What should you collect before skip tracing a property owner?

In PropertyRadar's import workflow, your row is matched to a property and then to a person, and each field does a different part of that work. Its import documentation names the inputs the matcher treats as high confidence: county and APN, a street address with a city or ZIP code, an exact phone or email, and a name paired with one of the first three.

County and APN come first. An APN is assigned by a county assessor, so it identifies a parcel only together with its county and state. A street address can carry the property match alone when you have no APN, provided it has the number and street plus a city or a ZIP code.

The owner name works differently. In PropertyRadar's matcher, a name counts toward a high-confidence match only in combination with an APN, an address, or a phone or email, since two people can share one. Its second job comes later. When a trace returns a person, the name and the mailing address in your file are what you compare that person against. The mailing address also gives you somewhere to send mail if no phone comes back.

PropertyRadar's matcher doesn't use ownership type. It separates the rows where the owner is a company or a trust, which need more work before a phone number is useful. A known phone or email is optional and matches on exact values only, so one mistyped digit defeats the match on that field.

Two more columns are worth keeping for your own records: where each row came from and the date you captured it. The capture date is not the age of the county's data, but it tells you how old your copy is.

The column names above are a working layout. PropertyRadar's importer has its own field names, and you map your columns to them during upload. The ownership values are the ones in its Owned By criterion.

Suppose one parcel in your file is a rental at 218 Birch Ln whose tax bill goes to a PO box in Reno. Its header row and data row would look like this:

county,state,apn,site_address,site_city,site_zip,owner_name,mail_address,mail_city,mail_state,mail_zip,ownership_type,known_phone,known_email,source,pulled_date
San Bernardino,CA,0123-456-78-0000,218 Birch Ln,Redlands,92373,DOE JANE R,PO Box 4410,Reno,NV,89501,Individual,,,County tax-defaulted list,2026-10-02

The site address and the mailing address sit in separate columns because they answer different questions. One locates the parcel and the other locates the owner, and on a rental those can be different places.

Most of the cleaning is about keeping the APN and the owner name exactly as the county wrote them.

Store the APN as text. A spreadsheet that treats 0123-456-78 as a number can drop the leading zero or the dashes, and the glossary says APN formatting, including dashes, has to match the county's. Some counties format an APN as book-page-lot, so don't force one pattern across counties.

Remove duplicates on state, county and APN, because one row per parcel is the unit you're matching. Owner name is the wrong key for this. One owner with six rentals is six parcels, and two different people can share a name.

Leave the owner name as the county wrote it, including the word order, the suffixes and entity endings such as LLC or TRUST. A tidied name is harder to compare with the recorded one when you review matches. Addresses are the exception. Split them into street, city, state and ZIP, since a street with no city or ZIP falls short of the importer's bar for a high-confidence address match.

Keep the state, county and APN on every row through the export. A returned phone number attaches to a person, and those three fields are what tie that person back to the parcel you started from.

Rows titled to an LLC or a trust are worth one more field when you can find it: a lead on who acts for the entity. It helps to work these rows on their own tab. The importer accepts company names, so the separation is a convenience that keeps the rows needing a named person in one place.

Where the lead comes from depends on the state where the entity was formed. Texas, for example, requires an LLC's certificate of formation to give the name and address of each initial manager, or of each initial member when the LLC has no managers. Add that name and address to the row as a person to ask. The filing lists the governing persons at formation. Their current role and their authority to sell are questions for the first conversation and for the entity's own documents.

PropertyRadar's importer accepts CSV, XLS or XLSX files, and imports are included with all current subscription plans at no additional cost. You start one from My Lists, under New List and then Import List. The Import window offers a sample file of its import fields to compare with your headers.

Two settings decide how strict the match is. The Match Threshold sets the line between records imported automatically and records held for manual review. A high threshold admits only close matches. A low one admits loose matches that the documentation says may need a manual accuracy check. For a list you're about to buy contact data on, set it high and review the remainder by hand. The second setting is the list type. Choose Property List for a file built from parcel records. With Customer List, the documentation says the matcher can find a person at a different address and still match them.

After you map your columns and submit, open the list and click Review Matches. You can filter by Confidence, by Property Status and by Person Status. The two statuses are separate checks. The first asks whether the row landed on the right parcel. The second asks whether the person attached to it is the owner in your file.

For a row in review, the magnifying-glass Match Lookup icon lets you look the parcel up by owner name and location or by site address and select the property. Under Choose Person Match, pick the person whose name and mailing address agree with your owner_name and mail columns. A matching name alone isn't enough. If no one agrees, choose "Do not import contact data" and keep the parcel without a person. Include these hand-reviewed rows in the sample when you test a skip-tracing provider's accuracy.

Phones and emails come after the match, and the purchase terms are the reason the file matters. PropertyRadar's contact purchase documentation lets you buy for every contact on a property or only the Primary Contact, and a purchase returns all the phones or all the emails available for that contact. A contact with nothing on file shows no purchase link or icon. Purchases are final and non-refundable, so a purchase on a row matched to the wrong parcel is charged like any other.

Suppose your file is 400 tax-delinquent parcels and all 400 match. Assume one phone purchase per owner, for the primary contact, counted as one against an allowance that hasn't been used this month. The Solo plan includes 250 phones a month and charges $0.08 for each one beyond that. The 400 purchases cost 150 times $0.08, or $12, on top of the subscription. If 30 of the 400 primary contacts have no phone on file, you buy 370, and the bill is 120 times $0.08, or $9.60. The Team plan includes 500 a month at $0.06 beyond that, and Business includes 2,500 at $0.04. Unused monthly amounts don't roll over. Look at the quantity in the purchase summary before you accept it, because choosing all contacts raises the count.

Each returned number is a candidate for the owner until a conversation confirms it. The profile lets you set a phone's status to Wrong Person or Disconnected as you learn more. Before dialing, screen every number. PropertyRadar's do-not-call criteria apply only to the first phone number on the Primary Contact, so a second number or a co-owner's number needs its own check. Screening a call list for do-not-call suppression covers the steps.