Methodology
How PropNext Intel calculates and presents data
PropNext Intel groups public records into consumer-friendly views such as leaderboards, activity snapshots, movement timelines, and profile summaries.
How we calculate this
Leaderboards rank agents by transaction volume within the selected year and optional filter set.
Profile pages combine registration information, contact details when available, recent transaction activity, property mix, and movement context.
Movement pages group records such as agency transfers, new registrations, and related changes into a clear timeline view.
Guides are written as answer-first summaries so consumers can understand what the metrics mean before they compare agents.
What this means
A high transaction count can indicate experience, but it should be read together with recency, property type, and role mix. The platform is designed to help consumers compare agents on relevant context, not one headline number alone.
Source transparency
PropNext Intel mixes official public records, data.gov ingestion, and structured scraping. The goal is to make the source context visible rather than hide it.
View data sources