Automate Market Research for 50+ Properties Monthly

Most small CRE teams rely on a combination of CoStar and manual spreadsheets. CoStar provides excellent listed property data, but it is a closed ecosystem. You cannot easily merge its data with your own off-market deals or information from county records. The platform's analytical tools are generic and cannot be customized for your firm's specific valuation model, forcing you back into Excel.

In practice, this means an analyst spends hours toggling between CoStar, LoopNet, public records, and a folder of PDF offering memorandums. For each of the 50+ properties, they manually copy lease rates, cap rates, and building specs into a master Excel file. This process is slow and introduces a data entry error rate of up to 15%. A single misplaced decimal in an NOI field can invalidate an entire comp set, and there is no automated way to catch it.

Some teams try using general data scraping tools, but these often fail. A scraper built by a freelancer might work for a few weeks, but it will break the moment a target website changes its layout. These tools also lack the context to parse unstructured CRE documents. They cannot reliably extract a tenant's name, lease expiration date, and specific NNN lease terms from a 40-page PDF offering memorandum. The data they produce is often messy and requires hours of manual cleaning.

The structural problem is that off-the-shelf tools are not designed to unify disparate data sources. CRE platforms want to keep you inside their system, and generic scrapers do not understand the industry's specific data formats. Without a custom pipeline, your team is stuck with costly manual work that creates a permanent ceiling on how many properties you can accurately track.

The first step would be a data source audit. Syntora would map every website, portal, and document type your team uses for market research. We would analyze sample offering memorandums and identify the key data points needed for your comp reports. This audit results in a concrete data schema and a technical plan that you approve before any build work starts.

The technical approach would use a series of Python scripts running on AWS Lambda. These scripts would perform scheduled data collection from public APIs and use browser automation for sites that lack them. For unstructured PDF documents, the system would use the Claude API's advanced document Q&A capabilities to extract specific fields like tenant names, lease terms, and operating income. All extracted data is cleaned, validated, and stored in a Supabase PostgreSQL database with a rigid schema to ensure data integrity.

The delivered system is a central, searchable database with a simple web interface built on Vercel. Your team could instantly filter properties by submarket, building class, or lease type and export a clean CSV for a comp report in under 60 seconds. The system is designed to feed your existing valuation models, not replace them. You receive the full source code, database schema, and a runbook for maintenance.