Automate Commercial Real Estate Comp Reports

Most CRE brokerages rely on an analyst to manually copy and paste data from multiple sources. The process starts with CoStar for property details, then moves to county records for tax history, and finally to internal spreadsheets for proprietary deal comps. This is slow, expensive, and prone to human error.

A common attempt to fix this involves hiring a freelancer to write a web scraper. The script works for a few weeks, but then CoStar updates its website, and the scraper breaks silently. The freelancer is gone, and the brokerage is back to manual work. The core issue is that these are not simple websites; they are complex web applications that require robust browser automation, not a fragile script.

Another approach is trying to use generic data tools. The problem is that these tools lack connectors for industry-specific platforms like CoStar and cannot parse the inconsistent formats of county assessor PDFs. They are built for structured data from standard APIs, not the fragmented and protected data sources that define commercial real estate research.

Syntora approaches custom AI automation by first conducting a detailed discovery to map every data source and interaction for a client's specific needs. For web-based platforms like CoStar, the strategy often involves using automation frameworks such as Playwright to manage browser sessions, securely handling logins and navigating to specific property records. For county records, dedicated parsers would be developed to handle their specific formats, whether these are searchable web portals or structured PDF uploads provided on a recurring basis. All raw data would then be staged in a database like Supabase Postgres for initial cleaning and normalization.

After data centralization, a Python service, often deployed on a serverless platform such as AWS Lambda, would process the information. Syntora's expertise in AI enables the application of models like the Claude API for tasks such as lease abstraction, extracting key terms like rent, term length, and concessions from unstructured text descriptions. This processed data would then be merged with structured data from sources like CoStar and a client's internal deal history to construct a unified comparable property set.

The final stage involves custom report generation. Syntora would develop a FastAPI endpoint designed to take a property address as input. This endpoint would trigger the data pipeline and format the results into the client's preferred branded Word or PDF template. Libraries such as `python-docx` are commonly used to populate placeholders, ensuring consistency and adherence to branding guidelines. The final report file could then be delivered through integrations with common communication platforms like Slack or email.

For deployment, the system would typically be configured on scalable cloud infrastructure like AWS Lambda for backend processing, which can help manage hosting costs efficiently. Monitoring is a critical component; CloudWatch alarms would be configured to send alerts to a designated Slack channel in the event of pipeline failures, such as a data source login issue. This allows for prompt investigation and helps ensure the system's ongoing reliability.