The National Property Parcel Dataset consolidates assessor, deed, mortgage, and AVM data into a single record for every parcel across U.S. jurisdictions — giving your acquisitions team a complete asset picture without the county-by-county data chase. Screen off-market targets, validate valuations, and surface lien risk in the time it used to take to pull one county file.

Most data vendors sell you a national license to millions of records you will never contact. BigGeo works differently.You tell us where you sell, and we cut you exactly that geographic slice of the dataset. A single city.
A cluster of ZIP codes. A metro area. A county. Whatever matches your territory. The result is a lean, CRM-ready file containing only the companies in the markets you actually work, delivered at a fraction of the cost of a full national license.

The commercial real estate market moves faster than the data infrastructure most acquisition teams are running on. County portals, fragmented assessor files, and manually assembled comp sets are not a competitive disadvantage — they are a deal-killer. By the time your team has stitched together ownership, valuation, and lien data for a single submarket, the off-market opportunity has already traded.
Current and historical ownership records with mailing addresses let your team identify absentee owners, track entity structures, and build direct outreach lists for off-market targeting — without a skip-trace vendor.

Every recorded sale on a parcel gives your analysts the comp depth to validate pricing assumptions, spot distressed trading patterns, and pressure-test seller expectations before you ever make an offer.

Multiple lien records per parcel, plus tax and exemption details, let you assess encumbrance risk and flag tax-delinquent assets during screening — not during escrow when it costs you time and money to unwind.

Automated valuation model estimates paired with structure attributes — size, year built, construction type, condition, room counts — give your team a first-pass underwriting baseline at portfolio scale, not just asset by asset.

BigGeo AI is live in ChatGPT today and available in Claude, giving your acquisitions team direct plain-language access to this parcel dataset without a GIS analyst, a data pull request, or a three-day turnaround. Ask which submarkets in a target metro have the highest concentration of absentee-owned commercial parcels with below-market assessed values, and get a governed, accurate answer grounded in real parcel records — not a training-data approximation. The underlying data never leaves the governed compute path, regardless of how your team accesses it.
Ownership, deed, and sales transaction records are sourced from county assessors and recorders on an ongoing basis, with update cadences varying by jurisdiction. Inside BigGeo's Strata layer, every incoming record is indexed to the DGGS grid the moment it arrives, which means your team is always querying the most recently delivered data without any manual refresh cycle on your end.
Piecemeal assessor files give you one county at a time, in formats that rarely match, with no lien data, no AVM, and no standardized spatial identifiers. This dataset consolidates assessor, tax, deed, mortgage, and AVM information into a single flat file with consistent parcel identifiers across every U.S. jurisdiction — so your team is running analysis, not data normalization. The difference is the difference between screening a metro in an afternoon versus a week.
The AVM estimates in this dataset are generated at the parcel level using recorded sales transactions, assessed values, and structural attributes — the same inputs a skilled analyst would use in a manual comp analysis. They are best used as a first-pass screening layer to prioritize assets for deeper underwriting, not as a substitute for full appraisal. At portfolio scale, that screening layer saves your team days of manual revaluation work per market cycle.
No GIS software required. Once purchased through BigGeo Marketplace, the dataset lives in your DataLab workspace where it can be queried directly, joined to other datasets, and visualized in DataScape without any custom engineering. If your team already has a data environment, delivery formats are available to match. And through BigGeo AI, analysts can start pulling answers in plain language the same day without writing a single query.
Request a sample through the form on this page and tell us your target markets. We will show you exactly what the dataset looks like for those geographies — coverage depth, attribute completeness, and a few example records — before you make any commitment. Most teams have enough to make a decision within 48 hours of the sample request.