Parsing Property Owner Names in pandas: Individuals, Joint Owners, Trusts and LLCs

Property owner fields in county assessor records often combine several people and entities in one cell, which complicates contact-finding work. Co-owner markers such as ETUX and ETAL, trustee designations, business suffixes, and estate wording all need to be recognized before a list is used for skip tracing. A tutorial shows how to build a pandas pipeline that classifies each owner as an individual or an entity, splits joint owners into separate rows, and parses LAST FIRST MIDDLE names while flagging ambiguous cases instead of guessing. The approach keeps the raw column unchanged and adds a parse_status column, with tests written as plain assert statements. Because naming conventions vary by county and over time, the author recommends sampling each export before relying on any keyword rule.