Presented By: Marks Paneth LLP
Data Analytics Done Right: Successful Adoption of Analytics in Commercial Real Estate
The commercial real estate industry is still in the early stages of adopting data analytics as an innovative strategy for managing investments, operations and finances. The tools to analyze a wealth of data relative to location, pricing, appraisals, industry trends, competition, vacancy rates and more are now widely accessible. The challenge is in (a) acquiring the data and (b) developing the analytics necessary to achieve these objectives.
Data analytics is an easy concept to champion, as the proper analytics can deliver insightful, actionable results. However, when introduced incorrectly, data analytics can become time-consuming and ultimately deliver little value to your business. The successful adoption of data analytics in commercial real estate — or any industry, for that matter — relies on an organization’s ability to:
- – Acquire pertinent internal and external data,
- – Validate the accuracy and completeness of the data,
- – Ask questions of unknown things rather than things that are known,
- – Build analytics that are actionable,
- – Apply what is learned.
The list above requires a certain level of information technology fluency, so it is not unreasonable to assume that when a real estate firm is considering the adoption of analytics, they are also making a build-versus-buy decision. However, even in cases where that determination has been made, it is still essential that any analytic investment incorporates the following key measures.
Acquire the right data
Data is everywhere. It already exists within your business and it can also be sourced from the outside. The key is to identify the data that will deliver the most valuable results. For example, an analytic designed to assess and compare properties in a particular market may benefit from the inclusion of external data such as demographics, traffic flows, schools, taxes, etc. Each of these data streams may or may not improve the quality of the analytic results. Source, accessibility and cost are all contributing factors in the acquisition of data; the key determining factor in whether the data is beneficial or not is understanding why the data is being acquired.
Keep it clean
The accuracy and quality of data can have a tremendous impact on the resulting insights. For example, an analytic insight that is date-dependent — such as occupancy — can become corrupted if any part of the data collected has been transposed or entered incorrectly. Accuracy and quality concerns apply to both internally and externally sourced data. To correct any issues associated with your internal data, establish strong data governance guidelines and appoint a person or persons for oversight of data management. If seeking out external data streams, vet your sources carefully.
Ask the right questions
Data analytics help you gain new insights and make discoveries that aid in the decision-making process. A common mistake made by those unfamiliar with the power of analytics is the tendency to ask for reports that contain information that is already available. For example, “What is our current cash flow?” is a question often asked in the development of financial analytics. Answering that question is as simple as running a set of standard reports comparing accounts receivable to accounts payable. A better question to ask is, “When will there be a change in the cash flow that will impact current reserves?” By altering the question to address an unknown outcome, the analytic can deliver greater value.
Insights that drive actions
Value-based analytics are comprised of analytics that spur action. For example, an analytic which identifies that 10 rental units in a residential housing complex will be vacant this month is informative, but it has little value if the residential housing complex usually has two or more vacancies. However, if the analytic not only identifies the 10 units, but also indicates that these same units have been vacant for 50 percent of the life of the complex, then concrete action — such as revised pricing, renovations or some other benefit focused on making the units more desirable — can be taken. If the insights obtained from an analytic prompt a call to action, then the analytic has value. If no actions are forthcoming based on the information, then the value of the analytic is minimal.
Never stop innovating
The methods and techniques for analyzing data are fairly new when compared to the real estate industry as a whole. The “data-centric organization” is a huge paradigm shift for most commercial real estate businesses. Companies today are still trying to understand the value of data and its impact on business. To assist in the paradigm shift from process-centric to data-centric, invest in data literacy programs that help your employees understand the power contained within data. Empowering employees to make informed decisions with value-based analytics improves upon the overall analytic investment as well as the operational and financial performance of your business.
Dean Boyer is a Director in the Technology Services Group at Marks Paneth LLP, a premier accounting, tax and advisory firm headquartered in New York City. He advises clients in an array of industries, including real estate, on how to harness data to increase operational efficiency and improve organizational performance. He can be reached at firstname.lastname@example.org or 267.768.3839.