Build Custom Affordable Housing Automations for Property Managers
You build custom automations for RealPage using its API and a separate backend system. This system handles application intake, income calculation, and waitlist sorting without manual data entry.
Syntora approaches custom RealPage automation by engineering backend systems that integrate with the RealPage API for application intake, document parsing, and waitlist management. These systems leverage technologies like Claude API for document processing, FastAPI for income calculation, and AWS Lambda for scalable deployment.
The scope of such a system depends on the complexity of your properties and income sources. A custom automation for a single LIHTC property processing standard pay stubs would typically be a straightforward engagement. A portfolio with layered HOME and HUD funding that accepts non-traditional income documents requires more complex parsing and validation logic, increasing the scope.
What Problem Does This Solve?
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.
How Would Syntora Approach This?
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.
What Are the Key Benefits?
Application to Sorted Waitlist in 90 Seconds
The system processes an entire application file, including income verification across multiple documents, in under 90 seconds. Your team acts on qualified leads immediately.
Eliminate 40+ Hours of Weekly Manual Work
Stop paying leasing agents to be data entry clerks. Reclaim a full-time employee's worth of work at lease-up and redirect them to resident-facing services.
You Get the Full Python Source Code
We deliver the complete codebase in a private GitHub repository. You are not locked into a proprietary platform and can have any developer extend the system.
Alerts for API Changes, Not Guesswork
We monitor RealPage API endpoints and our Claude API parsing. If an upstream change causes a failure, your designated contact gets a Slack alert within 5 minutes.
Writes Data Natively into RealPage Fields
The system updates applicant records directly via the RealPage API. Your team works from a single source of truth without ever leaving their primary software.
What Does the Process Look Like?
API Access & Workflow Mapping (Week 1)
You provide read/write API credentials for your RealPage instance. We map your exact income calculation and waitlist sorting rules into a technical specification document.
Core Engine Build & Document Training (Weeks 2-3)
We build the FastAPI service and train the Claude model on your sample income documents. You receive a link to a staging environment to test calculations.
RealPage Integration & Deployment (Week 4)
We connect the system to your live RealPage account and deploy it to AWS Lambda. The automation goes live, processing the first batch of 50-100 real applications.
Monitoring & Handoff (Weeks 5-8)
We monitor system performance for 30 days post-launch, tuning for accuracy. You receive a runbook, full documentation, and the private GitHub repository.
Frequently Asked Questions
- How much does a custom RealPage automation cost?
- Pricing depends on the number of unique property types (LIHTC, HOME, HUD) and the complexity of income documents we need to parse. A standard build for a single LIHTC property takes about four weeks. Projects with multiple layered funding sources require more complex logic and model training, which affects the timeline and final cost. We provide a fixed-price quote after the discovery call.
- What happens if RealPage is down or an application fails to process?
- The system is built with retries and a dead-letter queue. If a RealPage API call fails, the process will retry 3 times with exponential backoff. If it still fails, or if an income document is unreadable, the application is flagged in a 'manual review' queue. Your leasing manager gets a single daily email digest listing these exceptions, so nothing gets lost and the main workflow is never blocked.
- How is this different from using RealPage's AI Screening?
- RealPage's AI Screening focuses on credit, criminal, and eviction history for market-rate properties. It does not perform the complex, forward-looking income calculations required for affordable housing compliance. Our system is built specifically to automate the LIHTC and HUD income verification process, calculating anticipated 12-month income from variable sources and sorting applicants into the correct AMI buckets, a step RealPage does not automate.
- How do you handle sensitive applicant data?
- We never store raw documents or PII permanently. The system processes documents in-memory on AWS Lambda. Data exists temporarily in our Supabase database during calculation and is then pushed to RealPage. After 72 hours, the temporary records are automatically purged. RealPage remains the permanent system of record. We provide a full data processing agreement outlining these controls.
- What if the AMI income limits change next year?
- The AMI limits are stored in a simple table in the Supabase database. Updating them is a 5-minute task that requires no code changes. We include instructions for this in the handoff runbook. We can also build a small admin interface for your team to update these values themselves without needing to contact us. This is a common request and is included in most project scopes.
- Does this also work with AppFolio?
- Yes. The core income calculation engine is platform-agnostic. The only part that changes is the integration layer that communicates with the property management software's API. We have built identical systems for operators using AppFolio. The process and timeline are nearly the same; we just target the AppFolio API endpoints instead of RealPage's. We can discuss your specific platform during the call.
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