Land Parcel Data provides detailed information about individual parcels of land, offering insights into land ownership, boundaries, zoning regulations, land use, and other pertinent details.

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

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.

Residential Street Width and Value

Insight into US County Land Values and Subdivision Regulations

By [source]

About this dataset

This dataset serves as a comprehensive view into land value distributions and development regulations of residential street widths in some of the largest counties in the United States. Drawing from OpenStreetMap and county tax assessor parcel data, it provides an intricate look at the correlation between property values and streetscape configurations across 20 major cities. This information is crucial for policy makers, urban planners, developers, and homeowners alike for understanding how street design can positively affect land values - manifesting in well-planned subdivisions and healthier neighborhoods. With detailed insight into these elements available for inspection through this dataset, informed decisions can be made that can dramatically improve communities throughout America

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How to use the dataset

• Subdivision Name: This column shows the name of the subdivision that houses each street included in the dataset. Use this information to identify which streets are located in a specific suburb or area.
• Street Name: This is the official street name as it appears on OpenStreetMap records, providing information about all residential streets included in this dataset.
• Parcel Side length: This is length (in feet) of one side of each parcel associated with a specific street from OpenStreetMap records. Note that multi-parcel lots will have multiple entries for each parcel side length and should be analyzed separately for accuracy when using this representation of land availability for each residence along a video row order within a subdivision..
• Land Value Rating: A rating out of 5 stars representing the total Monetary Value, as determined by tax assessments, for all parcels within a subdivision, including those not included on OpenStreetMap records due to non-residential uses or development restrictions (ROW). Values may vary based on urban/suburban location and year assessed (if later than 2019), so consider applying these ratings with research into other datasets such as Zillow home values over time to see if there are changes in Urban Renewal projects or appreciations over time across different locations studied here.. 5 Number Of Parcels Per Street: The number of individual parcels associated with each record selected from OpenStreetMap records within this dataset, giving an indication of how much space is available per house/lot within that particular area..
  6 Right Of Way Widths: Representing width (in feet) along certain roads given to public access through city ordinances, such Right-Of-Ways extending beyond any single existing lot line up to sometimes hundreds and thousands feet into urban or residential areas serving communities' need like ambulance access etc…

Research Ideas

• Utilizing this dataset, municipalities can better understand development trends across their jurisdictions and inform smart growth initiatives.
• Municipalities may benefit from analyzing the relationship between street width and land value to proactively adjust regulations to encourage further investment in their communities.
• Local homeowners could use this data set to compare the amount of street right-of-way being dedicated in different residential areas to obtain insight into potential value of real estate investments in a particular area

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: data_dictionary.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Datasets referenced in "This land is your land, this could be marsh land: Property parcel characteristics of marsh migration corridors in Rhode Island, USA". This dataset is not publicly accessible because: Parcel data is commercially available as referenced in the paper and the SLAAM models are available at the referenced location in the manuscript. It can be accessed through the following means: Request to author and purchased through provider. Format: XLSX and GEOTIFF. This dataset is associated with the following publication: Burman, E., N. Merrill, K. Mulvaney, M. Bradley, and C. Wigand. This land is your land, this could be marsh land: Property parcel characteristics of marsh migration corridors in Rhode Island, USA. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 351(February 2024): 119657, (2024).

This Quarter Section feature class depicts PLSS Second Divisions . PLSS townships are subdivided in a spatial hierarchy of first, second, and third division. These divisions are typically aliquot parts ranging in size from 640 acres to 160 to 40 acres, and subsequently all the way down to 2.5 acres. The data in this feature class was translated from the PLSSSecondDiv feature class in the original production data model, which defined the second division for a specific parcel of land. Metadata

"Due to the size of this dataset, both Shapefile and Spreadsheet download options will not work as expected. The File Geodatabase is an alternative option for this data download"This is SCAG's 2019 Annual Land Use (ALU v. 2019.1) at the parcel-level, updated as of February 2021. This dataset has been modified to include additional attributes in order to feed SCAG's Housing Element Parcel Tool (HELPR), version 2.0. The dataset will be further reviewed and updated as additional information is released. Please refer to the tables below for data dictionary and SCAG’s land use classification.Field NameData TypeField DescriptionPID19Text2019 SCAG’s parcel unique IDAPN19Text2019 Assessor’s parcel numberCOUNTYTextCounty name (based on 2016 county boundary)COUNTY_IDDoubleCounty FIPS code (based on 2016 county boundary)CITYTextCity name (based on 2016 city boundary)CITY_IDDoubleCity FIPS code (based on 2016 city boundary)MULTIPARTShort IntegerMultipart feature (the number of multiple polygons; '1' = singlepart feature)STACKLong IntegerDuplicate geometry (the number of duplicate polygons; '0' = no duplicate polygons)ACRESDoubleParcel area (in acreage)GEOID20Text2020 Census Block Group GEOIDSLOPEShort IntegerSlope information1APN_DUPLong IntegerDuplicate APN (the number of multiple tax roll property records; '0' = no duplicate APN)IL_RATIODoubleRatio of improvement assessed value to land assessed valueLU19Text2019 existing land useLU19_SRCTextSource of 2019 existing land use2SCAGUID16Text2016 SCAG’s parcel unique IDAPNText2016 Assessor’s parcel numberCITY_GP_COText2016 Jurisdiction’s general plan land use designationSCAG_GP_COText2016 SCAG general plan land use codeSP_INDEXShort IntegerSpecific plan index ('0' = outside specific plan area; '1' = inside specific plan area)CITY_SP_COText2016 Jurisdiction’s specific plan land use designationSCAG_SP_COText2016 SCAG specific plan land use codeCITY_ZN_COText2016 Jurisdiction’s zoning codeSCAG_ZN_COText2016 SCAG zoning codeLU16Text2016 existing land useYEARLong IntegerDataset yearPUB_OWNShort IntegerPublic-owned land index ('1' = owned by public agency)PUB_NAMETextName of public agencyPUB_TYPETextType of public agency3BF_SQFTDoubleBuilding footprint area (in square feet)4BSF_NAMETextName of brownfield/superfund site5BSF_TYPETextType of brownfield/superfund site5FIREShort IntegerParcel intersects CalFire Very High Hazard Local Responsibility Areas or State Responsibility Areas (November 2020 version) (CalFIRE)SEARISE36Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)1 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)SEARISE72Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)2 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)FLOODShort IntegerParcel intersects with a FEMA 100 Year Flood Plain data from the Digital Flood Insurance Rate Map (DFIRM), obtained from Federal Emergency Management Agency (FEMA) in August 10, 2017EQUAKEShort IntegerParcel intersects with an Alquist-Priolo Earthquake Fault Zone (California Geological Survey; 2018)LIQUAFAShort IntegerParcel intersects with a Liquefaction Susceptibility Zone (California Geological Survey; 2016)LANDSLIDEShort IntegerParcel intersects with a Landslide Hazard Zone (California Geological Survey; 2016)CPADShort IntegerParcel intersects with a protected area from the California Protected Areas Database(CPAD) – www.calands.org (accessed April 2021)RIPARIANShort IntegerParcel centroid falls within Active River Areas(2010)or parcel intersects with a Wetland Area in the National Wetland Inventory(Version 2)WILDLIFEShort IntegerParcel intersects with wildlife habitat (US Fish & Wildlife ServiceCritical Habitat, Southern California Missing Linkages, Natural Lands & Habitat Corridors from Connect SoCal, CEHC Essential Connectivity Areas,Critical Coastal Habitats)CNDDBShort IntegerThe California Natural Diversity Database (CNDDB)includes the status and locations of rare plants and animals in California. Parcels that overlap locations of rare plants and animals in California from the California Natural Diversity Database (CNDDB)have a greater likelihood of encountering special status plants and animals on the property, potentially leading to further legal requirements to allow development (California Department of Fish and Wildlife). Data accessed in October 2020.HCPRAShort IntegerParcel intersects Natural Community & Habitat Conservation Plans Reserve Designs from the Western Riverside MHSCP, Coachella Valley MHSCP, and the Orange County Central Coastal NCCP/HCP, as accessed in October 2020WETLANDShort IntegerParcel intersects a wetland or deepwater habitat as defined by the US Fish & Wildlife Service National Wetlands Inventory, Version 2.UAZShort IntegerParcel centroid lies within a Caltrans Adjusted Urbanized AreasUNBUILT_SFDoubleDifference between parcel area and building footprint area expressed in square feet.6GRCRY_1MIShort IntegerThe number of grocery stores within a 1-mile drive7HEALTH_1MIShort IntegerThe number of healthcare facilities within a 1-mile drive7OPENSP_1MIShort IntegerQuantity of open space (roughly corresponding to city blocks’ worth) within a 1-mile drive7TCAC_2021TextThe opportunity level based on the 2021 CA HCD/TCAC opportunity scores.HQTA45Short IntegerField takes a value of 1 if parcel centroid lies within a 2045 High-Quality Transit Area (HQTA)JOB_CTRShort IntegerField takes a value of 1 if parcel centroid lies within a job centerNMAShort IntegerField takes a value of 1 if parcel centroid lies within a neighborhood mobility area.ABS_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within an absolute constraint area. See the Sustainable Communities Strategy Technical Reportfor details.VAR_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within a variable constraint area. See the Sustainable Communities Strategy Technical Reportfor details.EJAShort IntegerField takes a value of 1 if parcel centroid lies within an Environmental Justice Area. See the Environmental Justice Technical Reportfor details.SB535Short IntegerField takes a value of 1 if parcel centroid lies within an SB535 Disadvantaged Community area. See the Environmental Justice Technical Reportfor details.COCShort IntegerField takes a value of 1 if parcel centroid lies within a Community of Concern See the Environmental Justice Technical Reportfor details.STATEShort IntegerThis field is a rudimentary estimate of which parcels have adequate physical space to accommodate a typical detached Accessory Dwelling Unit (ADU)8.SBShort IntegerIndex of ADU eligibility according to the setback reduction policy scenario (from 4 to 2 feet) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SMShort IntegerIndex of ADU eligibility according to the small ADU policy scenario (from 800 to 600 square feet ADU) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)PKShort IntegerIndex of ADU eligibility according to parking space exemption (200 square feet) policy scenario (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SMShort IntegerIndex of ADU eligibility according to both the setback reduction and small ADU policy scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_PKShort IntegerIndex of ADU eligibility according to both the setback reduction and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SM_PKShort IntegerIndex of ADU eligibility according to both the small ADU policy and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SM_PKShort IntegerIndex of ADU eligibility according to the setback reduction, small ADU, and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)1. Slope: '0' - 0~4 percent; '5' - 5~9 percent; '10' - 10~14 percent; '15' = 15~19 percent; '20' - 20~24 percent; '25' = 25 percent or greater.2. Source of 2019 existing land use: SCAG_REF- SCAG's regional geospatial datasets;ASSESSOR- Assessor's 2019 tax roll records; CPAD- California Protected Areas Database (version 2020a; accessed in September 2020); CSCD- California School Campus Database (version 2018; accessed in September 2020); FMMP- Farmland Mapping and Monitoring Program's Important Farmland GIS data (accessed in September 2020); MIRTA- U.S. Department of Defense's Military Installations, Ranges, and Training Areas GIS data (accessed in September 2020)3. Type of public agency includes federal, state, county, city, special district, school district, college/university, military.4. Based on 2019 building footprint data obtained from BuildingFootprintUSA (except that 2014 building footprint data was used for Imperial County). Please note that 2019 building footprint data does not cover the entire SCAG region (overlapped with 83% of parcels in the SCAG Region).5. Includes brownfield/superfund site whose address information are matched by SCAG rooftop address locator. Brownfield data was obtained from EPA's Assessment, Cleanup and Redevelopment Exchange System (ACRES) database, Cleanups in my community (CIMC), DTSC brownfield Memorandum of Agreement (MOA). Superfund site data was obtained from EPA's Superfund Enterprise Management System (SEMS) database.6. Parcels with a zero value for building footprint area are marked as NULL to indicate this field is not reliable.7. These values are intended as a rudimentary indicator of accessibility developed by SCAG using 2016 InfoUSA business establishment data and 2017 California Protected Areas data. See documentation for details.8. A detailed study conducted by Cal Poly Pomona (CPP) and available hereconducted an extensive review of state and local requirements and development trends for ADUs in the SCAG region and developed a baseline set of assumptions for estimating how many of a jurisdiction’s parcels

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.

This service combines parcel data from various local government bodies in Alaska and describes a subset of input fields using consistent field names. This service was designed for use in statewide applications that only require specific types of land parcel information, and benefit from having this information in a single service with a consistent schema. Any changes to input parcel data will trigger this service to update. Note that many input services do not include a truly unique identifier, or sometimes any identifier at all. The 'parcel_id' field contains a record identifier carried over from the input service, or is null if there is none. The 'local_gov' value of any record can be used to reference an input parcel web service in the table below.During processing, a *mostly unique identifier is created, called 'feature_id'. Duplicate values will occur for records that have identical 'local_gov' and 'parcel_id' values and also identical geometries. These cases are extremely rare (< 0.003%), and for the vast majority of records 'feature_id' is unique. Any duplicate values will be attached to parcels in the exact same place.Please reference original parcel web services if your use case requires official, authoritative, or comprehensive land parcel information. Local Government Parcel Web Service

    Anchorage Municipality
    https://services2.arcgis.com/Ce3DhLRthdwbHlfF/ArcGIS/rest/services/PropertyInformation_Hosted/FeatureServer/0


    Denali Borough
    https://arcgis.dnr.alaska.gov/arcgis/rest/services/OpenData/Administrative_BoroughParcels/FeatureServer/1

    Bristol Bay Borough
    https://services8.arcgis.com/MqzStQjDmKoNl0E6/ArcGIS/rest/services/TaxParcels_Related/FeatureServer/0


    Dillingham Census Area
    https://services3.arcgis.com/gdLTz4xpy5IxwbSz/arcgis/rest/services/ParcelsOnline/FeatureServer/0


    Fairbanks North Star Borough
    https://services.arcgis.com/f4rR7WnIfGBdVYFd/ArcGIS/rest/services/Tax_Parcels/FeatureServer/0


    Haines Borough
    https://services3.arcgis.com/pMlUMMROURtJLUZt/ArcGIS/rest/services/ParcelsOnline/FeatureServer/0


    Juneau City & Borough
    https://services.arcgis.com/kpMKjjLr8H1rZ4XO/arcgis/rest/services/Juneau_Parcel_Viewer/FeatureServer/0


    Kenai Peninsula Borough
    https://services.arcgis.com/ba4DH9pIcqkXJVfl/ArcGIS/rest/services/Redacted_Parcels_view/FeatureServer/0


    Ketchikan Borough
    https://services2.arcgis.com/65jtiGuzdaRB5FxF/ArcGIS/rest/services/KetchikanAKFeatures/FeatureServer/0


    Kodiak Island Borough
    https://services1.arcgis.com/R5BNizttyFKxRSMm/arcgis/rest/services/KIB_Parcels/FeatureServer/0


    Matanuska-Susitna Borough
    https://maps.matsugov.us/map/rest/services/OpenData/Cadastral_Parcels/FeatureServer/0


    Nome Census Area
    https://services9.arcgis.com/Oi9vFzXc8ZcONgM6/arcgis/rest/services/Parcels_Joined_with_Taxroll_Symbolized_by_Exempt/FeatureServer/0


    North Slope Borough
    https://gis-public.north-slope.org/server/rest/services/Lama/Parcels_sql/FeatureServer/9


    Petersburg Borough
    https://services7.arcgis.com/RqATEQTpM1W1xU9c/ArcGIS/rest/services/Lots/FeatureServer/0


    Sitka City & Borough
    https://services7.arcgis.com/EozEvrS4g3SEhtG3/ArcGIS/rest/services/Sitka_Parcels_2022/FeatureServer/0


    Wrangell City & Borough
    https://services7.arcgis.com/7cBSaoaaRaH5ojZy/arcgis/rest/services/Parcels/FeatureServer/0


    Yakutat City & Borough
    https://services2.arcgis.com/gRKiTtxkoTx0gERB/ArcGIS/rest/services/ParcelsOnline/FeatureServer/0

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.

An area depicted as surface ownership parcels dissolved on the same ownership classification. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

Abstract

Historical tax assessment data for all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C. Each table represents a previous edition of CoreLogic's tax assessment data.

The CoreLogic Smart Data Platform (SDP) Historical Property data was formerly known as the CoreLogic Tax History data. The CoreLogic SDP Historical Property data is an enhanced version of the CoreLogic Tax History data. The CoreLogic SDP Historical Property data contains almost all of the variables that were included in the CoreLogic Tax History data, as well as additional property-level characteristics.

Methodology

In the United States, parcel data is public record information that describes a division of land (also referred to as "property" or "real estate"). Each parcel is given a unique identifier called an Assessor’s Parcel Number or APN. The two principal types of records maintained by county government agencies for each parcel of land are deed and property tax records. When a real estate transaction takes place (e.g. a change in ownership), a property deed must be signed by both the buyer and seller. The deed will then be filed with the County Recorder’s offices, sometimes called the County Clerk-Recorder or other similar title. Property tax records are maintained by County Tax Assessor’s offices; they show the amount of taxes assessed on a parcel and include a detailed description of any structures or buildings on the parcel, including year built, square footages, building type, amenities like a pool, etc. There is not a uniform format for storing parcel data across the thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from CoreLogic’s parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see CoreLogic 2024 GitLab.

Usage

Each table contains an archived snapshot of the property data, roughly corresponding to the following assessed years:

• Historical Property 1 = 2022-2023
• Historical Property 2 = 2021-2022
• Historical Property 3 = 2020-2021
• Historical Property 4 = 2019-2020
• Historical Property 5 = 2018-2019
• Historical Property 6 = 2017-2018
• Historical Property 7 = 2016-2017
• Historical Property 8 = 2015-2016
• Historical Property 9 = 2014-2015
• Historical Property 10 = 2013-2014
• Historical Property 11 = 2012-2013
• Historical Property 12 = 2011-2012
• Historical Property 13 = 2010-2011
• Historical Property 14 = 2009-2010
• Historical Property 15 = 2008-2009

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Users can check theASSESSED_YEAR variable to confirm the year of assessment.

Roughly speaking, the tables use the following census geographies:

• 2020 Census Tract: Historical Property 1-2
• 2010 Census Tract: Historical Property 3 – 12
• 2000 Census Tract: Historical Property 13 – 15

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The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.

For more information about included variables, please see **core_logic_sdp_historical_property_data_dictionary_2024.txt **and Historical Property_v3.xlsx.

Under Supporting files, users can also find record counts per FIPS code for each edition of the Historical Property data.

For more information about how the CoreLogic Smart Data Platform: Historical Property data compares to legacy data, please see core_logic_legacy_content_mapping.pdf.

Bulk Data Access

Data access is required to view this section.

Boundaries of legal units of land division as inventoried by the Mat-Su Borough Assessment Division. Boundaries are established from a variety of sources including cadastral plats, patents, subdivision plats, deeds, land contracts, right-of-way plats, and others. Each feature represents a parcel of land that is inventoried by a unique identifier, referred to as an “account” or (“taxid”) number. This dataset also includes multi-unit structures which have separate tax accounts for each unit, such as condominium units, mobile home parks, and business parks. Generalized land ownership is also represented in this dataset. Several fields have corresponding data sets which further explain the codes in the fields (e.g. For ESN code explanations reference the ESN data set.)

The Florida Department of Revenue’s Property Tax Oversight(PTO) program collects parcel level Geographic Information System (GIS) data files every April from all of Florida’s 67 county property appraisers’ offices. This GIS data was exported from these file submissions in August 2024. The GIS parcel polygon features have been joined with thereal property roll (Name – Address – Legal, or NAL)file. No line work was adjusted between county boundaries.The polygon data set represents the information property appraisers gathered from the legal description on deeds, lot layout of recorded plats, declaration of condominium documents, recorded and unrecorded surveys.Individual parcel data is updated continually by each county property appraiser as needed. The GIS linework and related attributions for the statewide parcel map are updated annually by the Department every August. The dataset extends countywide and is attribute by Federal Information Processing Standards (FIPS) code.DOR reference with FIPS county codes and attribution definitions - https://fgio.maps.arcgis.com/home/item.html?id=ff7b985e139c4c7ba844500053e8e185If you discover the inadvertent release of a confidential record exempt from disclosure pursuant to Chapter 119, Florida Statutes, public records laws, immediately notify the Department of Revenue at 850-717-6570 and your local Florida Property Appraisers’ Office.Please contact the county property appraiser with any parcel specific questions: Florida Property Appraisers’ Offices:Alachua County Property Appraiser – https://www.acpafl.org/Baker County Property Appraiser – https://www.bakerpa.com/Bay County Property Appraiser – https://baypa.net/Bradford County Property Appraiser – https://www.bradfordappraiser.com/Brevard County Property Appraiser – https://www.bcpao.us/Broward County Property Appraiser – https://bcpa.net/Calhoun County Property Appraiser – https://calhounpa.net/Charlotte County Property Appraiser – https://www.ccappraiser.com/Citrus County Property Appraiser – https://www.citruspa.org/Clay County Property Appraiser – https://ccpao.com/Collier County Property Appraiser – https://www.collierappraiser.com/Columbia County Property Appraiser – https://columbia.floridapa.com/DeSoto County Property Appraiser – https://www.desotopa.com/Dixie County Property Appraiser – https://www.qpublic.net/fl/dixie/Duval County Property Appraiser – https://www.coj.net/departments/property-appraiser.aspxEscambia County Property Appraiser – https://www.escpa.org/Flagler County Property Appraiser – https://flaglerpa.com/Franklin County Property Appraiser – https://franklincountypa.net/Gadsden County Property Appraiser – https://gadsdenpa.com/Gilchrist County Property Appraiser – https://www.qpublic.net/fl/gilchrist/Glades County Property Appraiser – https://qpublic.net/fl/glades/Gulf County Property Appraiser – https://gulfpa.com/Hamilton County Property Appraiser – https://hamiltonpa.com/Hardee County Property Appraiser – https://hardeepa.com/Hendry County Property Appraiser – https://hendryprop.com/Hernando County Property Appraiser – https://www.hernandopa-fl.us/PAWEBSITE/Default.aspxHighlands County Property Appraiser – https://www.hcpao.org/Hillsborough County Property Appraiser – https://www.hcpafl.org/Holmes County Property Appraiser – https://www.qpublic.net/fl/holmes/Indian River County Property Appraiser – https://www.ircpa.org/Jackson County Property Appraiser – https://www.qpublic.net/fl/jackson/Jefferson County Property Appraiser – https://jeffersonpa.net/Lafayette County Property Appraiser – https://www.lafayettepa.com/Lake County Property Appraiser – https://www.lakecopropappr.com/Lee County Property Appraiser – https://www.leepa.org/Leon County Property Appraiser – https://www.leonpa.gov/Levy County Property Appraiser – https://www.qpublic.net/fl/levy/Liberty County Property Appraiser – https://libertypa.org/Madison County Property Appraiser – https://madisonpa.com/Manatee County Property Appraiser – https://www.manateepao.gov/Marion County Property Appraiser – https://www.pa.marion.fl.us/Martin County Property Appraiser – https://www.pa.martin.fl.us/Miami-Dade County Property Appraiser – https://www.miamidade.gov/pa/Monroe County Property Appraiser – https://mcpafl.org/Nassau County Property Appraiser – https://www.nassauflpa.com/Okaloosa County Property Appraiser – https://okaloosapa.com/Okeechobee County Property Appraiser – https://www.okeechobeepa.com/Orange County Property Appraiser – https://ocpaweb.ocpafl.org/Osceola County Property Appraiser – https://www.property-appraiser.org/Palm Beach County Property Appraiser – https://www.pbcgov.org/papa/index.htmPasco County Property Appraiser – https://pascopa.com/Pinellas County Property Appraiser – https://www.pcpao.org/Polk County Property Appraiser – https://www.polkpa.org/Putnam County Property Appraiser – https://pa.putnam-fl.com/Santa Rosa County Property Appraiser – https://srcpa.gov/Sarasota County Property Appraiser – https://www.sc-pa.com/Seminole County Property Appraiser – https://www.scpafl.org/St. Johns County Property Appraiser – https://www.sjcpa.gov/St. Lucie County Property Appraiser – https://www.paslc.gov/Sumter County Property Appraiser – https://www.sumterpa.com/Suwannee County Property Appraiser – https://suwannee.floridapa.com/Taylor County Property Appraiser – https://qpublic.net/fl/taylor/Union County Property Appraiser – https://union.floridapa.com/Volusia County Property Appraiser – https://vcpa.vcgov.org/Wakulla County Property Appraiser – https://mywakullapa.com/Walton County Property Appraiser – https://waltonpa.com/Washington County Property Appraiser – https://www.qpublic.net/fl/washington/Florida Department of Revenue Property Tax Oversight https://floridarevenue.com/property/Pages/Home.aspx

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

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.

This dataset includes US industrial facilities from the Environmental Protection Agency's (EPA) 2017 National Emissions Inventory (NEI), combined with location data from the EPA's Facility Registry Service and land cover classes from the United States Geological Survey's (USGS) National Land Cover Data (NLCD). These land cover classes are measured in square meters at the parcel scale. The matching of facility parcels was done using a tiered approach to enhance spatial accuracy. The parcel data was provided by Homeland Infrastructure Foundation-Level Data (HIFLD) US Parcel Data. Because this parcel data is proprietary, the parcel geometries and fields were removed from the final dataset. However, centroid latitude and longitude coordinates were derived to allow spatial joins with publicly available parcel data.

This dataset is organized by unique EPA NEI facilities data fields. Unique facility observations are identified by the field cleaned_name which represents the concatenated address and/or place name for the facility (dependent on data availability). Each row represents a facility matched to parcel scale land cover information and are associated with the unique identifier MatchID. NLCD land cover fields are described as the total area in meters squared of each facility parcel. Please see data README file for more information on individual data fields.

Known Limitations

• Parcels are matched to the facility and in some cases multiple facilities are matched to the same parcel. Data users may want to omit these multiple match parcels and there is a data flag called multi_match that enables this.
• Approximately 15% of the dataset includes facilities where the latitude/longitude coordinates are over 200 meters from the matched parcel. Spot checking these instances revealed that, in many cases, the facility latitude and longitude locations did not accurately match the street address, city, or postal zip code associated with the facility. These instances are flagged as a potential source of inaccuracy and can be removed at the users discretion.

Abstract

The Owner Transfer and Mortgage data covers over 450 million properties, and includes over 50 years of sales history. The tables were generated in June 2024, and cover all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C.

The Owner Transfer data provides historical information about property sales and ownership-related transactions, including full, nominal, and quitclaim transactions (involving a change in title/ownership). It contains comprehensive property and transaction information, such as property characteristics, current ownership, transaction history, title company, cash purchase/foreclosure/resale/short sale indicators, and buyer information.

The Mortgage data provides historical information at the mortgage level, including purchase, refinance, equity, as well as details associated with each transaction, such as lender, loan amount, loan date, interest rate, etc. Mortgage details include mortgage amount, type of loan (conventional, FHA, VHA), mortgage rate type, mortgage purpose (cash out first, consolidation, standalone subordinate), mortgage ARM features, and mortgage indicators such as fixed-rate, conforming loan, construction loan, and private party. The Mortgage data also includes subordinate mortgage types, rate details, and lender details (NMLS ID, Loan Company, Loan Officers).

The CoreLogic Smart Data Platform (SDP) Owner Transfer and Mortgage data was formerly known as the CoreLogic Deed data. The CoreLogic Deed data contained both owner transfer and mortgage information. In the CoreLogic Smart Data Platform (SDP), this data was separated into two tables: Owner Transfer and Mortgage. Between the two tables, the CoreLogic Smart Data Platform (SDP) Owner Transfer and Mortgage data contains almost all of the variables that were included in the CoreLogic Deed data. Further, each CoreLogic Smart Data Platform (SDP) table is augmented with additional owner transfer and mortgage characteristics.

Methodology

In the United States, parcel data is public record information that describes a division of land (also referred to as "property" or "real estate"). Each parcel is given a unique identifier called an Assessor’s Parcel Number or APN. The two principal types of records maintained by county government agencies for each parcel of land are deed and property tax records. When a real estate transaction takes place (e.g. a change in ownership), a property deed must be signed by both the buyer and seller. The deed will then be filed with the County Recorder’s offices, sometimes called the County Clerk-Recorder or other similar title. Property tax records are maintained by County Tax Assessor’s offices; they show the amount of taxes assessed on a parcel and include a detailed description of any structures or buildings on the parcel, including year built, square footages, building type, amenities like a pool, etc. There is not a uniform format for storing parcel data across the thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from CoreLogic’s parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see CoreLogic 2024 GitLab.

Usage

The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.

Mortgage records can be linked to a transaction using the MORTGAGE_COMPOSITE_TRANSACTION_ID.

For more information about included variables, please see:

• core_logic_sdp_owner_transfer_data_dictionary_2024.txt
• core_logic_sdp_mortgage_data_dictionary_2024.txt
• Mortgage_v3.xlsx
• Owner Transfer_v3.xlsx

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For a count of records per FIPS code, please see core_logic_sdp_owner_transfer_counts_2024.txt and core_logic_sdp_mortgage_counts_2024.txt.

For more information about how the CoreLogic Smart Data Platform: Owner Transfer and Mortgage data compares to legacy data, please see core_logic_legacy_content_mapping.pdf.

Bulk Data Access

Data access is required to view this section.

Abstract

Tax assessment data for all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C., as of June 2024.

The CoreLogic Smart Data Platform (SDP) Property data was formerly known as the CoreLogic Tax data. The CoreLogic SDP Property data is an enhanced version of the CoreLogic Tax data. The CoreLogic SDP Property data contains almost all of the variables that were included in the CoreLogic Tax data, and its records are augmented with additional property-level characteristics.

Methodology

In the United States, parcel data is public record information that describes a division of land (also referred to as "property" or "real estate"). Each parcel is given a unique identifier called an Assessor’s Parcel Number or APN. The two principal types of records maintained by county government agencies for each parcel of land are deed and property tax records. When a real estate transaction takes place (e.g. a change in ownership), a property deed must be signed by both the buyer and seller. The deed will then be filed with the County Recorder’s offices, sometimes called the County Clerk-Recorder or other similar title. Property tax records are maintained by County Tax Assessor’s offices; they show the amount of taxes assessed on a parcel and include a detailed description of any structures or buildings on the parcel, including year built, square footages, building type, amenities like a pool, etc. There is not a uniform format for storing parcel data across the thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from CoreLogic’s parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see CoreLogic 2024 GitLab.

Usage

The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.

Census tracts are based on the 2020 census.

For more information about included variables, please see **core_logic_sdp_property_data_dictionary_2024.txt **and Property_v3.xlsx.

For a count of records per FIPS code, please see core_logic_sdp_property_counts_2024.txt.

For more information about how the CoreLogic Smart Data Platform: Property data compares to legacy data, please see core_logic_legacy_content_mapping.pdf.

Bulk Data Access

Data access is required to view this section.

This file geodatabase contains parcel data including plat boundaries, tax parcels, easements and road right of ways. Attributed Parcel Point and Polygon data represent property descriptions (legal descriptions) and land ownership in Ramsey County joined to tax parcels.

The following links can be used to obtain individual metadata pages:

Attributed Parcel Point: plan_attributedparcelpoint.html
Attributed Parcel Poly: plan_attributedparcelpoly.html
Common Interest: plan_commoninterest.html
Subdivision: plan_subdivision.html
Tax Parcels: plan_taxparcel.html
Manufactured Home: plan_manufacturedhome.html
Personal Property: plan_personalproperty.html
Real Property: plan_realproperty.html
State Assessed Property: plan_stateassessedproperty.html
Conveyance Division: plan_conveyancedivision.html
Special Survey: plan_specialsurvey.html
Parcel Info: plan_parcelinfo.html
Easement: plan_easement.html
Landtie: plan_landtie.html
Right of Way: plan_rightofway.html
Historic Right of Way: plan_historicrightofway.html