A dataset can have strong parcel-boundary coverage across a county, state or nationwide footprint while individual fields vary considerably.

For example, one jurisdiction may provide owner names, mailing addresses and detailed land-use information consistently.

Another may provide fewer attributes.

So a headline claim about geographic coverage does not tell you how complete every field will be.

When evaluating a provider, look at both:

One of the biggest reasons is local control.

Local authorities maintain property information according to their own administrative needs and priorities. Different jurisdictions may choose to collect different attributes, maintain them in different ways or make different information available publicly.

Local and state law can also affect coverage.

In some places, authorities may not be permitted to collect or disseminate certain information. It is also becoming more common for jurisdictions and states to require particular information to be redacted from public records.

That means a missing attribute may reflect:

• • • that the local source does not collect it;
    • that the local source does not maintain it consistently;
    • that the information is not publicly available; or
    • that disclosure is restricted by local or state law.

These causes can look identical in a coverage percentage — the field is simply unpopulated — but they have very different implications for how a buyer should interpret the gap.

This is one of the most important points for buyers to understand.

A field can have a low fill rate because the authoritative source does not provide that information broadly.

In that case, a lower fill rate may accurately reflect the available public record.

So fill rate should not be treated as a standalone quality score.

Instead, ask:

• • • Why is the field missing?
    • Is the limitation source-driven or provider-driven?
    • Is the gap concentrated in certain jurisdictions?
    • Does the gap affect the geographies or workflows that matter to us?
    • Is there another field or dataset better suited to the task?

A high fill rate can be useful.

But without understanding how the field was sourced and what the value represents, the percentage alone does not tell you whether the data is appropriate for your use case.

The reverse can also be true. A high fill rate may look reassuring while still overstating how much useful information is present if a source uses sentinel values or other placeholder values instead of leaving fields blank. Coverage percentages therefore need to be interpreted alongside the meaning of the values themselves.

Most buyers do not need every available parcel attribute.

They need a smaller set of fields to work reliably across the places they care about.

Start by identifying those critical attributes.

Then ask:

• • • Is the field available in the counties or states we need?
    • What is the fill rate in those geographies?
    • Are gaps concentrated in particular places?
    • Do local collection or disclosure rules explain those gaps?
    • Would missing values materially affect the decision or workflow?

This makes coverage a practical fit test: do the fields you rely on have enough usable coverage in the places your workflow actually operates?

Nationwide averages can hide local differences.

If your workflow depends on a particular set of counties, states or markets, evaluate coverage in those places specifically.

Regrid publishes field-level coverage information at the county level so users can see how individual attributes are populated by geography rather than relying only on a nationwide claim.

That makes it possible to compare the fields that matter against the locations that matter.

Regrid distinguishes between parcel coverage and field-level coverage. Its coverage resources let you inspect individual attributes at the county level, so you can compare the fields your workflow depends on against the specific geographies you need to support.

This makes coverage differences visible rather than hiding them behind a nationwide average, giving buyers a clearer basis for deciding whether the available data is sufficient for their use case.

🔗 Related: How to evaluate parcel data quality: A buyer’s guide