Automate Debt Sizing and Loan Analysis for Your Retail Properties

Manual debt sizing for retail properties creates a bottleneck that costs deals and profits. Each retail asset requires analyzing dozens of variables - from small tenant credit risk to anchor store lease terms, CAM reconciliation impacts, and percentage rent fluctuations. You're spending hours building models that account for tenant mix stability, seasonal revenue patterns, and occupancy assumptions specific to retail formats. Comparing multiple lender quotes becomes overwhelming when each has different LTV limits, DSCR requirements, and debt yield thresholds. Without automated sensitivity analysis, you miss optimal leverage points and can't quickly model rate change impacts. Inconsistent underwriting assumptions across team members lead to financing gaps discovered too late in due diligence. The manual process prevents you from rapidly evaluating multiple scenarios, comparing construction loans versus permanent financing, or stress-testing deals against retail market volatility. While you're buried in spreadsheets calculating debt service coverage ratios and adjusting for retail-specific risks, other buyers with automated debt sizing tools are submitting offers faster and more confidently.

Syntora would design and build a custom AI-powered debt sizing system tailored to the unique complexities of retail property loan analysis. Our initial engagement would begin with a comprehensive discovery phase, auditing existing manual processes and identifying critical data inputs such as tenant credit scores, percentage rent history, CAM recovery rates, and lease rollover schedules. We would then propose a technical architecture leveraging modern AI and cloud services to ingest and process these documents and data points.

The custom solution would be engineered to automatically calculate optimal debt amounts based on LTV, DSCR, and debt yield constraints, while inherently accounting for retail property nuances like seasonal cash flow variations, tenant category performance, and anchor tenant dependencies. We'd leverage a framework like FastAPI for a robust API, integrating with a powerful language model such as Claude API for accurate parsing of lease agreements and financial documents, as we've successfully done for financial documents in adjacent domains. Data management would be handled by a scalable solution like Supabase, with compute-intensive tasks for scenario modeling and sensitivity analysis orchestrated using serverless functions on AWS Lambda.

The delivered system would expose a user interface or API endpoints allowing for real-time scenario modeling, evaluating various financing options (e.g., bridge loans, construction, permanent debt) with retail market assumptions. This approach provides a precise, auditable solution built specifically for the client's operational needs, typically requiring 12-20 weeks for initial deployment depending on complexity. Clients would need to provide access to relevant data, domain expertise, and an internal project lead. The deliverables would include a fully deployed, custom-engineered debt sizing system, comprehensive documentation, and knowledge transfer to the client's team.