Centralize your operations with (Artificial) Intelligence
The NHMC Top 50 owners account for 10% of the US apartment stock. The percentage of institutional ownership of the market increases every year. However, most aggregated portfolios are still managed as individual assets which are traded as individual operating entities. The pressures of fund deployment and asset acquisition often result in increasing ownership but with a diminishing return for scale. Leading operators are increasingly looking to build a strategy that can leverage their scale and the blistering progress in Artificial Intelligence platforms like Adam, can now be capitalized on to utilize the benefits of scale and gain front-line intelligence.
One of the main benefits of building an operating platform that is powered by Artificial Intelligence is the ability to centralize and also customize without the need for ‘feet on the ground’. But in order to achieve this, rental operators need to make some fundamental strategic shifts.
Vision first, requirements second
‘Leaders also often think too narrowly about AI requirements. “
Building the AI-Powered Organization, Harvard Business Review
For most real estate operations teams, the idea of a centralized operations team seems ridiculous. To them, it takes away their insight from being on the ground and yet, a similar approach has been working for many single-family rental institutional operators for some time. To bring the real change you have to think differently. The biggest challenge an organization needs to overcome when trying to centralize its operations is to stop thinking about the existing organization.
Most operators will look at centralizing certain job functions like marketing, leasing, and possibly service dispatch. They use their understanding of existing jobs people occupy nad them try to centralize them. However, they are unable to imagine solutions for tasks or problems they are unaware of. Very little time and budget are given to defining the full scale of the problem. In order to successfully centralize operations and operate at scale, the business needs to use the benefit that their scale provides – DATA!
However, despite having the scale most organizations don’t have useful data to leverage and learn from. When formulating your AI Strategy, the worst mistake any operator can do is to apply a ‘bid based’ requirement created by the arbitrary demands of the business. First, you need to understand the full scale of your operations, understand the hidden facets of your business that you don’t know to exist.
An effective AI strategy will always start with capturing the right data. By doing so you will unearth the tasks you never knew your teams were performing and will discover both opportunities for centralization and problems that could derail the change. If you try to define requirements prior to data collection can result in the worst-case scenario where some jobs get centralized and others stay localized creating communication barriers, the unclear path of responsibility, and general corporate politics.
Institutional learning is a strategy
“At most firms that aren’t born digital, mindsets run counter to those needed for AI.”
Building the AI-Powered Organization, Harvard Business Review
The best operations teams are often led by experience-driven leaders who build a hierarchy for escalation for themselves. Starting the centralization strategy with the goal of collecting front-line intelligence allows for the human chain of command to be broken. Employees can now augment their judgment by using field intelligence to more transparent problems. Regional hubs can now become a place for problem-solving and decisions instead of fire-fighting. These decision teams can in fact be located anywhere and work iteratively on an ever learning and evolving operating platform. In order to make the most of institutional learning, the teams working with artificial intelligence should have a mix of skills and perspectives. It is important that operations, tech, marketing, and asset teams work together to address organizational learning and solutions in order to get the most value of the business’s scale.
Operations mean content and data
“Without data you’re just another person with an opinion.”
W. Edwards Deming
Property operators mostly consider the role of their operations teams to be reactionary. They are either working through the unexpected or following up on the ongoing. There is a precondition to describing the job as one of ‘legs on the ground’. While, there is no denying the importance of the field visits, follow-ups and inspections, there is a gaping hole in the industry towards the role of operations in content and data accumulation.
Centralizing operations teams can help remove some of the bias of opinion which interferes with scientific decision-making. Redefining the job description of operations to include data collection helps your scaling business to look at problem-solving as their job. The greatest impact of data collection and institutional learning is on content creation. Most operators today have unclear content strategies. It is often separated into marketing or community communication. Corporate communication teams prepare the content based on legal and marketing feedback but are often devoid of the detail that the field intelligence captures. Data does not make your teams smarter but it provides insight. The insight can identify a pain point and help your teams prevent it rather than reacting to it.
The appetite for change that needs a balance of feasibility, time and cost is found mostly in businesses that are looking to scale and build defendable IP for their industry. A large NMHC 50 operator needs to look at prioritizing long-term initiatives which are not embedded in software but in data. The scaling strategy for most rental operators is currently focused only on acquisition. It is glamorous and is described in millions of dollars invested. However, the challenge of operating these assets as a single brand is not a small problem to solve.