Automate Commercial Real Estate Market Research

The core of CRE market research is combining data from disparate sources, and no off-the-shelf tool does this well. A firm’s primary data subscription, like CoStar, provides excellent market data but its reporting tools are rigid. An analyst cannot easily merge CoStar’s sales comps with the firm’s proprietary lease data from an internal database or with zoning information scraped from a municipal portal. The data lives in silos.

This forces analysts into a painful manual workflow. For a single comp report, an analyst might log into CoStar, a county records portal, and an internal SQL database. They spend hours copying and pasting dozens of fields for multiple properties into a master Excel template. The process is slow, tedious, and a significant source of data entry errors. A last-minute update to a single comp can trigger an hour of rework to update summaries and averages.

Some firms attempt to use generic data scraping tools, but these are brittle. When a county clerk's office updates its website design, the scraper breaks without warning, silently feeding stale data into reports. These tools also fail to interpret nuance. They can extract text from a 20-page lease PDF but cannot identify the termination clause or rental escalation schedule. They provide raw text, not structured, actionable information.

The structural problem is that commercial real estate data is fragmented and lacks a universal identifier. A property might have one ID in CoStar, a different parcel number in public records, and a different internal ID. Off-the-shelf software cannot resolve these entities. A custom system is required to build a persistent, unified view of a property that links all these sources together.

The engagement would begin with a data audit. Syntora would map every data source you use for market research, from API-driven platforms like Reonomy to public-facing portals and internal databases. We would identify the common fields (like APN or address) that can be used to join records across these disparate systems and define the business logic for selecting and ranking comparable properties. This audit produces a clear data model and technical plan.

The system's core would be a set of Python functions running on AWS Lambda, designed for parallel data retrieval. For sources without a proper API, a browser automation component would be built to log in and retrieve data. The Claude 3 Sonnet API would parse unstructured text from sources like lease abstracts or offering memorandums, extracting key data points like rent rolls or expense ratios into a structured format. All collected data would be normalized and stored in a Supabase Postgres database, creating a unified data asset for your firm.

An analyst would interact with the system through a simple web interface. They would input a subject property address, and a FastAPI service would query the Supabase database to assemble a full comp report in under 3 minutes. The final output would be a pre-formatted Word document or PDF, matching your firm's existing branding and templates. You receive the full source code, deployment scripts, and a runbook detailing how to maintain the system.