LoopNet + Crexi CRE Intelligence Actor

Commercial Real Estate Brokerage Intel

Problem Commercial real estate teams often repeat the same search across LoopNet and Crexi, then manually normalize and deduplicate listings before they can review a market or its public broker data.

What I built I designed, developed, published, and maintain an Apify Actor that collects public listings from supported sources and turns them into one structured dataset. It supports market filters or official search URLs while preserving the source context.

Key capabilities • normalized property, pricing, asset-class, and listing fields • cross-platform deduplication • source-listed cap rate and days on market when available • public broker names, companies, phone numbers, and email addresses when discoverable • JSON, CSV, Excel, Google Sheets, API, and webhook-ready outputs • documented input/output schemas, examples, and known limitations

My role Market research, product scope, Actor implementation with AI-assisted development, testing, debugging, schema design, documentation, pricing, publication, support, and maintenance.

Proof Public product: https://apify.com/kazkn/commercial-real-estate-brokerage-intel Video demo: https://youtu.be/TVAcUSzaS7w Technical examples: https://github.com/Boo-n/loopnet-crexi-listings-api

Constraints The product works with public or properly authorized sources. Field availability depends on source data, and missing commercial information is never invented.