We Have Elevated Parcel Data To A Whole New Level

With Ease of Integration and Frequency of Updates

What Do We Offer?

We are a land grid GIS mapping company that provides property boundaries, surface ownership, and property information associated with land parcels.

Our dataset is 150+ million parcel boundaries and growing with 99.5% of coverage for the US population. The data is standardized and seamless, regardless of county or state.

Our data is purchased, not leased. You will never need to remove our data from your maps (you own it). We also give you the option to choose future updates or just buy the data at that static point in time. Buy the county, the state, or grab our nationwide coverage. Updates are 50% of the buy price starting in year one. Our data packages are designed to meet all needs and budgets.

Land Parcels Paired with Building Footprints

Explore the most complex and detailed dataset with 135 million+ building footprints. Incredible data that includes primary occupancy, address, height of the building and area of the building. Learn the most about your area to make the right decisions for your projects.

Understand your location:

  • Identify the use and type of building
  • Know where the buildings are positioned
  • Identify vacant land
  • Review building shapes and sizes, assess building height
  • Match IDs and addresses across Land Ownership

Our nationwide and up-to-date building footprints matched with our nationwide and up-to-date tax parcels provide the most comprehensive surface land picture available.

Why Does Parcel Data Matter?

The Unites States was populated with the use of little polygons called land parcels. Each of those parcels were broken into legal boundaries within the land grid. These land parcels have associated owners, used and assessments. Essentially, these polygons, or land parcels, encompass everything from commercial buildings, home, parks, farms, and everything in-between.

Parcel data that is up to date is critical in making decisions about land. The core fundamentals of ownership, address, name, use and other characteristics are the key to understanding what happens on that piece of land.


Tax Parcel Schema:
parcel_id Parcel Identification Number [PIN) /
Assessor's Parcel Number [APN)
county_id County FIPS Identifier
county_name County Name
muni_name Municipality Name
state_abbr State Abbreviation
addr_number Physical/Site House Number
Physical/Site Street Prefix  
Physical/Site Street Name  
Physical/Site Street Suffix  
addr_street_type Physical/Site Street Type
physcity Physical/Site City
physzipPhysical/Site Zip Code  
census_zip Census Zip Code
owner Owner Name
mail_name Mailing Name
mail_address1 House number Street name Street type or PO BOX
mail_address2 Suite number, Building number, or other mailing information
mail_address3 City, State, and Zip
trans_date Most Recent Transfer [Sale] Date
sale_price Sale Price
mkt_val_land Land Market Value
mkt_val_bidg Improvement Market Value
mkt_val_tot Total Market Value
oldg_sqft Building / Home area in square feet
ngh_code Neighborhood code
ngh_code Neighborhood code
land_use_code Land Use Code
Derived Land Use Class ['Residential', 'Agricultural', 'Commercial',  
'Tax Exempt', 'Industrial, or 'Mineral')  
story_height Story Height
muni_idCensus municipality id number  
school_dist_id Census school district id number
acreage_deeded Deeded acreage from source
acreage_calc Acreage calculated from area of geometry
latitude Latitude of a point within the parcel
longitude Longitude of a point within the parcel
owner_occupied Owner Occupied [Query with v=4 or greater to see in output.)
robust_id Second property identifier
usps_residential USPS 'Residential' or 'Commercial' classification. [Query with v=4 or greater to see in output.]
elevation Elevation of property, in feet. (Query with v=4 or greater to see in output.]
buildings Number of buildings. [Query with v=5 or greater to see in output.]
legal_descl Legal Description 1. [Query with v=5 or greater to see in output.]
legal_desc2 Legal Description 1. [Query with v=5 or greater to see in output.]
legal_desc13 Legal Description 1. [Query with v=5 or greater to see in output.)
last_updated YYYY-QQ Year and quarter the data was last updated

Land Parcels FAQ

Where does your county data come from?

We source our data directly from each county or whom they designate as the official source for their parcel data.

How do you standardize county data generally?

The main way we make county data much easier to work with is by standardizing the column names of the raw data provided county. We do not standardize the values in most columns, we keep those exactly as provided by the county. We do, however, make sure that every county in our system is converted to a standard table schema, with consistent column names across the nationwide dataset.

How do you deliver bulk data?

All bulk data is provided via SFTP as zip files of each county in the format of your choice using a pull model. We organize data on a county by county basis using the county's FIPS code.

How do I download your parcel data?

We use the "Secure File Transfer Protocol", also called SFTP. This is supported by most traditional FTP clients and SSH client software.

When was your data last updated?

On average 94% of our parcels have been refreshed in the last 12 months, with most of those in the last 6 months. All data is tracked with the date of "last_updated" from the county.

What software can I use to work with your data?

Editing or working with most of our data requires software for working with geographic and geospatial data. There is free and open source desktop software to work this kind of data called QGIS.

What about Google Earth?

We provide KML/KMZ options for Google Earth and Google Earth Pro, but neither of those applications support editing our data, only viewing the data. If you need to make changes to the data you get from us, you will need a desktop application like QGIS discussed above.

How large is the nationwide dataset?

The nationwide dataset is approximately 400-800 GB uncompressed, varying by file format, storage method, attribute tier, and other factors.

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