Automate NOI Calculations and Pro Forma Projections for Senior Housing Properties

Manual NOI calculations for senior housing properties create a cascade of problems that impact every deal. Operators struggle to reconcile T-12 statements with resident census data, especially when occupancy fluctuates between independent living, assisted living, and memory care units. Medicare and Medicaid reimbursement changes create revenue recognition complexities that spreadsheets can't handle consistently. Non-recurring expenses like facility upgrades, healthcare licensing fees, and emergency maintenance often get misclassified, skewing stabilized NOI projections. Without standardized pro forma assumptions for senior housing, teams waste time debating growth rates for different care levels while deals stagnate. The lack of trailing versus stabilized NOI comparison makes it nearly impossible to present confident projections to investors. These manual processes typically take 15-25 hours per property and still produce inconsistencies that force last-minute deal restructuring or, worse, post-closing surprises that impact returns.

Syntora approaches NOI calculation automation for senior housing by first conducting a detailed discovery phase. We'd audit existing data sources—T-12 statements, rent rolls, and census data—to define specific parsing requirements and reporting needs.

The proposed system architecture centers on an intelligent document processing pipeline. We apply patterns from our experience building Claude API-based pipelines for financial documents in other sectors. Unstructured data from T-12s and other sources would be ingested and parsed by an LLM (e.g., using Claude API) to extract and structure key financial line items and categorizations. This structured data would then be stored in a scalable database, such as Supabase for rapid iteration or a cloud-managed relational database.

A custom backend application, developed with frameworks like FastAPI, would contain the core business logic. This would involve algorithms to reconcile occupancy, correctly categorize the multitude of senior housing revenue streams (e.g., Medicare/Medicaid, ancillary services, care-level income), and identify non-recurring expenses for appropriate adjustments. The system would also support client-defined pro forma projection assumptions for variables like occupancy ramp-up and care acuity shifts.

The delivered system would provide standardized NOI calculations, enable side-by-side trailing and stabilized comparisons, and offer sensitivity analysis through an API for integration into existing underwriting platforms or a dedicated reporting interface.

An engagement to develop and deploy such a custom system typically spans 12-20 weeks, dependent on data complexity and integration scope. Clients would need to provide example datasets, detailed accounting rules, and their specific pro forma projection models. Key deliverables include a fully deployed, tested system, source code, and architectural documentation.