AI for Real Estate Acquisitions
We've been building AI acquisitions agents with real estate GPs. On August 13, we're opening the hood in a live 2-hour workshop
Real estate data and analytics, from parcel and location data to operational reporting. Thesis Driven tracks data providers and the CRE data value chain, location and foot traffic data, feasibility and permit data as leading indicators, unified data warehouses and the readiness AI tooling depends on, forward-looking metrics beyond vacancy and opex ratio, and analytics as a source of edge.
Finding land for ground-up development has always been a local, relationship-driven, somewhat chaotic process. Work the network. Drive
A data-driven analysis of how real estate GP scale correlates with technology adoption, specialization, asset class, and more
Patrick Carino couldn’t find the CRM he needed, so he built one. Then it became a company.
There are tens of thousands of real estate leaders in Thesis Driven's database. What do they all do?
First, let's get the news out of the way: ReZone, a company Thesis Driven co-founded and incubated,
For multifamily operators, the AI gap is shifting from technology to execution
Case studies from real estate companies implementing AI to solve real problems from delinquency to diligence
Introducing nine new asset classes, projects, and more
Exploring how real estate operators can prepare themselves for AI, from data operations to governance and compliance
New technologies are rapidly changing how homebuilders and developers are finding sites
How better technology and data availability is transforming the single tenant real estate market
Finding active real estate developers is harder than it sounds. What actually works — and a searchable database of 8,000+ US operators.
Covering the future of real estate and the people creating it