National Property Parcel Dataset with Ownership and Valuations

Find the Deal Before Anyone Else Does

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.

Isometric white geographic platform with blue parcel markers, routes, layered data surfaces, and floating abstract cards representing national parcel acquisition intelligence.
At a Glance

The Data Behind the Decision

Parcel, ownership, valuation, lien, and sales history
Five data layers, one record
Assessor, tax, deed, mortgage, and AVM
Five source types consolidated
Standardized identifiers across all U.S. jurisdictions
Consistent IDs, every market
Buy by Geography

Stop buying the whole country. Buy just your markets.

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.

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See It Working on Your Pipeline

The Problem

Why CRE Teams Miss Deals They Should Win

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.

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Fragmented Multi-County Data Pulls
Screening a single metro means logging into dozens of assessor portals, each with different formats and update cadences. Every hour spent normalizing that data is an hour your competitors are spending on outreach.
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Incomplete Ownership and Lien Visibility
Missing a junior lien position or a stale ownership record during screening does not just slow due diligence — it can kill a deal at the table or expose your firm to title risk that should have been caught in week one.
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AVM Gaps Forcing Manual Revaluations
Without a reliable AVM baseline at the parcel level, analysts spend days rebuilding valuations from sparse comp data. That is not analysis — that is data janitor work that belongs in a database, not a spreadsheet.
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No Spatial Context for Portfolio Decisions
Acquisition decisions made without proximity analysis, clustering context, or jurisdictional boundary data routinely miss concentration risk and market adjacency opportunities that are obvious the moment the data is mapped.
What Is In The Dataset

Every Data Point That Wins the Deal

Ownership and Mailing Address History

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.

Full Sales Transaction History

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.

Mortgage Lien Positions and Tax Detail

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.

AVM Estimates and Building Characteristics

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.

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The world's spatial data is more accessible than you think. Let's show you how close you already are.
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You already have the questions. We have the data. Let's see what happens when they meet.
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The where in your business is more important than you think. Let's find it together.
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Most spatial data conversations start with a problem nobody thought was solvable. What is yours?
BigGeo AI

Ask Any Acquisition Question in Plain Language, Get a Governed Answer

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.

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Show me all commercial parcels in Denver County with assessed value below AVM estimate and no sales activity in the last 5 years.
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Which zip codes in Phoenix have the highest density of absentee-owned industrial parcels under 50,000 square feet?
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Flag all parcels in our target corridor with junior lien positions recorded in the last 24 months and ownership held by an LLC.
FAQ

Frequently asked questions

How current is the ownership and sales history data, and how often is it refreshed?
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We already have some county assessor data. What does this dataset give us that we do not already have?
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How reliable are the AVM estimates, and how do they compare to what our analysts would build manually?
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How does this data get into our existing environment? Do we need GIS software or a data engineering team to use it?
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How do we get started if we only need coverage for a handful of target markets, not the entire country?
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