Automate CRE Market Analysis and Underwriting

A 15-person CRE firm relies on junior analysts to compile market reports. The process involves manually exporting data from CoStar, searching for the property on two different county assessor websites, and copy-pasting everything into a Word document template. This takes over two hours per property and is prone to human error, especially at the end of the day.

Off-the-shelf CRE data platforms like Reonomy or CompStak offer some automation but have two major failures for a small firm. First, they charge per seat. A $300/user/month fee for five analysts costs the firm $18,000 per year for a single tool. Second, these platforms cannot incorporate the firm's most valuable asset: its internal deal history. They cannot analyze your past deals stored in a spreadsheet to identify better, more relevant comps.

Trying to patch these systems together with simple connectors also fails. The data from a county assessor's PDF report is unstructured. You cannot use a basic workflow tool to parse a zoning document or a deed transfer. This step requires real data extraction code, which is where generic tools stop and a custom-engineered solution becomes necessary.

Syntora's engagement would begin with a discovery phase to audit your current market analysis process, identify key data sources, and define the specific insights required. This initial phase ensures the proposed system directly addresses your firm's challenges.

Based on these findings, Syntora would design and build custom data ingestion pipelines. For public data, we would develop Python scripts using libraries like httpx and BeautifulSoup to extract property data from target county assessor websites. For subscription services such as CoStar, the system would be designed to process and integrate your CSV exports. These ingestion scripts would be deployed as scheduled AWS Lambda functions, configured to refresh data regularly, storing the results in a Supabase Postgres database. This pattern of automated data ingestion is one we have implemented successfully for clients in adjacent financial document processing domains.

With all relevant data centralized in Supabase, Syntora would implement data cleaning and standardization routines using tools like pandas. This process would involve joining disparate data sources, such as CoStar records with county data, on common identifiers like parcel IDs to create a unified view of each property. The structured property data, alongside any available textual descriptions, would then be prepared for processing by the Claude API. Our team would engineer specific prompts to direct the model to summarize market conditions, identify relevant comparable properties, and provide justifications for its selections. Syntora's expertise in prompt engineering ensures the AI output is accurate and actionable.

The final stage involves report generation. The structured JSON output from the Claude API would be passed to a Jinja2 templating engine, which can populate a branded HTML or PDF report. The entire workflow would be exposed via a FastAPI service, providing a programmatic interface to the system. Syntora would also develop a simple web interface, potentially hosted on Vercel, allowing an analyst to easily input a property address and trigger the generation of a formatted market report.

This system would be architected using serverless components, which helps minimize operational overhead. Syntora would implement structured logging with structlog to provide clear visibility into system health and alert mechanisms for any API call failures or data source format changes. Throughout the engagement, Syntora would work closely with your team, providing regular updates, documentation, and knowledge transfer to ensure your firm can fully utilize and maintain the delivered system.