Analytics can provide a wealth of information that can also feel overwhelming at times. How can that deluge of data be turned into actionable tasks to streamline efficiency and reduce costs?
In a CRE Talk at NAIOP’s CRE.Converge in Las Vegas, Dennis Norton, analytics manager, Wipfli LLP, shared common use cases and success stories of multifamily projects that have best leveraged data for critical decision-making. He explored opportunities for optimizing performance through cost reduction and improved operational efficiencies.
For commercial real estate, data analytics can be applied to areas such as rent roll, financial analysis, property management, tenant quality assessment, and location analysis.
Among the benefits of data analytics that Norton shared:
- Data analytics helps us get insights faster.
- Data analytics allows us to contextualize information, making it actionable.
- Data analytics provides ongoing access and transparency enabling awareness and accountability.
- Data analytics allows for automating tasks or processes to increase efficiency.
A key part of the process of harnessing data is considering your company’s analytics roadmap, according to Norton. There is a long pathway from standard reports up to machine learning; where is your company now and where do you want to go? Do you want to do predictive modeling? Do you want statistical analysis? What are your competitors doing?
Norton shared the example of an employee named Bill who gathers excel sheets and assembles the information for a report. This is recurring time (and money) spent on retrieving the latest inputs, and there is a delay in providing that information to everyone else on the team. This stage is the most labor-intensive and provides the least sophisticated intelligence as it is focused on descriptive analytics, exploring “what happened?”
The next state of sophistication of intelligence addresses predictive questions like, “Why is this happening? What will happen next?” This stage includes forecasting, statistical analysis and predictive modeling.
Next is prescriptive analytics, which includes experimental design and optimization, and addresses, “What happens if we try this?” and “What’s the best that can happen?” At the upper end of the sophistication level, and providing the most sophisticated intelligence, is autonomous learning. This stage employs machine learning to answer the question: “What can we learn from this data?”
As a sample use case, Norton shared that a large rental residential real estate developer in the U.S. was looking for ways to improve the process used to develop and share a critical weekly report. The existing process did not allow for understanding asset performance in a timely fashion. They established a goal of automating their weekly report and also developed an analytics roadmap of how to define data strategy and align it with their operational objectives.
There is a lot of hype about the ease of automation in the market, Norton said, but again, it’s critical to establish the company’s goals and game plan before jumping headlong into implementing machine learning and other kinds of advanced analytics. The single biggest reason analytics initiatives fail is lack of planning, Norton said. On that note, it’s okay to take baby steps when evaluating current and ideal analytics goals.
This post is brought to you by JLL, the social media and conference blog sponsor of NAIOP’s CRE.Converge 2024. Learn more about JLL at www.us.jll.com or www.jll.ca.
