Bringing Big Data and Machine Learning into the Commercial Real Estate World

Economic Value is defined as the measure of benefit that is provided from a particular good, service, or resource. These resources have, throughout time, transformed from grains, to foods, to precious metals, and then to oil as every one of history’s revolutions swept across the globe. Where there are resources, there is value. Where there is value, there is power. As the Information Age has taken the world by storm, value has once again shifted to lie not within precious metals, not fossil fuels, not oil, but in information itself. Data, to be specific. Data and “cryptic” underlying information has the power to provide both qualitative and quantitative insights which have never before been possible. These insights hold unharnessed possibilities when it comes to propelling value growth and conservation.

Virtually every single industry has invested significant financial and Human Resources to absorbing new Data information in the hopes of providing a leg up on the competition and improving their industry standing. When it comes to value growth, financial investments provide the foundations for progression of value as well as safe conservation of funds. The Commercial Real Estate Industry, however, is one which has held some reservations and taken more time towards its adoption of Data as a driving mechanism for decisions and higher returns.

Old School Mentalities

Commercial Real Estate valuations are historically lucrative. High earnings potentials, consistent demand, and objective price information have long steered investors towards CRE investments. These valuations are driven through complex models and even more complex Excel spreadsheets. However, at the end of the day, CRE decisions are made by a human being with a plethora of experience (good and bad) that has led them towards instinctual action. This has led towards a slower than normal adoption of technological opportunities in the CRE space. Investment opportunities are aplenty and Data insights can assist in marrying the old school instincts with new school advantages.

-Peter Lynch“Know what you own, and know why you own it.”

New School Opportunities

Ultimately, whether you are using Excel SUMIF functions or Machine Learning methods, the value of the investment through time will be forecasted through analysis and comparison of a large variety of factors. Data analytics, particularly through machine learning, can provide avenues which lead towards strategic advantage s that competitors in the space may not have. Descriptive Analytics Traditional metrics for investment information are still extremely valuable. Vacancy Rates, footfall, loan-to-value ratios, and lease turnover rates all tell a story as to how the property’s value got to where it is. These metrics can be mapped into more visible figures which give more accurate and wholesome insights into why certain trends are occurring or did occur. Observations of economic indicators at the time of significant events or mapping of patterns in market movements will help give more backing to those instinctual decisions. Predictive Analytics The most sought after question is “what’s next?” What is about to happen in the industry and markets and when, and then how does one be in the best position to capitalize. Static Data can be the driving force behind these predictions. By using this static data in constantly updating machine learning models, accurate information can be provided in real time to the decision makers. Prescriptive Analytics Financial markets are extremely fast-paced and up-to-date information is crucial to be able to capitalize on opportunities in the market. Machine Learning and AI provide a structure to take results from descriptive and predictive analytics and turn them into practical advice for moving forwards. Instead of just providing information, the analytics can provide real time advice as to best course of action for a particular situation.

To Recap:

Most companies use only a fraction of the data available to them to make key decisions, but a growing number of firms are using predictive analytics to help better understand and forecast trends.

Unlike descriptive analysis and other techniques based on past data from in-house and outside sources, predictive analytics uses a combination of artificial intelligence, past data and innovative “Big Data” sources such as geo-tracking of mobile phone users to develop new insights.

Commercial real estate firms are just scratching the surface of the opportunities presented through the use of predictive analytics, but a growing number of big players, private equity investors and start-ups are targeting promising uses for the CRE world.