space efficiency
What drove you to come up with a workplace efficiency formula?
It was mostly out of necessity. As a boutique firm, it was almost impossible to win business from the big brokerage firms. On the few occasions when I did win, I would engage an architect to help with the programming, space projections, and planning. The problem was the space plans they produced were rarely in line with their original space estimates. That killed our budget, and sometimes it even killed the deal. At the very least, it usually meant we had to step back and consider buildings in a lower price range to fit the square footage and stay on budget. When this continued to happen on almost every deal, I started working on formulas. It just didn’t make sense that the industry couldn’t be at least reasonably close in projecting space accurately.
How did you come up with the formulas?
Well, I started by simply redrawing space plans that seemed obviously inefficient, partially to gain knowledge and partially to see if I could reduce my clients’ rent. While I was doing this, I made it my business to fully understand different space planning situations, furniture options, workplace trends and tricks to plan in unusually shaped buildings. I also noticed definite patterns in layouts and design by some of the architecture firms that just didn’t make sense from an efficiency standpoint. Lastly, I started questioning what seemed to be random circulation factors that all the architects used. With all that, I developed a circulation formula based on the assumption that space was efficiently designed space, but also adjusted the required circulation based on the size of each area and the desired aisle widths.
But after testing that formula on a number of projects, it noticed it wasn’t working 100% of the time. I finally realized that every building has characteristics that make it either easier or harder to efficiently lay out space, and there needed to be some adjustment for that. So that led to a second algorithm that adjusted space projections based on each building’s relative efficiency. Both the original circulation algorithm and the building efficiency algorithm have since been awarded patents and are really the nucleus of the entire software application.
How does your quantitative analysis differ from what the rest of the industry is doing?
The first mistake that traditional space projections make is applying a circulation factor that really has no mathematical basis to it at all. I tell people all the time: “It’s not that it’s bad math, it’s that there’s no math at all.” It’s really an educated guess based on what’s happened in the industry for decades and decades, a rule of thumb. But just like the skin is the largest organ in the human body, the circulation area is by far the largest single space in any space program. But nobody has ever really questioned it because they assume that there is some architectural foundation for it. There isn’t.
The other half of the problem is another broad assumption that a tenant needs the same amount of space in all buildings. This is pretty far from the truth. I’ve worked with clients who originally were told they needed 100,000 rsf regardless of the building, and only to have our software show them that they actually need 79,000, 83,000, 86,000 and 91,000 rsf, depending on the efficiency of the various properties they’re considering.
Interesting, but aren’t there standards that regulate how much rentable space each landlord can charge for?
Kind of. There’s BOMA, and in New York there’s REBNY. The problem is that each time BOMA changes its measurement standards, it makes the difficult problem of accurate space projection even more challenging. That’s because it leaves tenants shooting at moving targets. You see, when BOMA releases a new measurement standard, not every landlord just picks up and re-measures their building. So when a tenant looks at a group of, say, 8 buildings as potential new locations, it’s not unusual that there are three or four different versions of BOMA being used to calculate square footages. This inability for tenants to compare buildings on an apples-to-apples basis gives landlords an unfair advantage. The other problem is that BOMA doesn’t consider many building characteristics that affect the architect’s ability to efficiently lay out space. A very simplistic way to look at this would be to compare two buildings that have the same square footage per floor, but one is a rectangle and the other is shaped like a football. BOMA considers them equal. But our building efficiency algorithms adjust our space projections to account for that building characteristic, as well as 14 other factors that negatively affect efficient space design.
How often does your process work for the typical office user?
It works virtually every time. I think the lowest efficiency we have ever been able to generate for a client is 9.8%, but that was strictly a redesign of a space plan. Ensuring efficient design is just one third of the process. If we are able to affect the three main efficiency disciplines, programming, building analysis and space planning, we’re usually in the 15-20% range.
Is it typical that tenants have undergone some sort of base efficiency process prior to working with you?
Yes. Most of the clients that we work with have some sort of corporate standards already in place that are supposed to ensure consistent, efficient occupancy across all their offices. The problem is that these corporate standards do not address space efficiency or accurate space estimating. It is absolutely possible to fully comply with corporate standards but still be 20% oversized. In fact, I might say it’s the norm. For example, a company’s standards might say you should be in a 10’ x 12’ foot office, but if there is a 10’ aisle around that office, it obviously isn’t going to be efficient. The corporate standards feature in our software addresses this by creating a space program, with accurate space estimates, simply by entering the desired headcount of each employee.
And for what size portfolio does this technology really shine?
Certainly, any tenant with a portfolio of a million square feet and up would be very interested at looking at how they could streamline their space acquisition process and cut costs dramatically. For single locations, it seems like tenants with 20,000 rsf or more are the ones that see how this really impacts the entire space acquisition process, from search to negotiations to occupancy.
Is it applicable both for occupiers as well as landlords and developers?
Yeah, but they use the technology in different ways. For example, we get hired frequently by owners who are trying to do a few different things to increase the ROI on their asset. First, they want to understand the relative efficiency of their building against its competitive set, basically so they know whether their building will take more or less space when it comes to test fits. Knowing that helps them avoid making unnecessary tenant concessions if their building is incorrectly viewed as ‘inefficient’.
The other way the landlords use our technology is to engage us in the estimating and planning process when they’re trying to attract a new larger tenant to their building. We’ve been pretty instrumental in helping a few different landlords close lease transactions from 20,000 to 200,000 rsf that they otherwise would not have gotten. If you think about it this way, if we’re able to prove a tenant needs even 10% less space in a $40 building, that’s the same at the landlord dropping their rental rate by $4.00 per rsf per year. That kind of concession just isn’t even on the table on most transactions.
And for occupiers?
Yes, this was actually developed with large tenants in mind. You know, the banks, the insurance companies, the technology companies. They clearly see the benefit because if we can prove they only need 85,000 rsf as opposed to 100,000 rsf without making any space concessions, it changes which buildings they can consider and also completely changes the negotiation strategy. One way or another, it’s going to impact their occupancy costs dramatically.
