Saurabh Mahajan, Real Estate assistant manager, Deloitte Services LP, May 18, 2016
In 1854, the infectious and fatal disease Cholera spread through London’s Soho district, claiming many lives.1 Everyone believed the disease to be airborne, until physician John Snow discovered that it actually spread through water.2 How did he do that? Well, Snow plotted the cholera cases on a detailed Soho map and found that most cases were centered around a water pump on Broad Street, which was supplying polluted water.3 Many say that Snow’s lifesaving work was also one of the earliest examples of geospatial analysis.
Geospatial technologies have evolved constantly since then, and some of the key forms used today include geographic information systems (GIS), remote sensing, and global positioning systems. Along the way, these have been well supported by other technology advancements such as satellites to capture earth’s imagery, and computer software and systems for storing and processing information.
Geospatial technologies have been used across many industries, and they clearly play an important role in commercial real estate (CRE) location-based activities as well. So far, applications in CRE have focused on mapping and analyzing static, public data such as demographics and topography, to help companies optimize site selection and tenant mix to achieve desired sales growth. However, I think the CRE sector today is at an important cusp, as increased adoption of mobility and social media is driving growth in the volume and veracity of geocoded data. The data pool is only going to increase as the sector adopts the Internet of Things, where sensors will likely generate unprecedented amounts of structured and unstructured data on different aspects of a property.4 These can include building condition, occupant behavior, and the external environment. In addition, new ways of data capture, such as drones and crowdsourcing, are making it possible to collect more precise location data on both outside and inside the built environment.5 The question is, how can CRE companies leverage the large sets of diverse geocoded data to make smarter decisions?
Today’s geospatial analytic tools provide a useful way to aggregate, analyze, and map the information from diverse sources. The mapping and visualization of disparate datasets can help CRE companies discover new patterns and relationships that otherwise would be difficult to comprehend. For instance, mall owners can more precisely track shopper movements to analyze both the high-traffic zones within a mall, and the customer buying behavior.6 This can enable owners to make smarter and faster decisions related to appropriate location for each tenant, comparative rent to be charged for different spaces within the mall, and new tenant acquisition. In another case, office owners can leverage geospatial tools to identify future talent hot spots by analyzing trends around demographic evolution, immigration, and education. These insights can then be layered with data on competitor locations and local regulations to make smarter decisions around new sites.
However, many companies are not well prepared to seize such opportunity. Companies need to invest in advanced geospatial solutions that can help them to analyze and map the varied geocoded data, predict more accurately, and make more intelligent decisions. For instance, companies can use geostatistical tools to simulate and make precise predictions even when the data is incomplete, or track and analyze real-time data for proactive portfolio management. But are these solutions compatible with legacy systems and can they be used by less tech-savvy CRE professionals? The answer is yes! These solutions not only integrate well with existing business systems, but are also available through user-friendly apps, which can be accessed through different devices.7 The mobility feature, while increasing the utility of geospatial solutions, can also improve coordination among different CRE functions. What do you think? Is your company planning to invest in capabilities to leverage the coming avalanche of geocoded data?
1Simon Rogers, “John Snow’s Data Journalism: The Cholera Map That Changed The World,” The Guardian, March 15, 2013. 2Ibid.
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