Automate Comparable Sales Analysis for CRE Appraisals

Most appraisal firms rely on a combination of CoStar, Argus, and Excel. CoStar and Argus are excellent for storing and searching structured property data, but they cannot ingest and structure information from external, unstructured documents. If a broker emails a PDF offering memorandum for a new off-market comp, an analyst must manually read it and key the data into the system. There is no 'Upload PDF' button that intelligently parses the contents.

This forces analysts into Excel, the default tool for compiling comps. Consider an appraiser at a 15-person valuation firm tasked with valuing a multifamily property. They pull 20 comps from CoStar, then spend the next hour manually transferring data from three PDF OMs into their master spreadsheet. This 30-minute-per-document process is not just slow; it is a major source of errors. A single typo in a property's net operating income or sale price can fundamentally skew the entire valuation model.

The structural problem is the gap between structured databases and unstructured documents. Your most valuable, timely comp data often arrives in formats your primary software cannot understand. General-purpose AI document readers fail because they do not recognize CRE-specific terms like 'Loss to Lease' or 'Cap Rate on Trailing 12-Month NOI' without extensive customization. This leaves your highly-paid analysts acting as a manual bridge between systems, a low-value task that caps their productivity and introduces unacceptable risk.

The first step would be a data audit. Syntora would analyze a sample of 20-30 recent comp reports and their source documents like PDFs and broker emails. This audit identifies the key data points you rely on for valuation and maps the variations in how they are presented across different sources. You receive a precise data schema and a project plan before any code is written.

The core of the system would be a data processing pipeline built in Python. We would use the Claude API for its advanced reasoning capabilities to parse unstructured text and tables from documents, extracting the specific data points defined in the audit. A FastAPI service would expose a simple endpoint where you or an automated system could submit a new document. Extracted data would be structured and stored in a Supabase PostgreSQL database, creating a clean, queryable source of truth for all your comps.

The final deliverable is a private, secure system that fits your workflow. An analyst could drag a new comp PDF into a web interface and, in under 60 seconds, see the structured data in your central database. This system would also expose an API, allowing your existing Excel models or valuation software to pull standardized data directly. You receive all the source code, deployed on AWS Lambda for cost-effective, serverless operation that only incurs costs when processing documents.