People data, United States
Property-to-Person Identity Resolution
Resolve an address, a name, a phone or an email to a person and the property they own, anchored to the most stable identifier there is: the parcel.
Most identity graphs are built on digital exhaust. Ours is anchored to the physical world: a parcel, a deed and a person's name on it. Give us any fragment, an address, a name plus city, a phone or an email, and we return the resolved person, their current and prior addresses, the properties they own and their verified contact points.
It runs as an API for real-time use and as batch for files of millions.
Sample rows
Illustrative and synthetic. Real samples for your geography are a reply away.
| input | resolved_name | current_address | owns | prior_addresses | mobile | confidence | |
|---|---|---|---|---|---|---|---|
| (615) •••-••33 | A••••• N••• | 5•• Maple Ridge Dr, Franklin TN | 1 property | Chicago IL (2019-2026) | (615) •••-••33 | a•••••@outlook.com | 0.94 |
| •••• W Oakwood Ave, Chicago IL | K••••• W••••• | same | 3 properties | (•••) •••-••04 | k•••@gmail.com | 0.91 |
Schema
| Field | Type | Notes |
|---|---|---|
| input | text | non-personal |
| resolved_name | text | personal data |
| current_address | currency | personal data |
| owns | boolean | non-personal |
| prior_addresses | text | personal data |
| mobile | phone | personal data |
| personal data | ||
| confidence | number | non-personal |
Abridged. The full data dictionary ships with every sample and lists every field, its fill rate and its source.
Used for
- CRM deduplication and enrichment
- Lead scoring with property context
- KYC and fraud checks
- Cross-device audience matching
How it is built
A union of our consumer, telco, property and professional graphs with permissive matching where the address disambiguates and strict matching where it does not. Confidence is reported on every response.
Questions we get
Do you match on hashed emails?
Yes, SHA-256 email and phone hashes are accepted inputs.