Generate Real-Time CRE Comp Reports with a Custom AI System

Most CRE firms rely on a combination of CoStar, Reonomy, and internal spreadsheets to build comp reports. While these platforms are powerful data sources, they are closed ecosystems. They do not talk to each other, and their value is locked behind a user interface that requires manual searches, clicks, and tedious copy-pasting to extract information for a single property.

Consider a typical workflow for a 15-person investment firm. An analyst needs comps for a Class B office building. They log into CoStar, run a search for comparable sales, and export a CSV with 50 columns. They then log into Reonomy to find ownership history, exporting another CSV. Finally, they open the firm’s 10,000-row master deal tracker in Excel to find similar properties the firm has underwritten. The next hour is spent manually merging these files, standardizing column names like “Sale Price” vs. “Price”, and formatting everything for a presentation. If a partner asks to add a new comp at the last minute, the manual process starts over.

The structural problem is that these data platforms are designed to be destinations, not integrations. They profit from keeping users logged into their interface. Their data export features are a concession, not a core function, which is why they lack the robust, queryable APIs needed for true automation. This leaves the final, most time-consuming step of data synthesis entirely on the analyst, creating a workflow bottleneck that is slow, expensive, and prone to human error.

The first step would be a data source audit. Syntora would map every source you currently use: CoStar, Reonomy, public records portals, and your internal databases, whether they are in SQL, Supabase, or a collection of Excel files. We would identify which sources provide structured exports versus which require custom parsing logic. You would receive a data flow diagram showing exactly how information would be pulled, cleaned, and unified.

The core of the system would be a Python-based data pipeline running on AWS Lambda, triggered on demand via a simple web interface. The pipeline would use pandas to process and transform structured data from CSV exports. For synthesizing narratives or extracting key terms from unstructured text like PDF property brochures, the pipeline would use the Claude API. All cleaned and unified data would be stored in a Supabase Postgres database, creating a new, centralized comp database that becomes a valuable asset for your firm.

The delivered system would present a simple interface, deployed on Vercel, where a broker enters a property address and filtering criteria. The system queries the unified Supabase database and generates a formatted report in under 90 seconds. You receive the full source code in your GitHub repository, a runbook for maintenance, and a system with hosting costs typically under $50 per month.