Historically, commercial real estate (CRE) intelligence has been passed down from person to person, either through a brokerage company or a corporate structure. While this seems to be working for now, the future I see for the CRE industry includes a combination of this human intelligence and technology-based systems. The blending of these knowledge-based systems is currently being driven by the use of artificial intelligence (AI) systems such as machine learning (ML) and natural language processing (NLP). AI-based systems, when combined with human intelligence, can help propel our business of real estate development and advisory.
As a commercial real estate development and investment company, our office uses technology-based applications every day and in most of the steps within a particular assignment. The use of technology tools in CRE is growing and expanding as more companies seek to specialize and differentiate their service offerings while streamlining their internal systems. There are many existing tech applications already supporting the industry, including financial analysis, asset and property management, lease analysis, sales platforms and others. CRE firms also gather data from CRE-focused sources like Loopnet, CoStar, LandVision and others. When combined, these tools can help make our work more efficient and accurate. However, the data sources are separate and generalized, and thus not task-specific. The ability to automatically source data from available locations and compile it into a useable, task-specific format would add tremendous value to the industry. This is where I see AI coming into play.
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The organizational processes that I believe could benefit from the use of machine learning include:
• Market planning: We need to understand and assess the retail viability of a particular geographic region or city for one or more retail companies using demographic and other ranking criteria.
• Site selection: We conduct numerous site selection ranking activities to locate superior sites within an approved market.
• Comparable analysis: If selected sites score as viable targets from a retail standpoint, we need to conduct a comparable analysis to identify which potential sites will be most successful.
Our goal in applying machine learning to our business is to develop and implement a unique process of intelligent site selection. In commercial real estate business, most retailers, brokers and developers perform a complicated and time-consuming process to identify superior sites. Our hope in applying machine learning is to streamline the process — save time and reduce errors — and increase the resulting success factor.
How To Get There
In looking at the steps involved in the process, start by planning overall market strategy to identify general areas within a city or larger geographic region. After assembling large sets of ranked data, using machine learning, firms should be able to highlight preferred areas via heat mapping. Combining these results with human intelligence about traffic patterns, retail centers, etc., CRE firms can set goals for the market.
Once market areas are identified, step closer to census blocks or city blocks to better define the target opportunities. Using a combination of databases and machine learning, compare potential sites against existing sites in other markets to determine a ranking or score of potential revenue/success (analytical predictions). There are several off-the-shelf ML-based site selection systems, such as SiteZeus and LocateAI. These systems require vast data entry in order to deliver realistic predictive results. Integrating these and other systems with a standardized database structure of site-related data could lead to a real-time tool that provides an intelligent base map for predictive site selection.
To implement NLP into business organizations, the process involves streamlining internal operations in order to save time and be better able to act quickly on new opportunities. This will result in a differentiating competitive edge. Many companies employ a large amount of human capital to conduct the many different searches to identify, quantify and execute new opportunities. Streamlining these processes using primarily text-reading NLP can reduce operational and payroll expenses and shorten the process timeline.
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A market research department, for example, using an NLP process of text-reading through many listings of available properties, searching for keywords and phrases, can isolate particular properties for review by human analysts. Further, for particular property research, portions of ownership data are stored in various web-based locations. Using text-reading NLP and the property owner’s name, we can scan identified sources for matches. In researching tenant contact information, we find that people in these positions routinely change companies and locations. Being able to text-read through industry publications and related databases by company name, geographic area and title would save CRE companies much time and effort.
The use of an AI-based real estate site selection tool is a prime example of humans and machines working together to develop a more accurate and efficient method of analyzing markets and sites. Hopefully, as these systems are fine-tuned and used more broadly, data input applications will evolve to integrate the many sources of data into a cohesive tool that can truly improve the collective intelligence in CRE.
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