Automate CRE Comp Reports and Market Research with a Custom AI System

Most CRE brokers live in CoStar, Buildout, and Reonomy. These platforms are essential for finding property data, but they are not built to talk to each other. A broker preparing a comparative market analysis (CMA) has to manually pull data from each system, one property at a time. The data formats are different, requiring tedious copy-pasting into an Excel sheet just to normalize cap rates, price per square foot, and lease terms.

Consider a 10-broker firm in Chicago trying to win a new listing. The broker needs to build a comp report for a 50,000 sq. ft. industrial property. They open 15 tabs: one for the subject property in Buildout, five for sales comps in CoStar, five for lease comps in Reonomy, and four for local market trends. They spend the next three hours copying addresses, sale prices, and tenant names into a branded Word template, manually reformatting each entry and checking for typos. This process happens for every single proposal.

The structural problem is that these platforms are closed data silos designed to keep users logged in. Their APIs exist but are often limited, expensive, or not designed for cross-platform data fusion. There is no "Export to Report" button that combines CoStar sales data with Reonomy ownership info and Buildout property specs. The platforms have no incentive to build this, as their business model relies on being the one-and-only source of truth.

The result is that your most valuable assets, your brokers, spend up to 25% of their time on low-value data entry instead of building relationships and closing deals. Each manual report introduces the risk of a copy-paste error that could undermine a multi-million dollar proposal. This is not a tooling problem that another subscription can fix; it is an integration problem that requires a dedicated engineering solution.

The first step is a discovery audit of your current data sources and report templates. Syntora would map every field you currently pull manually from CoStar, Buildout, and Reonomy APIs. We would analyze your existing client-ready reports to define the exact output schema. This audit produces a clear data flow diagram and a fixed-scope proposal, so you know precisely what will be built.

The technical approach would use a Python-based pipeline running on AWS Lambda. An API gateway built with FastAPI would accept a property address as input. The system then makes parallel, asynchronous calls using httpx to the APIs for CoStar and Reonomy, pulling all relevant comp data. For sources without a stable API, the system would use Playwright for browser automation. The Claude API would parse and normalize the extracted data, handling variations in field names and formats across sources. The normalized data is stored in a Supabase PostgreSQL database.

The delivered system is a simple web interface where a broker enters a property address. Within 10 minutes, the system generates a formatted report in your branded Word or PDF template and sends it via email. This process reduces a 3-hour manual task to a single click. The system logs every run, tracks API usage to stay within the 5,000 calls/month limit of a typical plan, and alerts if a source API changes. You receive the full source code and a runbook.