In this paper, we explore deep property data and the industry’s reach into various sectors, professions and services dependent on reliable data.

We’ve seen the capability of AI ingesting public and open market data on the income side of property operation. It also raises concern and highlights inherent risk.

Another side of real estate AI faces different challenges. Often considered private or insignificant detail, deep data is essential to improve user efficiency between property ecosystems.

The expense side of property operation deals with build, development, maintenance, performance, tenant improvements, routine upgrades and hundreds of service providers.

Any use of AI and efforts to improve operating expense is dependent on reliable up-to-date data. The information AI requires to augment process and workflows today has historically been inaccessible to users and technology.

In the industry’s transition to digital technology, property owners are not only participants, they’re pioneers! Early adopters will lead in their property’s AI data, cost efficiency, earning a tangible return and shaping the future of digital property data.

What is the minimum technology stack necessary to improve efficiency in property operating expense? How will owner’s transition property to digital technologies and what will it cost? Can investors recover the expense and improve property valuations?

SLACi begins to address these questions with AI & Data in Real Estate.