As of its June 24, 2026 refresh, Regrid’s data store lists 67,315 parcels for Norfolk city, Virginia. Our Norfolk layer contains 74,425. The 7,110-record gap represents nearly one record in ten, and the properties involved carry just over $2 billion in assessed value. We want to find out why.
Note: We do not have access to any of Regrid's underlying parcel data, and instead used their public coverage statistics to evaluate.
One polygon, thirty-seven owners
Take 401 College Place, a condominium in downtown Norfolk with 28 residential units and 9 deeded boat slips at the adjoining 255 College Crescent marina. Look it up on the city’s own AIR property site and you’ll find 37 tax accounts — 37 owners, 37 assessments, $11.96 million of real estate.
On the city’s lot map, though, all of that is one polygon (GPIN 1427766699). And a parcel dataset built from the lot map ships one record for it: the polygon, tagged with whichever unit’s tax account the city happened to list first. In this case that’s unit 1 — a single owner and a $412,800 assessment standing in for the entire building. That record isn’t just incomplete; it’s misleading. It names one household as the face of a 37-owner property and shows 3.5 cents of every dollar actually on the ground.
Why mapped-lot datasets have this hole
Norfolk, like many jurisdictions, publishes its cadastre as two products on the same GIS service: a mapped-lot polygon layer (67,405 polygons — one per lot, condos collapsed) and the CAMA assessment roll (72,505 distinct tax accounts — every condo unit, boat slip, and condo garage, each linked back to its polygon by a foreign key). The polygons are the easy part: they download as ready-made geometry. The units live in a keyed reference table that has to be joined, reconciled, and stacked.
Regrid’s Norfolk count of 67,315 sits within a hundred records of the polygon layer’s 67,405 — the signature of a dataset built from the lot map alone. What that leaves out, in one city: more than 5,000 condominium accounts across 575 polygons, from 1,901 units in small 2–4 unit buildings to hundreds in 51+ unit towers, plus 108 commercial condos, 75 condo boat slips, and even 16 condo garages. Counting only tax accounts that exist nowhere in the polygon layer, that’s 5,097 properties worth $2.07 billion that a mapped-lot dataset cannot show you.
| Norfolk city, VA | Regrid (refreshed 2026-06-24) | landrecords.us |
|---|---|---|
| Parcel records | 67,315 | 74,425 |
| The 401 College Place condominium | 1 record, 1 owner | 37 records, 37 owners |
| Assessed value visible at 401 College Place | $412,800 | $11,955,200 |
How we model it: parcel stacks
Our schema represents condominiums the way assessors do — as a stack. The polygon becomes the base record, keyed on the shared lot identifier (in Norfolk, the GPIN). Every other tax account on that polygon becomes a child record keyed on its own account number, with two linking fields from our published schema tying the family together: parentid on each child points at the base record’s parcelid, and all records in the stack — base and children alike — share one stackid. Children inherit the base polygon’s geometry, so every unit lands on the map, and each carries its own owner, mailing address, use code, and assessment. Here is the actual 401 College Place stack as it appears in our dataset:
The 401 College Place stack, drawn from our production records: the base record keys on the shared GPIN, each child keys on its own tax account with parentid pointing back to the base, and one stackid ties all 37 together.
Why our collection method finds every account
The gap comes down to what a pipeline treats as the unit of truth. If the unit of truth is a polygon, condos are invisible by construction. Ours is the tax account, and three habits of our pipeline follow from that:
- We acquire every layer, not just the geometry. When we ingest a county’s GIS service, we take the polygon layers and the keyed reference tables — in Norfolk, the CAMA roll with all 72,505 accounts — and our LLM-driven harmonization identifies the foreign key that links each account back to its polygon, on either side of the relationship.
- Merges must never lose an account. The step that consolidates sources into one county layer enforces a measurable invariant: consolidation may not reduce the number of surviving tax accounts. If any account fails to come through, the pipeline raises a warning naming the count — it cannot fail silently.
- We reconcile against the assessor, not against other vendors. Benchmarking coverage vendor-to-vendor is a trap: measured against a benchmark with the same blind spot, a dataset missing every condo in Norfolk still scores over 100%. We reconcile our published records against the county’s own roll, account by account. In Norfolk that leaves exactly 256 accounts unmapped — leaseholds and state land with no polygon anywhere in the city’s GIS — and we know each one by name.
None of this is hand-wired. The schema map below is drawn automatically from the harmonization views our pipeline wrote and stored in our catalog — the same way we draw it for every county. All three of Norfolk’s acquired datasets are there: the parcel polygons, the zoning layer, and — in the REFTABLE section — the CAMA assessment roll itself, where you can see the account roll’s routing_number (the GPIN that ties a condo unit to its building’s polygon) mapped onto parcelid and its parcel_id tax account mapped onto taxacctnum. Those two wires are the condominiums’ path into the dataset.
Norfolk’s complete schema map, regenerated August 2026 from the live harmonization views in our catalog: source columns on the left, harmonized entities in the middle sections, and the published parcel schema at the top. Scroll inside the figure — the REFTABLE section is where 5,000+ condominium accounts enter the dataset.
Norfolk is one city. The same two-layer publishing pattern — polygons in one place, the per-account roll in another — shows up all over the country, and the same account-level reconciliation catches it everywhere, from Wake County, North Carolina to Wasco County, Oregon. If your work touches condominiums at all — ownership outreach, valuation, title research, lending — a mapped-lot dataset isn’t showing you a smaller version of the market. It’s showing you a different one.
See how we compare on coverage, fields, and price on our home page, browse the US Nationwide Parcel Dataset, or email hello@landrecords.us — we’re happy to run this same comparison for any county you care about.