Find More Accurate CRE Comps in Seconds, Not Hours

Brokerages rely on platforms like CoStar and LoopNet for market data. These tools are powerful databases but poor workflow engines. An agent searching for industrial comps still manually sifts through listings, copy-pasting lease rates, tenant names, and building specs into a spreadsheet. The process involves juggling multiple browser tabs, PDFs, and internal records, taking hours of a broker's time for a single report.

For example, a broker needs comps for a 50,000 sq ft warehouse with a 32-foot clear height. A keyword search in CoStar might miss a perfect comp because the listing agent wrote “clearance: 32'” in the unstructured property notes instead of entering it in the structured field. The broker must manually read dozens of listings to catch these variations. After finding potential comps, they then have to cross-reference them with the firm's own deal history, which is often trapped in a separate, non-searchable spreadsheet.

The structural problem is that these platforms are designed for data access, not data synthesis. Their architecture prevents deep integration with your proprietary deal history. You cannot run a single query across CoStar's market data and your firm's private lease details. This data silo forces brokers into tedious, error-prone manual work that directly limits the number of clients they can effectively service.

The first step is a data audit. Syntora would map every data source you use: CoStar exports, internal deal spreadsheets, public records, and any other subscriptions. We would identify the critical fields for your comp reports and create a unified schema to hold all this information. This audit provides a clear plan for what data to pull and how to structure it before any code is written.

The core of the system would be a custom data pipeline written in Python, running on a schedule to keep the data fresh. This pipeline feeds a central Supabase (PostgreSQL) database. We would use the Claude API to read unstructured text from property descriptions and transaction notes, extracting key attributes like door types, power specs, and concession details. A FastAPI service would expose a secure API endpoint, allowing your team to query the entire database using natural language. For example, a query could be “comps for 20-30k sf industrial leases in the O'Hare submarket in the last 18 months with ESFR sprinklers”.

The final deliverable is a simple web interface for your team. A broker enters their query and receives a list of ranked comps in seconds. They can select the most relevant ones, and the system generates a formatted PDF or Word report automatically. The entire system would run on AWS Lambda and Supabase, with typical hosting costs under $200 per month. You get a production-grade asset, not just a script.