The Role of Data Analytics in Commercial Real Estate Siting, Design and Valuation Decisions

Industries are rapidly evolving as business processes grow more interconnected and automated. Data and analytics play an important role in information technologies and their interaction with the physical world, including emerging fields such as artificial intelligence, the Internet of Things (IoT), and virtual and augmented reality. Although commercial real estate (CRE) has been slower than other industries to adopt data analytics, some firms have identified several ways that data analytics can support land and building development and contribute to better project outcomes.

To gain a sense of how CRE firms are using advanced data analytics, the NAIOP Research Foundation commissioned this report to examine applications in site selection, design and valuation for commercial buildings. The author conducted secondary research and interviewed brokers, data providers, investors, developers and professionals at CRE technology firms.

Firms continue to rely primarily on traditional forms of market research when making investment and development decisions. Nonetheless, several commercial real estate technology companies have developed specialized software that draws from data analytics to support applications ranging from highest and best use analysis to real-time building rendering. These emerging applications suggest that data analytics has the potential to add substantial value to new development projects through improved siting decisions and building design. This report makes several findings of interest to the development community:

• Emerging applications for data analytics in commercial real estate development include examining parcels within a jurisdiction to identify land packaging or building development opportunities, advanced construction planning and project management, evaluating the rent premiums associated with different building amenities, and evaluating a building’s suitability for conversion to a new use, among others.
• However, respondents indicate that data analytics are not yet being widely used to identify building locations or influence design characteristics for new or renovated buildings.
• Adoption of data analytics in commercial real estate has been slow due to the high costs associated with developing in-house capabilities and the currently limited range of applications for data analytics in the industry.
• Most commercial real estate firms currently outsource data-analytics tasks as the need arises. More companies will develop their own analytics capabilities as applications for data analytics expand.
• Advancements in artificial intelligence and further investments in structuring CRE data will expand the utility and potential applications for data analytics in siting and design