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.
Why this matters
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.
Here are some FAQs to get you started.
How is the rent confidence score calculated?
The confidence score returns a value ranging from 0-1 and takes several factors into consideration, including:
- Indicators in the transaction that signify rent (eg. cadence, txn description, amount, etc.)
- Analytics such as rent-to-income ratio
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.
Documentation
You can find the documentation here – https://docs.pave.dev/insights/rent/
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