As commercial real estate copes with the ramifications of COVID-19, collections of April rent surprised on the upside for office, industrial and multifamily at around 90 percent. Still, the prospect of rent shortfalls in May, an economic recession threatening fundamentals, and a record amount of dry powder waiting to be deployed to buy distressed assets has caused anxieties to remain high.
Now, the pressure is on for operators and asset managers to cut expenses, and to do so quickly.
When the clock is ticking, it’s tempting to make broad changes that undeniably have the desired effect. Even before COVID-19, many properties were run this way. If a tenant needed overtime HVAC for a couple hours, operators would run the systems 24 hours a day to make sure they were covered.
But following the same approach with budget cuts is like using a sledgehammer instead of a scalpel to remove a pinky. It will certainly have the desired result, but it carries a significant risk of collateral damage.
A full audit takes time that portfolios don’t have. Cash flow is under threat today and any decisions are better than no decisions at this point. Unfortunately, this often means letting people go. While this sledgehammer approach effectively reduces costs, it also eliminates institutional knowledge that has been built up over decades and cannot be replaced.
On the other hand, data can serve as a scalpel, expanding the options for which operating expenses to maintenance service contracts, parts inventory, CapEx reserves, and utilities. This allows operators and asset managers to make cuts that don’t undermine long-term performance.
Of course, the value of data is conditional on the actions that can be taken. To do that, you need to be able to quickly surfaces and test hypotheses. In our efforts to help clients quickly make decisions on where to make precision budget cuts, we had a handful of early hypotheses:
Hypothesis #1: There is a very wide discrepancy between your best engineer and average engineer
Without thinking too hard about it, asset managers would probably assume that the performance of their building engineers is normally distributed. There are a small number of very high performers and an equivalent number of very low performers, with the bulk of engineers clustered near the average.
But the world doesn’t work like that.
Like in countless other professions, performance follows a “power law” distribution instead of a normal distribution. There are a small number of engineers who are "hyper high performers," a broad swath who are good performers and a smaller number who are low performers.
This means two things: There is a much wider variation in performance than assumed and the small number of hyper performers account for a very high percentage of total business value.
These hyper performers are doing everything right – running equipment on a tight schedule, adhering to preventative maintenance plans, limiting the need for expensive third-party contractors, and constantly evaluating the market for technologies and investments that promise an attractive return.
Their talents add hundreds of thousands of dollars to net operating income by running their buildings for less than their peers. The trick, and the job of data, is to identify these hyper performers based on empirical evidence, not perception, years of experience, or assumptions about normal distribution.
When things do return to normal, the goal should be to democratize the best practices of these hyper performers. But this will be impossible if hyper performers are caught up in sledgehammer-style budget cuts.
Hypothesis #2: There’s revenue other than rent being left on the table
In addition to heavily scrutinizing every dollar going out, operators and asset managers should consider evaluating creative ways bring more dollars in.
For example, one of the reasons that office landlords feel powerless to lower operating expenses is that net lease tenants control their equipment and pay their own operating expenses.
But what happens when base building equipment operates because one tenant runs their systems 24 hours a day, while others do not?
Depending on the lease, it may be possible to bill-back that one tenant for overtime HVAC usage. While relatively small compared to total rental income, this can amount to an additional $25-30k annually for a mid-sized office building.
Data unlocks this scalpel approach. With equipment performance data, even a non-technical property manager could quickly identify the tenant, calculate precisely how much it is costing, and provide evidence to back up the claim.
Hypothesis #3: Even the most basic information is difficult to get a hold of
Of course, making scalpel-style decisions is aided by granular building data that some properties may not have. While sensors are easier and less expensive to install than ever, it’s about leveraging the data available today, not the data that you wish you had.
Any data is better than no data. Unfortunately, things as simple as maintenance contracts, purchase orders, parts and asset inventories, and utility bills cannot be accessed by those who don’t work in the building. Now that many assets are being operated by skeleton crews, it has become even more difficult to get responses to emails requesting this type of information.
Budget cuts are tough enough to make with all the relevant information at your fingertips. It’s not enough that this data exists, it must be accessible to everyone in the organization.
This is where data visualization comes into play.
Over the past three years, many asset managers have invested in business intelligence dashboards to be able to analyze rent rolls, demographics, leasing activity and other important metrics on the fly. But most are still flying blind when it comes to benchmarking their portfolio when it comes to the drivers of OpEx.
It’s not about understanding the details of engineering, it’s about pulling basic information on-the-fly during tough budget meetings and making smart decisions quickly. This can be accomplished even in this tough environment and before cuts are made.
Conclusion
Back in the good old days of two months ago, revenue was growing enough to offset inefficiencies on the expense side of the balance sheet. Now, despite the good news from April rent collections, most properties simply don’t have the runway to make the mistakes that they could previously if they want to weather the storm.
When making cuts, salaries are always going to be a major source of costs. However, organizations should be, with whatever data is available, aiming to make strategic and defensible reductions instead of broad and evenly distributed cuts. Hyper performers are extremely difficult to replace and each one lost will cause much deeper pain down the line.
In addition, it’s going to be important to start thinking about additional sources of revenue to rent. Obviously, this will have to be done very carefully based on the financial situations of tenants, but things like overtime HVAC billing can add a revenue stream that helps ease the burden of budget cuts.
Finally, any indication that decisions are being made without the full picture available should be addressed immediately. This doesn’t necessarily mean installing sensors, it can be as simple as getting all contracts, bills, and purchase orders in a centralized database that can be sliced and diced at a moment’s notice.
Enertiv is here to help. We specialize in creating asset value by cutting operating expenses, regardless of what data you have. Schedule a demo today.
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