Automate LIHTC Compliance with Custom RealPage Integrations

Property management teams often rely on RealPage's built-in tools, which are designed for one-at-a-time file review, not bulk processing. During a lease-up, these tools cannot automatically calculate anticipated income from varied sources like tips, commissions, and multiple hourly jobs. Every application with non-standard income gets flagged for manual review, recreating the exact bottleneck you need to eliminate.

The default alternative is exporting applications to spreadsheets for manual processing. Imagine a 550-unit property receiving 1,200 applications in one week. A leasing agent manually enters pay stub data into Excel, using the `=SUM(HOURS*WAGE*52)` formula. They miscalculate an applicant with two part-time jobs, placing them in the 60% AMI bucket when they belong in the 50% AMI group. That qualified applicant is never contacted for a unit they were eligible for. This error happens for over 15% of manually processed files, leading to lost leases and compliance risk.

This manual approach is fundamentally broken for high-velocity lease-ups. It introduces data entry errors, creates massive delays in applicant communication, and burns dozens of hours of skilled labor on low-value sorting tasks. It does not scale past the first hundred applications.

Syntora approaches LIHTC compliance automation by first conducting a discovery phase to understand your specific RealPage configuration and layered funding requirements. This initial step would involve auditing your existing workflows and data points.

The technical architecture would typically involve a Python service, deployed on AWS Lambda for scalability and cost efficiency, configured to poll your RealPage instance using official API endpoints. Upon detection of new applications, the service would fetch associated income documents, such as pay stubs and offer letters, and prepare them for processing.

For parsing unstructured data from these documents, we would integrate the Claude API. Syntora has experience building document processing pipelines using Claude API for financial documents, and the same pattern applies to LIHTC compliance documents. The Claude API excels at extracting specific data points like hourly wages, salaries, tips, bonuses, and commission structures from various document types, including PDFs and image files. A custom Python script would then apply the precise LIHTC rules to calculate the anticipated 12-month income, for example, by annualizing wages and other income sources.

The calculated income would then be checked against your property's specific AMI tables. We would configure and manage these tables within a Supabase database. The system would then assign the applicant to the appropriate AMI bucket (e.g., 30%, 50%, 60%) and write this classification back to a custom field in RealPage via another API call. This process is designed to automatically sort your waitlist, allowing your leasing team to prioritize applicants from the top of the correct list.

As part of the engagement, the delivered system could also include features like triggering automated emails to applicants confirming projected eligibility. We would implement `structlog` for detailed, structured logging to aid in monitoring and debugging. An alert system, potentially integrating with Slack, would be configured to notify your team of any API failures or documents that cannot be parsed with a high degree of confidence, enabling prompt intervention. Typical build timelines for an integration of this complexity range from 8 to 16 weeks, depending on the scope of layered funding. The client would need to provide access to their RealPage API and relevant AMI tables. Deliverables would include the deployed and tested system, source code, and comprehensive documentation.