The human brain is capable of tremendous achievements. But what are its limitations in business transactions, specifically those involving property and real estate investment management? At what point do machine data-based systems make more accurate decisions than intuition? Human intuition certainly has its place. As Deloitte researchers Surabhi Kejriwal and Saurabh Mahajan have noted, “The [real estate investment and management] industry has long thrived on relationships, which is how many investors have traditionally gained access to unique information. Traditionally, most investors have combined this information with their gut instincts to make investment decisions.” But although intuition can be a useful tool, Harvard Business School Online writer Tim Stobierski cautions that “it would be a mistake to base all decisions around a mere gut feeling. While intuition can provide a hunch or spark that starts you down a particular path, it’s through data that you verify, understand, and quantify.” A team of McKinsey experts echoes this sentiment, noting that complex decision-making requires analysts to “sift through tens of millions of records or data points to discern clear patterns and place their bets with few supporting tools to help glean insights from that material.” By the time the data needed to determine a course of action is collected, compiled and processed, they note, “the best opportunities are gone.” There’s also the problem of “cognitive biases” that misguide decisions with information drawn from the wrong sources. Fortunately, Stobierski notes, “it’s never been easier for businesses of all sizes to collect, analyze, and interpret data into real, actionable insights” into portfolio health measurements such as revenues, debt, risk, occupancy and sales, along with property-level operations like energy consumption and accounts receivable. Ronald D. Marten, CCIM, writing in Forbes, adds that “CRE brokers who can tap into today’s sophisticated data tools can differentiate themselves and their core value proposition to clients. Knowing everything about a building by using flood maps, demographics reports, traffic counts, tenants and retailers … and more gives a potential buyer an accurate idea of what their ROI is going to be on day one.” What do machine learning algorithms in the real estate realm consist of? One example is combined macro and local forecasts that identify areas with the highest demand for residential housing. On another front, retail mall investors can combine operational data at the property level with sales data from mobile sensors, social media and physical store sales, then use machine learning algorithms to analyze consumer buying behavior. Similarly, commercial property tenants can compare rent rates across various markets to make more informed decisions and get into spaces faster. Data compiled from multiple disparate systems is complicated and prone to error. As a result, sophisticated software applications capable of collecting, processing and using data across the asset management lifecycle have been developed and brought to market. This technology, complemented by machine learning recommended actions, enable management of deals, budgeting, investor reporting and more in a single connected system. Developers seeking new parcels, for example, can use advanced analytics to assess the properties’ potential, property uses and even pricing, among other things. Asset managers can evaluate pipelines and match deals with investors, benchmark their properties’ rent against others in the area, tie capital calls to investment lifecycle data and generate reports. Property-level data collected within a centralized location enables everything from online tenant payments to reduced heating, cooling and ventilation costs and better oversight of construction projects. Kejriwal and Mahajan point out that “investors and managers can leverage analytics and AI across key steps in the investment life cycle, from deal sourcing to portfolio management to risk management. In addition, these technologies can help increase efficiency and effectiveness of operational processes, such as information integration, investment accounting, and reporting.” Real estate software technology holds massive potential to shift decisions from humans to machines. Assimilating all asset management information at the property and portfolio levels and makes it universally available can preempt...
AI, Examined
CRE's Machine Learning Future
Editor’s note: The following article by Kevin Yardi, vice president of consulting practices for Yardi, was originally printed as a Realcomm Advisory on May 31, 2019. It is reprinted here with permission. Various aspects of big data, AI and Machine Learning have been reported extensively in this space and elsewhere. I’ll use this opportunity to highlight some key points that I think are particularly important to helping the commercial real estate industry benefit from these capabilities. Just what are we talking about? “Big data” means large, complex data sets that most traditional software platforms can’t manage. AI refers to computer systems that can perform tasks normally requiring human intelligence. Machine learning, a form of AI that enables systems to “learn as they go” without being explicitly programmed, supports informed decision-making by assembling and analyzing property information more quickly and more accurately than other systems. The expansion of digital data availability, computing power and software enhancements, along with cheap storage, have made these options viable for commercial real estate. What are the commercial real estate benefits of AI and Machine Learning? AI and Machine Learning can give companies better-structured data that improves business performance. For example, AI systems can detect patterns in conditions affecting energy consumption without being requested, then optimize the target temperature every 30 seconds to ensure comfort without using more energy than necessary. They can also learn from past performance to react to changes in occupancy, weather and other factors. All this translates into better performance through lower utility, energy and equipment maintenance costs; increased tenant comfort that reduces service calls and increases retention; regulatory compliance; investor satisfaction; and higher ENERGY STAR® scores. In short, AI saves energy and money while creating more comfort than humans could do on their own. More...
2018 Tech Trends
Innovation Ahead
With 2017 drawing to a close, we’ve been thinking about the future. The current year’s tech trends have been big and impactful, with artificial intelligence topping the charts. How about 2018? Most likely, technology is set to focus on the internet of things (IoT), artificial intelligence and machine learning. The Internet of Things Pretty much everyone has heard about IoT as it can be found in almost any industry now. You must have noticed that everything is becoming ‘smart’. In your car, at home, in the office, or shopping—there’s smart technology everywhere, ready to collect data and connect to other devices in order to assist you with your tasks. Gartner states that by 2020, a quarter of a billion cars will be hooked up to the internet. No doubt the trend will continue to progress in 2018 and even expand to areas outside of those mentioned above. Artificial Intelligence Pretty much anywhere you look—tech conferences, development and discussions, AI is at the forefront. And rightly so as it’s no easy thing to have computers able to learn in much the same way as humans do. Behind its extraordinary advancement is the incredible explosion in data—the more data an AI system has, the faster it can learn and the more accurate it becomes. It seems like humanity is on a path without a return alley. Machine Learning This technology has already swept every platform into its net. All developers want to make life and software more intelligent and advanced. It will replace all those mindless, repetitive and time-consuming tasks, precisely what technology should do. We already have some automated processes, decisions, functions and systems, carried out by algorithms or robots. Machine learning is the next step and ever more industries will be impacted by it—truck...
