Real Estate Analytics Is Evolving from “Location, Location, Location” to “Data, Data, Data”

Purchasing single family rentals is a booming business, and one that has become entirely data-driven for institutional players. Investors access local multiple listing services for available homes and use algorithms to select homes based on location and asset criteria. Due diligence occurs in minutes and offers can be made as soon as a house hits the market.

Real estate investors have always known that data is a critical component of analyzing a property’s potential and risks. Decades ago, we standardized data field requirements that enabled trading and securitizing asset portfolios and commercial mortgage pools. But those sets of data – a property’s financials and rent rolls, market occupancy, sales and rent comparables, construction pipeline – have not changed much in thirty years. Industry participants have often questioned whether additional, more complex data could be used to enhance decision-making across property types. Today, a proliferation of technology companies is offering new and far more granular data that will take real estate analytics to a new plateau and strengthen management’s investment and operating decisions.

Data was the focus of Cherre’s first Real Estate Data Summit, recently held in New York City. Cherre is a platform that aggregates many types of data for its users from dozens of sources. While we read about technology and big data all the time, it is rarely applied to the real estate industry. That is changing. As in other industries, real estate companies are beginning to prioritize data architecture along with IT architecture, and shifting from being process-centric to data-centric. As one executive said at the conference, “Data is the topic at every meeting.”

Accessing, managing, and analyzing data is often a struggle for businesses across industries, and real estate is traditionally a slow technology adopter. In the past, non-institutional real estate players have not had the systems and analytical prowess to take advantage of new data sources that can bolster their management and investment decisions and make them more competitive in winning deals. However, new technologies are enabling real estate companies of all sizes to shift property analytics from number crunching on spreadsheets to cloud-based predictive solutions that provide insight on the opportunities and risks of prospective property performance.

Exploring the dozens of companies now offering real estate data can be daunting. Management must prioritize what data would truly enhance analytics, which begins by articulating the questions the new data will answer. Management must also be sure that the company has the technology to receive and manage the data so that there is only one source of consistent data across all its business processes: a “single truth.” Many companies have already developed data warehouses, but the requirements of integrating new sources of data into analytical tools across functions takes those initiatives to a new level. Needless to say, the age of unique spreadsheets for analyzing and storing data on specific transactions is over.  

Examples of trending data sources include:

• Monitoring energy, lighting, elevator, and other property systems to maximize efficiency and benchmark against other buildings in the market
• Having real-time updates on the credit quality of tenants
• Following changing demographics, income, affordability, and mobility within neighborhoods
• Understanding how individual employees of tenants are using the space, including frequency of visits and the percentage of each floorplate being occupied each day
• Capturing market trends beyond supply and demand including weather, flood/wildfire, crime, zoning, and utility rates
• Tracking changing insurance costs in specific locations, particularly in relation to environmental vulnerabilities

Beyond investment analysis and performance tracking, environmental, social and governance (ESG) reporting is highly data driven. As more real estate companies adopt ESG reporting standards such as GRESB and GRI, the requirements for capturing external and internal data will increase.

As noted, the data journey begins with identifying knowledge gaps that impede business optimization, whether in understanding the drivers of demand for specific locations or enhancing competitiveness to maximize deal flow. What data would differentiate your business approach to decision-making so you can win more deals while maintaining your risk-adjusted returns? What data would enable you to better maximize occupancy at market leading rents? What data would alert you to the environmental vulnerabilities of assets or portfolios, and the resulting higher maintenance and insurance costs that would reduce margins?

A thorough review of information used in all business processes will reveal data gaps that can be matched against the many new data sources available to the industry. Exploring new offerings may reveal to management what they don’t know about the markets in which they operate. The journey is just beginning, and we have a long way to go before all the right data points become available for all property types and all markets. But the types and granularity of data will continue to evolve as investors begin to take advantage of new offerings. While it is unlikely that commercial real estate will ultimately be traded like single family homes, enhanced data and the resulting analytics will improve asset performance and risk management.