnergyVisit the Regrid Data Store for the ArcGIS User CommunityHassle-Free Parcel Data for Esri UsersWhen you click a parcel in the tile layer, you will see its address, size, and parcel ID number, along with a convenient link to purchase additional parcel attributes in The Regrid Data Store for the ArcGIS User Community. Once in the Data Store, you can purchase and download parcel files with attributes by the county and state for use in ArcGIS, as well as our add-on datasets like standardized zoning, matched building footprints, and matched secondary addresses.See regrid.com/esri for all of Regrid’s parcel products for the Esri ecosystem, including Feature Service delivery for ongoing parcel updates at scale.Key Features of Regrid's Parcel DataSourced & Standardized: Data combines authoritative public sources & third-party enrichments, aggregated, standardized, and matched by the Regrid team.158+ Million Parcel Records: Covering all 3,200+ US counties and territories.143+ Standardized Data Fields: Including geometry, ownership, buildings, secondary addresses, land use, and zoning.Universal Parcel ID & Placekey Location Identifier: Ensuring precise identification and integration.Detailed Attributes: Tax assessments, building counts, square footage, stacked parcels (condos), right-of-way, vacancy indicators and USPS deliverability. Comprehensive Coverage: 100% land parcel coverage across the US.Parcel Data Resources & DocumentationRegrid Data Dictionary / Parcel Data SchemaRegrid Coverage ReportParcel Data FAQsThank you to all the GIS professionals, state, county and federal officials, assessors, recorders, and public officials across the country who maintain the nation's parcel data and infrastructure.

In 2023, some *** billion parcels were received and sent by mail in the United States (U.S.). This represents a decrease of over eight percent compared with the previous year when around *** billion parcels were sent and received in the country.

The United States International Courier, Express and Parcel Market Report is Segmented by End User Industry (E-Commerce, Financial Services (BFSI), Healthcare, Manufacturing, Primary Industry, and More), by Speed of Delivery (Express and Non- Express), by Shipment Weight (Heavy Weight Shipments and More), and by Model (Business-To-Business (B2B), and More). The Market Forecasts are Provided in Terms of Value (USD).

An area depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction. The same individual or organization may currently own many parcels that may or may not have been acquired through the same legal transaction. Therefore, they are captured as separate entities rather than merged together. This is in contrast to Basic Ownership, in which the surface ownership parcels having the same owner are merged together. Basic Ownership provides the general user with the Forest Service versus non-Forest Service view of land ownership within National Forest boundaries. Surface Ownership provides the land status user with a current snapshot of ownership within National Forest boundaries. Metadata

This dataset is a compilation of county parcel data from Minnesota counties that have opted-in for their parcel data to be included in this dataset.

It includes the following 59 counties that have opted-in as of the publication date of this dataset: Aitkin County, Anoka County, Becker County, Benton County, Big Stone County, Carlton County, Carver County, Cass County, Chippewa County, Chisago County, Clay County, Clearwater County, Cook County, Crow Wing County, Dakota County, Douglas County, Fillmore County, Grant County, Hennepin County, Houston County, Isanti County, Itasca County, Jackson County, Koochiching County, Lac qui Parle County, Lake County, Lake of the Woods County, Lyon County, Marshall County, McLeod County, Mille Lacs County, Morrison County, Mower County, Murray County, Norman County, Olmsted County, Otter Tail County, Pennington County, Pipestone County, Polk County, Pope County, Ramsey County, Red Lake County, Renville County, Rice County, Scott County, Sherburne County, St. Louis County, Stearns County, Steele County, Stevens County, Traverse County, Wabasha County, Waseca County, Washington County, Wilkin County, Winona County, Wright County, and Yellow Medicine County.

If you represent a county not included in this dataset and would like to opt-in, please contact Heather Albrecht (Heather.Albrecht@hennepin.us), co-chair of the Minnesota Geospatial Advisory Council (GAC)’s Parcels and Land Records Committee's Open Data Subcommittee. County parcel data does not need to be in the GAC parcel data standard to be included. MnGeo will map the county fields to the GAC standard.

County parcel data records have been assembled into a single dataset with a common coordinate system (UTM Zone 15) and common attribute schema. The county parcel data attributes have been mapped to the GAC parcel data standard for Minnesota: https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

This compiled parcel dataset was created using Python code developed by Minnesota state agency GIS professionals, and represents a best effort to map individual county source file attributes into the common attribute schema of the GAC parcel data standard. The attributes from counties are mapped to the most appropriate destination column. In some cases, the county source files included attributes that were not mapped to the GAC standard. Additionally, some county attribute fields were parsed and mapped to multiple GAC standard fields, such as a single line address. Each quarter, MnGeo provides a text file to counties that shows how county fields are mapped to the GAC standard. Additionally, this text file shows the fields that are not mapped to the standard and those that are parsed. If a county shares changes to how their data should be mapped, MnGeo updates the compilation. If you represent a county and would like to update how MnGeo is mapping your county attribute fields to this compiled dataset, please contact us.

This dataset is a snapshot of parcel data, and the source date of the county data may vary. Users should consult County websites to see the most up-to-date and complete parcel data.

There have been recent changes in date/time fields, and their processing, introduced by our software vendor. In some cases, this has resulted in date fields being empty. We are aware of the issue and are working to correct it for future parcel data releases.

The State of Minnesota makes no representation or warranties, express or implied, with respect to the use or reuse of data provided herewith, regardless of its format or the means of its transmission. THE DATA IS PROVIDED “AS IS” WITH NO GUARANTEE OR REPRESENTATION ABOUT THE ACCURACY, CURRENCY, SUITABILITY, PERFORMANCE, MECHANTABILITY, RELIABILITY OR FITINESS OF THIS DATA FOR ANY PARTICULAR PURPOSE. This dataset is NOT suitable for accurate boundary determination. Contact a licensed land surveyor if you have questions about boundary determinations.

DOWNLOAD NOTES: This dataset is only provided in Esri File Geodatabase and OGC GeoPackage formats. A shapefile is not available because the size of the dataset exceeds the limit for that format. The distribution version of the fgdb is compressed to help reduce the data footprint. QGIS users should consider using the Geopackage format for better results.

Geospatial data about United States, Virgin Islands Parcels. Export to CAD, GIS, PDF, CSV and access via API.

An area depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction.

An area depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction. The same individual or organization may currently own many parcels that may or may not have been acquired through the same legal transaction. Therefore, they are captured as separate entities rather than merged together. This is in contrast to Basic Ownership, in which the surface ownership parcels having the same owner are merged together. Basic Ownership provides the general user with the Forest Service versus non-Forest Service view of land ownership within National Forest boundaries. Surface Ownership provides the land status user with a current snapshot of ownership within National Forest boundaries.

This dataset is a compilation of tax parcel polygon and point layers from the seven Twin Cities, Minnesota metropolitan area counties of Anoka, Carver, Dakota, Hennepin, Ramsey, Scott and Washington. The seven counties were assembled into a common coordinate system. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. (See section 5 of the metadata). The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties will polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. The primary example of this is the condominium. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

Polygon and point counts for each county are as follows (based on the January, 2007 dataset):

Anoka = 129,392 polygons, 129,392 points
Carver = 37,021 polygons, 37,021 points
Dakota = 135,586 polygons, 148,952 points
Hennepin = 358,064 polygons, 419,736 points
Ramsey = 148,967 polygons, 166,280 points
Scott = 54,741 polygons, 54,741 points
Washington = 97,922 polygons, 102,309 points

This is a MetroGIS Regionally Endorsed dataset.

Each of the seven Metro Area counties has entered into a multiparty agreement with the Metropolitan Council to assemble and distribute the parcel data for each county as a regional (seven county) parcel dataset.

A standard set of attribute fields is included for each county. The attributes are identical for the point and polygon datasets. Not all attributes fields are populated by each county. Detailed information about the attributes can be found in the MetroGIS Regional Parcels Attributes 2006 document.

Additional information may be available in the individual metadata for each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person listed in the individual county metadata.

Anoka = http://www.anokacounty.us/315/GIS

Caver = http://www.co.carver.mn.us/GIS

Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx

Hennepin: http://www.hennepin.us/gisopendata

Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data

Scott = http://www.scottcountymn.gov/1183/GIS-Data-and-Maps

Washington = http://www.co.washington.mn.us/index.aspx?NID=1606

This dataset includes one file for each of the 51 counties that were collected, as well as a CA_Merged file with the parcels merged into a single file.Note – this data does not include attributes beyond the parcel ID number (PARNO) – that will be provided when available, most likely by the state of California.DownloadA 1.6 GB zipped file geodatabase is available for download - click here.DescriptionA geodatabase with parcel boundaries for 51 (out of 58) counties in the State of California. The original target was to collect data for the close of the 2013 fiscal year. As the collection progressed, it became clear that holding to that time standard was not practical. Out of expediency, the date requirement was relaxed, and the currently available dataset was collected for a majority of the counties. Most of these were distributed with minimal metadata.The table “ParcelInfo” includes the data that the data came into our possession, and our best estimate of the last time the parcel dataset was updated by the original source. Data sets listed as “Downloaded from” were downloaded from a publicly accessible web or FTP site from the county. Other data sets were provided directly to us by the county, though many of them may also be available for direct download. Â These data have been reprojected to California Albers NAD84, but have not been checked for topology, or aligned to county boundaries in any way. Tulare County’s dataset arrived with an undefined projection and was identified as being California State Plane NAD83 (US Feet) and was assigned by ICE as that projection prior to reprojection. Kings County’s dataset was delivered as individual shapefiles for each of the 50 assessor’s books maintained at the county. These were merged to a single feature class prior to importing to the database.The attribute tables were standardized and truncated to include only a PARNO (APN). The format of these fields has been left identical to the original dataset. The Data Interoperablity Extension ETL tool used in this process is included in the zip file. Where provided by the original data sources, metadata for the original data has been maintained. Please note that the attribute table structure changes were made at ICE, UC Davis, not at the original data sources.Parcel Source InformationCountyDateCollecDateCurrenNotesAlameda4/8/20142/13/2014Download from Alamenda CountyAlpine4/22/20141/26/2012Alpine County PlanningAmador5/21/20145/14/2014Amador County Transportation CommissionButte2/24/20141/6/2014Butte County Association of GovernmentsCalaveras5/13/2014Download from Calaveras County, exact date unknown, labelled 2013Contra Costa4/4/20144/4/2014Contra Costa Assessor’s OfficeDel Norte5/13/20145/8/2014Download from Del Norte CountyEl Dorado4/4/20144/3/2014El Dorado County AssessorFresno4/4/20144/4/2014Fresno County AssessorGlenn4/4/201410/13/2013Glenn County Public WorksHumboldt6/3/20144/25/2014Humbodt County AssessorImperial8/4/20147/18/2014Imperial County AssessorKern3/26/20143/16/2014Kern County AssessorKings4/21/20144/14/2014Kings CountyLake7/15/20147/19/2013Lake CountyLassen7/24/20147/24/2014Lassen CountyLos Angeles10/22/201410/9/2014Los Angeles CountyMadera7/28/2014Madera County, Date Current unclear likely 7/2014Marin5/13/20145/1/2014Marin County AssessorMendocino4/21/20143/27/2014Mendocino CountyMerced7/15/20141/16/2014Merced CountyMono4/7/20144/7/2014Mono CountyMonterey5/13/201410/31/2013Download from Monterey CountyNapa4/22/20144/22/2014Napa CountyNevada10/29/201410/26/2014Download from Nevada CountyOrange3/18/20143/18/2014Download from Orange CountyPlacer7/2/20147/2/2014Placer CountyRiverside3/17/20141/6/2014Download from Riverside CountySacramento4/2/20143/12/2014Sacramento CountySan Benito5/12/20144/30/2014San Benito CountySan Bernardino2/12/20142/12/2014Download from San Bernardino CountySan Diego4/18/20144/18/2014San Diego CountySan Francisco5/23/20145/23/2014Download from San Francisco CountySan Joaquin10/13/20147/1/2013San Joaquin County Fiscal year close dataSan Mateo2/12/20142/12/2014San Mateo CountySanta Barbara4/22/20149/17/2013Santa Barbara CountySanta Clara9/5/20143/24/2014Santa Clara County, Required a PRA requestSanta Cruz2/13/201411/13/2014Download from Santa Cruz CountyShasta4/23/20141/6/2014Download from Shasta CountySierra7/15/20141/20/2014Sierra CountySolano4/24/2014Download from Solano Couty, Boundaries appear to be from 2013Sonoma5/19/20144/3/2014Download from Sonoma CountyStanislaus4/23/20141/22/2014Download from Stanislaus CountySutter11/5/201410/14/2014Download from Sutter CountyTehama1/16/201512/9/2014Tehama CountyTrinity12/8/20141/20/2010Download from Trinity County, Note age of data 2010Tulare7/1/20146/24/2014Tulare CountyTuolumne5/13/201410/9/2013Download from Tuolumne CountyVentura11/4/20146/18/2014Download from Ventura CountyYolo11/4/20149/10/2014Download from Yolo CountyYuba11/12/201412/17/2013Download from Yuba County

An area depicting ownership parcels of the surface estate. This data is intended for read-only use. The PAD-US feature classes were developed by the Forest Service for submission to the Protected Areas Database of the United States (PAD-US). It is the official inventory of public parks and other protected open space. With more than 3 billion acres in 150,000 holdings, the spatial data in PAD-US represents public lands held in trust by thousands of national, State and regional/local governments, as well as non-profit conservation organizations. PAD-US is published by the U.S. Geological Survey Gap Analysis Program (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity, conservation, recreation, public health, climate change adaptation, and infrastructure investment. USFS PAD-US data is pulled weekly from USFS Lands data. This dataset is more current than the combined annual update of PAD-US from USGS GAP.

In 2023, some *** billion Parcel Select packages were received via the U.S. Postal Service in the United States (U.S.). That year, around *** billion first-class mail was received by mail in the U.S.

Discover the booming US Domestic Courier, Express, and Parcel (CEP) market. This in-depth analysis reveals key trends, growth drivers (e-commerce, B2C delivery), and market restraints from 2019-2033. Explore market segmentation, leading companies, and regional insights. Recent developments include: July 2023: XLT Pack and Ship Services opened a service center in James Town, Virginia, where there were no packing and shipping centers available. It offers packing and shipping services through Spee-Dee Delivery Service Inc. and other companies.March 2023: United Parcel Services announced the opening of a new 168,000-square-foot building in Douglas County, in collaboration with AVK America, to improve the region's connectivity within UPS' worldwide package and distribution network.January 2023: YRC Worldwide Inc. expanded YRC Freight's Regional Next-Day Service with the addition of more lanes in the Mid-Atlantic region of the United States. Thirteen terminals will offer the service, with Richmond as a hub for the region.. Key drivers for this market are: Increasing consumption of canned and frozen food, Growth urbanization and increased adoption of healthy lifestyle. Potential restraints include: Limited self-life of frozen food, Growing awareness regarding the consumption of fresh vegetables and fruits. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

The statewide composite of parcels (cadastral) data for New Jersey was developed during the Parcels Normalization Project in 2008-2014 by the NJ Office of Information Technology, Office of GIS (NJOGIS.) The normalized parcels data are compatible with the NJ Department of the Treasury system currently used by Tax Assessors, and those records have been joined in this dataset. This composite of parcels data serves as one of the framework GIS datasets for New Jersey. Stewardship and maintenance of the data will continue to be the purview of county and municipal governments, but the statewide composite will be maintained by NJOGIS.Parcel attributes were normalized to a standard structure, specified in the NJ GIS Parcel Mapping Standard, to store parcel information and provide a PIN (parcel identification number) field that can be used to match records with suitably-processed property tax data. The standard is available for viewing and download at https://njgin.state.nj.us/oit/gis/NJ_NJGINExplorer/docs/NJGIS_ParcelMappingStandardv3.2.pdf. The PIN also can be constructed from attributes available in the MOD-IV Tax List Search table (see below).This feature class includes a large number of additional attributes from matched MOD-IV records; however, not all MOD-IV records match to a parcel, for reasons explained elsewhere in this metadata record. The statewide property tax table, including all MOD-IV records, is available as a separate download "MOD-IV Tax List Search Plus Database of New Jersey." Users who need only the parcel boundaries with limited attributes may obtain those from a separate download "Parcels Composite of New Jersey". Also available separately are countywide parcels and tables of property ownership and tax information extracted from the NJ Division of Taxation database.The polygons delineated in this dataset do not represent legal boundaries and should not be used to provide a legal determination of land ownership. Parcels are not survey data and should not be used as such. Please note that these parcel datasets are not intended for use as tax maps. They are intended to provide reasonable representations of parcel boundaries for planning and other purposes. Please see Data Quality / Process Steps for details about updates to this composite since its first publication.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

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US Courier, Express, And Parcel Market Size 2024-2028

The US courier, express, and parcel (CEP) market size is forecast to increase by USD 28.5 billion at a CAGR of 5.4% between 2023 and 2028.

The courier, express, and parcel (CEP) market In the US is witnessing significant growth due to the adoption of advanced technologies such as GPS, predictive analytics, and automation in logistics and supply chain networks. The integration of last-mile delivery models with CEP companies is also driving market growth, as consumers increasingly demand faster and more convenient delivery options. However, the market is facing challenges from sharing-based business models, which are disrupting traditional CEP business models. The COVID-19 pandemic has further accelerated the trend towards e-commerce and the need for efficient and reliable CEP services, particularly In the delivery of vaccines and other time-sensitive goods.

What will be the size of the US Courier, Express, And Parcel (CEP) Market during the forecast period?

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The market in the global economy experiences significant activity, driven primarily by theincrease in e-commerce sales and the increasing preference for convenient and fast delivery options among consumers. CEP (Courier, Express, and Parcel) volumes continue to rise, fueled by the integration of returns management solutions in physical stores and the growing popularity of 'Happy Returns' and similar services. The transportation services sector, comprised of couriers and messengers, as well as postal service workers, plays a pivotal role in this market. Key players in this sector include major retailers such as eBay, Walmart, Target, and Apple, who leverage their logistics capabilities to offer seamless delivery options to customers.
The market also benefits from advancements in air travel and water transportation, as well as the development of port infrastructure and maritime administration. Furthermore, the increasing reliance on CEP services is influenced by various macroeconomic factors, including gasoline and petroleum prices, refining capacity, and refinery accidents. Despite these challenges, the market remains a dynamic and growing sector, demonstrating resilience and adaptability In the face of changing consumer demands and market conditions.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Consumer

  B2B
  B2C
  C2C


Delivery

  Domestic
  International


Geography

  US

By Consumer Insights

The B2B segment is estimated to witness significant growth during the forecast period.

The market caters to both business-to-business (B2B) and business-to-consumer (B2C) transactions. B2B transactions involve corporations procuring goods for their operations, contributing significantly to the supply chain. The B2B segment of the market is anticipated to expand moderately during the forecast period, driven by the expanding e-commerce and increasing Internet penetration In the US. Physical stores continue to leverage CEP services for returns management through partners like Happy Returns. Consumers increasingly rely on CEP for convenient last-mile delivery. Key industries such as eBay, Walmart, Target, Apple, and others contribute substantially to CEP volumes.

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The B2B segment was valued at USD 42.00 billion in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adoption of US Courier, Express, And Parcel (CEP) Market?

The key driver of the market is the adoption of new technologies in courier, express, and parcel supply chain network systems.

The market is experiencing significant growth due to advancements in technology. These innovations are revolutionizing the industry by providing real-time parcel tracking, eliminating delivery uncertainties, and enhancing convenience for consumers. Key technologies driving this transformation include global positioning systems (GPS), bar-coding, management information systems (MIS), 3D printing, robotics, radio frequency identification (RFID), warehouse management software, and transportation management software. companies are also investing in mobility technologies to improve last-mile connectivity. For instance, FedEx Corp. Partnered with Chanje Energy Inc. In February 2020 to enhance their last-mile delivery capabilities. Consumer

Graph and download economic data for Producer Price Index by Industry: U.S. Postal Service: High Density and Saturation Flats and Parcels (PCU491110491110731) from Dec 2012 to Sep 2025 about postal, services, PPI, industry, inflation, price index, indexes, price, and USA.

This statistic gives the size of the express and small parcels market in North America from 2012 to 2016 and gives a forecast for 2020. In 2016, the North American express and small parcels market was sized at over ***** billion euros.