Commercial real estate research, data, analytics and advisory provider
Green Street sells commercial real estate and infrastructure research, data and analytics, backed by a Python data stack (Pandas, Airflow, Great Expectations, FastAPI) on AWS with Redshift and PostgreSQL. Hiring tilts toward sales and finance, with Gong and Salesloft in place, and job posts point to a post-merger finance integration plus a migration between cloud providers. That combination suggests a company consolidating back-office systems while pushing a faster go-to-market motion.
Green Street is a privately held provider of commercial real estate and infrastructure research, news, data, analytics and advisory services, headquartered in Newport Beach, California. It serves market participants, including REIT-focused investors, across the U.S., Canada, Europe, Asia and Australia. The company employs 201–500 people and hires in the United States, United Kingdom and Australia. Its products combine market research, proprietary data and predictive analytics, delivered through web applications built on React, Vue and Angular.
Green Street uses Python, Pandas, Apache Airflow, Great Expectations, FastAPI and Flask for data work, AWS with Redshift, PostgreSQL and MongoDB, React, Vue and Angular on the front end, plus Salesforce, Gong, Salesloft and NetSuite.
Green Street is headquartered in Newport Beach, California, and currently hires in the United States, United Kingdom and Australia.
Job posts point to a post-merger integration of the finance function, a migration between cloud providers, a cloud-based data collection platform and a web and mobile app for retail real estate data.
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Green Street's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.