Q&A with Chris Magill, Partner and Managing Director of Vista Site Selection
This article originally appeared in the Winter 2025 edition of Development Incentives Quarterly.
In this edition of Development Incentives Quarterly, we welcome Chris Magill, partner and managing director of Vista Site Selection. Vista Site Selection, an ancillary business of Vorys, is dedicated to helping clients select the most advantageous and economically viable real estate sites for expansion or future development.
In this Q&A, Chris discusses artificial intelligence (AI), data models and their role in both identifying ideal project sites and aligning the visions of all parties involved when negotiating incentives packages.
Vista Site Selection’s use of data, technology and AI sets it apart in the site selection industry. Could you explain how you use technology and AI to help clients identify optimal sites?
We are in the golden age of data. Twenty years ago, the norm was that some consultants were using data but many weren’t touching it. Now, everyone is using data. The key differentiator is now the “what” and “how” of data usage. The “what” entails a plethora of private data sources available for purchase than can be mixed in with typically free public data (e.g. census, etc.). The “how,” which is a large part of Vista’s competitive advantage, comes in many forms. How are we best answering our clients’ questions or solving their problems with data? How are we ensuring we are not sacrificing reliability for recency in combining public and private sources? How are we storytelling our results? Is it through static tools (maps, charts, tables, reports) or dynamic tools (interactive maps, calculators and dashboards)?
Currently, Vista is using AI to aggregate historical company performance data, then testing that performance data against the current geographic and real estate market conditions, scanning nearly every intersection in the country for an optimal location. Vista will add factors (additional inputs) into the model, which are based on company preferences. For instance, you can find our managing director of analytics, Evan Stair, narrating a machine learning model his team created based on ALDI’s existing location performance here on our YouTube channel. In this scenario, we downloaded cell phone visitation data for all of ALDI’s locations. Targeting the most-visited locations, we then ran analytics of those top-visited sites that include a series of drive-time radii to determine what else is happening in the market around the top-visited locations. This piece may include thousands of data inputs (related to consumers, businesses, demographics, economics, etc.). We can then derive relationships to what data inputs most strongly correlate to a site with the most visits, then use our model to identify those sites nationally.
What are some other AI applications Vista Site Selection can deliver for clients?
Vista has developed a number of tools that utilize AI to help clients choose optimal sites. We have a Retail Growth Model, which runs similar analytics to the ALDI example, an industrial labor tool model that can run optimal site analytics based on factors that include labor, infrastructure, supplier proximity and market dynamics, and a Development Impact Model that can estimate economic impacts based on historical use performance and business investment scenarios. While these products all exist, the common ingredient goes back to the “what” and “how.” Our significant investment in data infrastructure/sources and the talent we have on our team to create custom solutions is our competitive advantage, and it is the advantage we share with our clients to eliminate risk (and cost) from site decisions.
To take it a step further, AI can be a strong force in looking backward at underperforming sites in contributing to consolidation decisions. As we know now, the office market, retail market and hotel markets have been experiencing a decline in new building construction compared to pre-pandemic levels, as many corporate offices are consolidating and existing buildings are being rehabbed or converted. Industrial manufacturing tenants are looking at consolidating and hyper-scaling facilities, as the number of megaprojects in the U.S. has increased more over the last five years than the previous 20. Using performance analytics and ranking sites can help clients better understand where to optimize the performance of their facilities.
How can AI help align the interests of developers/end users, investors and local communities when pursuing projects that involve economic development incentives?
Data can often be the common ground in getting deals done if it unlocks the entirety of the story. Each project has its own unique value proposition and oftentimes that value proposition does not get articulated in its entirety. Take tourism projects, for instance. The direct ROI of a convention or event space may be limited in some circumstances, as it is typical for governments to focus on direct jobs, wages, ticket sales and the cost to build the center. However, this can be enhanced by creating a solution that scans comparable events from existing locations around the U.S. to better understand the correlation to indirect and induced economic impacts. This might include inputs such as:
- cellular phone data to illustrate visitation and where consumers typically go before and after events to spend money;
- credit card data to understand the types of households that most consume the event; and
- correlations to increases in area property values from the presence of the facility, among many others.
Having this data can help investors understand the full economic value of the asset, governments better understand tax revenue returns and developers sell the full value of their projects to these groups. The same predictive analysis, with different inputs, can be used for office projects, industrial projects, retail and community facilities.