AI and the art of location

It just starts with a little bit of experimentation – and seeing what could be.

We explore now through a practice area we are currently enhancing with the help of AI: location intelligence.

How can AI models work to improve a critical CRE practice like location intelligence, and how are we leveraging AI to enhance these service offerings?

One of the benefits of AI is this ability to understand unstructured data – or data that does not have a predefined model or format such as written documents, web pages, and images. An AI system can read unstructured data to extract insights and structure the information or data within these sources. In an industry inundated with written materials such as leasing agreements, brochures, and property listings, AI offers the ability to gather critical details for analysis.

Through investigating and analyzing key demographic data, existing service offerings, and determining future demand, we are able to leverage proprietary AI models today to deepen location intelligence in support of site selection in real estate decision-making.

More specifically, we are currently collecting and processing immense amounts of data through tools that enable our real estate experts and clients to explore scenarios and outcomes that unlock new perspectives on locations, risks, and strategic opportunities.

Finding quicker paths to care: leveraging AI-enhanced location intelligence tools in healthcare

The term “AI in Healthcare” suggests images of robotic surgeons undertaking complex procedures, computers offering diagnoses, and machines monitoring vitals and administering basic care. Many of these potential applications may seem futuristic and far from reach (but the reality is some may be much closer than we think).

The potential for AI has already been recognized and deployed on the administrative side of healthcare with notable changes for documentation and revenue cycle management, and AI also aims to transform the complex process of healthcare site selection.

Health systems and large provider groups have been using population demographics to understand demand for years. Population demographics are particularly useful for modeling the potential demand for the clinical specialties for which patients often make their own provider selections – primary care, general pediatrics, OB/GYN, and urgent care. Demographic data is often used in combination with a separate understanding of the competitive environment to help providors pinpoint opportunities for growth. This has been a reliable process for health systems and large provider groups alike, but it is inherently subjective because the demographic and competitive data is rarely well integrated. Additionally, disparate data means insights are likely produced manually, introducing natural subjectivity, and lengthening the process of site selection.

Machine learning algorithms have the potential to quickly transform the site selection process by combining disparate data sets on population demographics, competitive providers, and availability of talent.

This is exactly the approach the AVANT team at Avison Young is taking with our Location Intelligence for Healthcare tool. By comparing every potential location for a new provider to existing provider locations across the country, our AI model can identify the unmet demand for each clinical service line in every U.S. market, constantly updating to reflect changes in provider locations and shifts in population demographics. With this technology, health providers can easily understand and compare opportunistic locations in their home markets for each clinical service and large provider groups can find optimal locations for growth in their existing markets and identify potential acquisition targets in new markets.