Build Custom Affordable Housing Automations for Property Managers

Most leasing teams try to use RealPage's built-in tools for waitlist management. While useful for market-rate properties, they cannot automate the compliance-specific calculations for affordable housing. The software does not automatically anticipate 12-month income from variable hourly wages, tips, or commissions. A leasing agent must still manually calculate `(hours x 2080)` in a spreadsheet, then find the right AMI chart to place the applicant.

A typical failure scenario involves a 400-unit property with mixed LIHTC and HOME units. The team receives 100 applications per week through the online portal. A leasing coordinator spends their entire Monday and Tuesday opening PDFs, transcribing pay stub data into a calculator, and manually tagging each applicant in RealPage. This takes 15-20 minutes per application, totaling over 30 hours of error-prone work. One typo can place an applicant in the wrong AMI bucket, leaving a unit vacant for weeks.

Off-the-shelf connectors and general workflow tools cannot solve this. They can move a file from point A to point B, but they lack the domain-specific logic to parse a W-2, understand pre-tax deductions, and apply the correct income rules for a HUD-funded unit. These tools fail when confronted with stateful, compliance-driven workflows that require more than simple if/then branching.

Syntora's approach to building custom RealPage automations begins with understanding your specific operational workflows and data requirements. We would architect a robust, scalable system designed for real-time application processing.

The core of the system would connect to your RealPage instance using its API to ingest new applications and all attached documents as they become available. For parsing income documents like pay stubs, bank statements, and offer letters, the system would leverage the Claude API. We have built document processing pipelines using Claude API for financial documents, successfully extracting key figures like pay rate, hours worked, and pay period with high accuracy, and the same pattern applies to real estate application documents. The extracted data is then staged in a Supabase Postgres database.

A Python service built with the FastAPI framework would serve as the income anticipation engine. This service would project the next 12 months of income from all sources, designed to correctly handle variable hourly work, seasonal bonuses, and commission structures specific to your criteria.

Following income calculation, the FastAPI service would query a Supabase table containing the latest LIHTC and HUD income limits for your specific county. It would then programmatically sort the applicant into the correct AMI bucket (30%, 40%, 50%, 60%, 70%, or 80%). The system would subsequently write this processed data back to RealPage via its API, tagging the applicant record and adding them to the correct, pre-sorted waitlist. This would provide your leasing team with fully qualified and sorted lists without manual intervention.

The entire system would be deployed on AWS Lambda, enabling event-driven processing of applications as they arrive and scaling efficiently to handle high volumes, such as during a lease-up period. Syntora would configure structured logging with structlog, sending all operational data to AWS CloudWatch for monitoring. A simple Vercel-hosted dashboard would provide your team with visibility into processing volume and overall system health.