Automate Your Commercial Real Estate Comp Reports with AI Agents

Most small CRE brokerages rely on CoStar and LoopNet for market data. While these platforms are data-rich, they are designed as closed ecosystems. You can search and view data, but extracting it in a structured format to merge with your own internal deal history is a manual process of copying and pasting values into an Excel or Google Sheets template.

Consider a 10-person brokerage preparing a comparable report for a 50,000 sq ft office property. An analyst spends two hours pulling 15 comps from CoStar, manually transcribing sale price, date, and cap rate into a spreadsheet. Then they spend another hour on three different county assessor websites to verify tax data and ownership history for each comp. This 3-4 hour process is repeated for every report, with a high risk of data entry errors.

The core problem is data fragmentation, not a lack of data. Critical information lives in three separate buckets: paid subscription services, unstructured public websites, and internal spreadsheets. A CRM can't scrape a county website, and CoStar's export functionality is intentionally limited to prevent you from building your own database. The business model of these data providers conflicts with the workflow needs of a small brokerage.

The result is that senior brokers spend time on low-value data entry instead of business development, or the firm hires junior analysts primarily for manual research tasks. The quality of comp reports becomes dependent on an individual’s attention to detail, not a systematic process. This manual bottleneck limits the number of proposals a firm can generate and introduces unnecessary operational risk.

Syntora's process would begin with a thorough audit of your current data sources. We would map out every platform you use, from paid subscriptions like CoStar to the specific county assessor websites you access. The goal is to define a single, unified schema for a 'comparable property' that incorporates all the fields you need for your final report.

The core of the system would be an AI agent orchestrated by a Python FastAPI service. This service would query API-enabled sources like your MLS using httpx for parallel processing. For websites without APIs, like public records portals, the system would use browser automation on AWS Lambda to mimic human navigation and extract data. For PDF offering memorandums, the Claude API would parse the text to pull specific fields like tenant names and lease expiration dates, a pattern we have successfully applied to complex financial documents.

The delivered system would store all structured data in a Supabase Postgres database you own, with a simple front-end hosted on Vercel for initiating searches. A typical search across 5 data sources that takes a human 3 hours would be completed in under 5 minutes. The system is designed to process over 1,000 property lookups per day, and hosting costs on AWS Lambda and Supabase are typically under $50/month at that scale. The final output would be a validated CSV or a direct write to a designated Google Sheet, eliminating manual entry.