2024 Drivers of Change

AI and the art of location

At a glance:

  • Advancements in Artificial Intelligence (AI) have the potential to enhance current capabilities and practices across CRE.
  • One example? We can use AI to analyze unstructured data sources such as leasing agreements, property listings, social media, and brochures.
  • AI models can indicate potential gaps in the market, vastly improving possible outcomes in the site selection process.

In an era marked by rapid technological advancements, AI has emerged as a transformative force across industries and markets. Within CRE, AI is playing an increasingly vital role, reshaping traditional practices and unlocking new opportunities for innovation. From predictive analytics to portfolio optimization, AI will dramatically impact how we make informed decisions, mitigate risks, and enhance operational efficiency.

What do we mean when we say “AI”?

In general, when we use the term “AI”, we are referring to the field of computer science that is involved in the development of algorithms and models that enable machine systems to perform tasks or functions that typically require human intelligence. This includes the ability to learn from data, recognize patterns, and make decisions. However, not every AI system uses the same model, the following two definitions encompass those most commonly used:

  • Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.
  • Generative AI (GenAI) is artificial intelligence capable of generating text, images or other data using generation models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generative new data that has similar characteristics.

Across both, a key notion remains true: AI takes known data or information, learns from this data, and then uses the learning to produce new findings or data. The generation of new findings, without instruction, is a critical element of AI.

"The powerful opportunity for an advisory firm like ours is to integrate AI-driven capabilites into a strong, trustworthy environment of data, tools, and talent. These dependencies cannot be overstated and will determine the ability to develop innovative techology that directly delivers value to stakeholders."

- Martin Jepil, Chief Information Officer, Avison Young

Martin Jepilheadshot

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.

illustration of a commercial real estate advisor working with artificial intelligence

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.

"Our proprietary AI model helps us predict patient demand for a total of 30 medical service lines across 800,000 distinct metro area locations in the US; resulting in over 22 million place-based recommendations seamlessly integrated into the decision-making process with our clients. Human intelligence alone could never consider that many potential locations in a traditional advisory setting."

- Olivier Maene, Global Product Director, Avison Young

Olivier Maene headshot

Finding a new way to improve health access and outcomes

Finding the right location can be tricky, but AI tools powering location intelligence take some of the guesswork out. Greater clarity on which locations will meet critical demographics best leads to improved outcomes. Here are just a few examples of the kinds of insights it can help us uncover:

Phoenix is in a pickle

Arizona is hotbed for pickleball enthusiasts, reflecting the sport’s remarkable surge in popularity across the United States. With participation rates skyrocketing by 86% year-over-year and 159% over three years, it should be no surprise that recent data has revealed a staggering 90-fold surge in orthopedic fractures, with pickleball-related fractures doubling since 2020. Our location intelligence tool identified the unmet demand for orthopedic care around Phoenix and pinpoints locations where urgent care services are most needed.

Phoenix Data Bite Gif

Philadelphia needs optimal OB/GYNs

In 2022 alone, private equity investment in women's healthcare companies surpassed $3.3 billion, reflecting robust demand for more comprehensive women's healthcare services. This fervent investor interest is driven by several factors, including the highly fragmented OB/GYN market and the opportunity to streamline specialist referrals. In Philadelphia, demographic analysis reveals potential areas of need for OB/GYN services. By comparing demographic drivers with existing OB/GYN supply, we have identified locations with the greatest demand for new facilities.

Philadelphia Data Bite Gif

From a microtrend’s impact on Phoenix’s healthcare markets to a more traditional use case concerning Philadelphia women’s healthcare demand, AI unlocks improved site selection capabilities that are worth taking note. Healthcare demand is inherently place-based and employing AI in this way can better ensure, as efficiently as possible.

What does this mean for commercial real estate?

The integration of advanced AI technology will improve the way that we design solutions for our clients. In addition to identifying the optimal location for service providers across industries, AI could suggest the best use for an available existing property. As we continue to explore the ways in which AI can support existing CRE offerings, we can embrace the opportunities for innovation through improved cost modeling, space optimization, and portfolio management. People will always be at the center of our places, however, AI might be able to predict how to best design those places to fit the needs of our people.

For more information, contact:

Martin Jepil

    • Principal, CIO
    • Corporate Executive
    • Enterprise Solutions
    • Global Services
[email protected]

Olivier Maene

    • Principal, Global Product Director
    • Strategic Consulting
[email protected]

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