In part one of this series, CRE and the Accelerating Change Phenomenon of Artificial Intelligence, we took an introductory look at artificial intelligence (AI) and its influence and application in the commercial real estate (CRE) industry. In our second installment, we focus on what makes AI and machine learning possible — and how this innovation can be leveraged to drive CRE business forward.

Big Data
With a projected worth of $273.4 billion by 2026, big data is what powers AI and machine learning. It’s different from “data,” wherein traditional information is structured with fixed formats or fields that are relatively straightforward. Big data, on the other hand, speaks to massive amounts of information — gathered through analysis, measurements, observations and research — that are scattered extensively and substantially more complex. It is often defined by the 3 Vs of data: volume, variety, and velocity.

Big data, however, doesn’t just refer to the quantity of data. It also includes the disciplines used to process the data into digestible and useful insights. This is where AI and machine learning come into play.

Volume = amount of data collected/generated

Velocity = speed at which data is generated and processed

Variety = different types and formats of data

In simple terms, AI is computer software that mimics the ways that humans think in order to perform complex tasks, such as ascertaining, analyzing, and reasoning. Machine learning, meanwhile, is a subset of AI that uses algorithms trained on data to produce models that can do the same work.

Today, most AI is performed using machine learning, so the two terms are often used synonymously, but AI actually refers to the general concept of creating human-like cognition using computer software and systems, while machine learning refers to only one method of doing so.

In CRE, data is critically important because it gives stakeholders valuable information about a property and the overall condition of their investment. The majority of CRE data is collected from physical objects such as a building’s energy use, leasing trends, property financials, local demographics, and more. These data sets are collected through what is known as the Internet of Things (IoT), which means a “network of physical objects — “things” — that are embedded with sensors, software, and other technologies.”

While incredibly useful, this data tends to be limited and doesn’t tell the whole story. For the most part, stakeholders in CRE rely on a combination of restricted traditional data, experience and good old-fashioned intuition to make business decisions — potentially leaving money on the table.

Big data provides CRE with insights into things unseen, reaching beyond the tangible to make unsuspecting connections and provide a deeper breadth of information and predictive analysis about a property, and everything associated with it. All of this data helps companies identify micro patterns and trends that are highly specific to the block where a building is located.

As the saying goes, “you don’t know what you don’t know,” and tapping into new insights via big data could prove to be a significant advantage in CRE, including these specific fields:

Big data is a powerful planning tool in construction and can narrow down locations most suitable for a new development based on macroeconomic and demographic trends, infrastructure, economic development incentives (e.g., tax incentives, grants, zoning), and so on. Predictive analysis looks at expected property appreciation, construction costs, capital expenditures, and development timelines to further help developers determine the feasibility and financial implication of a planned project.

However, keep in mind, that highly automated buildings and systems using some form of AI are only as good as their data. Human setup and continued analysis are required. With plans in place, intuitive 3D models can provide a visual representation of a property with flexible designs that show the best and most sustainable use of space, along with associated maintenance and operational costs and income potential for each design.

CRE loans represent big money, and lenders depend on data analytics for credit consideration. Big data can swiftly aggregate and interpret all this manual data with incredible speed, as well as tap into new information that historically hasn’t been available to provide a detailed, yet very comprehensive evaluation of a borrower’s repayment ability and risk of loan default.

Acquiring big data analytics help property managers measure an asset’s performance and tenant patterns in real-time in much greater detail. Managers can then respond to tenant needs with more intent and purpose, leading to improved property and tenant experience and overall maximum lease value.

Maintenance is one of the costliest expenses for property owners and managers. But with big data, management has greater intel into building systems and processes to increase utility and energy efficiencies and monitor systems to identify and correct any discrepancies before an actual issue arises. Big data-supported properties are inherently easier and more affordable to maintain and manage.

With consideration to marketing of an asset, management benefits from predictive analysis in employment, demographic shifts, demand/supply trends, and other information to identify what prospective tenants are looking for — then adjust marketing efforts accordingly.

When considering an acquisition, more information is better. In addition to operations, big data sheds light on the physical condition of an asset, location, advanced market analysis (e.g., area comps, crime rate, competition) and forecasts to assess overall investment risk and short- and long-term ROI before making a major business decision.

Property valuation and appraisal is a significant part in the acquisition process that can be easily replaced by big data-decision making. Big data looks at historical property prices and market conditions and combines it with other variables to make accurate valuation assessments and optimal pricing suggestions.

Additionally, big data may make it possible for CRE companies to not only circumvent certain risks but initiate a more targeted investment strategy from the beginning by identifying revenue-generating opportunities.

By conducting their own market-level analyses with a “boots on the ground” approach, KBS has been able to take big data a step further. KBS maintains and tracks a list of at least 25 target markets — conducting thorough and continuous studies to understand targeted buildings.

With asset managers in each region that have first-hand experience and in-depth insight on these markets,” said KBS Co-founder, President and Chairman Charles J. Schreiber Jr., “we have a ground-level perspective on changes in the industry and a deep comprehension of market conditions that few other investment managers have.”

Big data is a revolutionary property technology for CRE. It gives players access to a new host of variables and to up-to-date information about everything related to developing, purchasing, leasing, and management of a CRE asset — plus the artificial intelligence to come up with highly accurate and more profitable solutions and predictions. By harvesting the power this big data, CRE stakeholders can uncover greater opportunities and transform their business.

Be sure to catch part three of this report in the coming weeks. Learn more by visiting