Automate Debt Sizing and Loan Analysis for Mixed-Use Commercial Properties

Manual debt sizing for mixed-use properties is a nightmare of complexity and inefficiency. Underwriters must separately analyze retail, office, and residential cash flows while accounting for shared common area expenses, parking allocations, and varying lease escalations across different tenant types. Each loan quote requires hours of spreadsheet modeling to determine optimal LTV ratios, calculate blended DSCR across diverse income streams, and analyze debt yield constraints for properties with multiple valuation approaches. The process becomes even more cumbersome when comparing multiple lender quotes, as each institution may weight different property components differently or apply varying underwriting standards to mixed-use assets. Sensitivity analysis on interest rate changes or vacancy assumptions requires rebuilding models from scratch, often leading to missed opportunities or suboptimal financing decisions. These manual processes not only consume valuable time but also introduce inconsistencies in underwriting assumptions, making it difficult to confidently present financing options to investors or make quick decisions in competitive acquisition scenarios.

Syntora's engagement for mixed-use debt sizing and loan analysis begins with a detailed discovery phase to understand the client's specific underwriting criteria, data sources, and desired analysis outputs. We would audit existing rent rolls, lease agreements, and financial statements to define the architecture for data ingestion and processing.

The core system would be engineered to automate the extraction and interpretation of critical financial data from various mixed-use property documents. We have built robust document processing pipelines using Claude API for financial documents in adjacent domains, and this same pattern applies to mixed-use property leases and rent rolls. Claude API would parse and categorize line items from complex documents, separating retail, office, and residential components, and accurately allocating shared expenses.

A custom backend service, likely built with FastAPI, would manage data flows, apply defined debt sizing methodologies, and integrate business logic. This service would expose APIs for data input, triggering analysis, and retrieving results. Financial models would be implemented to calculate optimal leverage across various property types, adhering to specified LTV, DSCR, and debt yield constraints for individual components and the blended property. Data storage would typically leverage a scalable solution like Supabase for structured data and S3 for document storage.

The delivered system would include a user interface for uploading documents, configuring analysis parameters, and visualizing loan comparison reports. It would be designed to run sensitivity analyses, allowing users to quickly assess how changes in interest rates, vacancy assumptions, or rental growth impact debt capacity across each property component. The final deliverable is a production-ready, custom-built system deployed to a cloud environment like AWS, tailored to the client's infrastructure and specific underwriting workflow. Clients would need to provide access to historical data, sample documents, and clear definitions of their underwriting rules and reporting requirements throughout the project. Typical build timelines for a system of this complexity range from 12 to 20 weeks, depending on data availability and feature scope.