They might be lagging near the back of the pack, but commercial real estate analytics hardware is finally catching up with other, similar industry tools. Across the board, data and analytics providers in the real estate sector are experiencing unprecedented growth, both in terms of their ability to secure funding due to demand in higher quality applications and in the technology being utilized to help today’s professionals manage their portfolio.

Traditionally, commercial real estate professionals walked the slippery slope of depending on a risky combination of intuition and retrospective data that didn’t allow for a full understanding of a property’s strengths and weaknesses. Now, numerous analytics tools are making it easier than ever to gain a far more insightful look at potential pitfalls and opportunities.

More than ever, access to relevant data analytics is vital for succeeding in the commercial real estate market, and knowing where to acquire property and when to develop it can mean the difference between success and failure.

The Different Types of Analytics, and How They Pertain to CRE

The ability to have technology do the legwork of culling through mountains of data to find hidden patterns isn’t just a convenience in today’s commercial real estate market, it’s a necessity: granular trends within specific city blocks are far superior to city-wide assessments, affording a hyper-local look at actionable, relative data points.

Hundreds – if not thousands – of variables can now be extrapolated from existing data sets, typically in one of four categories: Descriptive (which gives the answer of what happened), Diagnostic (helps to answer why something happened), Predictive (which forecasts what is likely to happen), and Prescriptive (which gives an idea of what action to take).

Descriptive Analytics (Reactive)

Descriptive analytics are the cornerstone for any business to help them understand how they’ve performed in the past, and the same holds true for commercial real estate. These types of analytics give a glimpse into how assets have fared in the past – right up to the present – and will provide key data points on assessing a return on an investment (ROI). Perhaps unsurprisingly, descriptive analytics have even been demonstrated to accurately forecast year-over-year rent per square foot for multifamily buildings, giving asset managers additional knowledge to help inform key decisions.

Diagnostic Analytics (Reactive)

Diagnostic analytics are the M.D.’s of big data. Much like a physician looking at the root cause of what caused a particular ailment, information gleaned here can show what happened in the past to help inform future actions, and correct (or improve upon) the issue moving forward. Leasing, marketing and advertising cues are all key pieces of the diagnostic analytics puzzle. These patterns can reveal how leasing trends might have affected tenant rates, or how specific advertising and marketing initiatives may have pushed numbers north or south. Being partial to this type of data can help with planning more streamlined activations in the future.

Predictive Analytics (Proactive)

Perhaps more so than any of the others, predictive analytics have the potential to be a real gamechanger in real estate. The ability to look at the number of grocery stores within a mile radius of a potential property or cellular signal patterns to see where and how people are living is invaluable. This helps alleviate some of the more conventional pain-points in decision-making, and as the power and deductive capabilities of these tools grow, macroeconomic and demographic indicators will increase exponentially, putting at your fingertips an unfathomable amount of information on a specific area.

Prescriptive Analytics (Proactive)

Prescriptive analytics are forecast to have massive growth over the next ten years, thanks in part to machine learning and AI. A virtual smorgasbord of predictive analytics with a dash of simulation and a sprinkling of experimentation, this data category is all about helping you make the right decision, whether looking to correct an error or simply help in the decision-making process.

As advanced as analytics have become, commercial real estate analytics are still in their relative infancy, and their complexity and capabilities will only continue to burgeon. However, these tools should only serve as a supplement to investment hypotheses, and derivative data should never be the generator of them. Yet as they advance and continue to flourish, analytics in the commercial real estate sector can and will yield dynamic data that will help inform, challenge conventional methodology, and help professionals in identifying key factors for all of their commercial real estate challenges.