We’re excited to announce the release of our Rent endpoint! This release follows a growing interest in helping our customers detect rent payments for cashflow forecasting, credit building, and underwriting.
Rent payments can be difficult to detect, especially when the data isn’t coming from a direct integration with a rental payment portal. The Rent endpoint surfaces all of a user’s rent payments, along with a confidence score indicating the likelihood that the transaction is rent. This endpoint surfaces all of a user’s rent payments across multiple payment types, including Zelle and Venmo transfers, check payments, money orders, and more.
How is the rent confidence score calculated?
The confidence score returns a value ranging from 0-1 and takes several factors into consideration, including:
Can you detect rent for people who split rent with a roommate?
Yes. If a user splits rent and pays via Zelle or Venmo, we still detect these rent payments.
Do you surface when a user stopped paying rent or skipped a payment?
We group rent transactions into sets based on similar payments, so you will easily be able to understand if a user has missing or stopped payments within a recurring set of rent transactions.
Please note: While the Rent endpoint will surface ALL transactions that our models detect as rent, only the transactions with a high confidence score are surfaced in the recurring_expenditures endpoint.
You can find the documentation here – https://docs.pave.dev/insights/rent/