Automate Property Performance Analysis for Your CRE Team

Most CRE teams rely on a patchwork of disconnected tools for valuation. The industry standard, ARGUS, is a powerful calculation engine but getting data into it is entirely manual. It cannot connect to live market data or automatically parse a rent roll from a PDF. Analysts spend their time on data entry, not analysis. This forces the entire workflow into Excel, the universal but brittle solution.

In practice, a 20-person investment team evaluating a new property faces a fragmented process. An analyst downloads market comps from CoStar as a PDF, receives the T-12 financials in Excel, and gets 50 individual lease agreements as separate PDF files. The first four hours are spent manually abstracting key dates and clauses from leases and re-keying numbers from the CoStar report into the firm's master valuation spreadsheet. This introduces a high risk of human error that can quietly derail a multi-million dollar decision.

Off-the-shelf property management systems like Yardi or AppFolio track historical performance but are not built for forward-looking analysis or market data integration. They are systems of record, not analytical engines. The structural problem is that CRE data lives in silos. Market data is in one platform, operational data in another, and legal data is trapped in unstructured PDFs. No off-the-shelf product is designed to be the connective tissue that automates data aggregation before analysis begins.

We would begin with a comprehensive audit of your current underwriting workflow. This involves mapping every data source, from CoStar and public record APIs to internal Excel rent rolls and broker offering memorandums (OMs). The goal is to define a unified data model that can accommodate all sources and serve as the single source of truth. You receive a data map and a proposed database schema before any code is written.

The core of the system would be a data pipeline built with Python and deployed on AWS Lambda. For unstructured documents like leases and OMs, we would use the Claude API for its large context window, which is critical for parsing 100+ page documents. The API would extract key terms like renewal options, expense stops, and tenant improvement allowances into structured JSON. This structured data, along with data from other sources, would load into a central Supabase (PostgreSQL) database.

The delivered system feeds your team's existing tools, not forces them into a new one. A FastAPI endpoint would allow your analysts to pull clean, validated data directly into their Excel or Google Sheets models with a simple refresh. You receive the full source code, a runbook for managing the AWS services (typically costing under $50/month), and complete documentation for the database schema.