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Improving Mulitfamily Sales Effectiveness Can Significantly Impact NOI

Measuring the value of sales (and sales improvements) in multifamily housing rentals is notoriously difficult. Except in very low occupancy situations, sales volume is “capped” by the number of units that can be rented. And, as I’ve noted before, metrics like “closing ratio” are highly contextual—as leasing agents succeed by leasing, their job should (and with revenue management systems, does) get more difficult as prices rise accordingly.

Yet no one disputes that sales efforts affect NOI. And with cap rates around 5-6%, there’s a 16-20X multiplier in value creation for each dollar of incremental NOI. With those kinds of numbers, it’s important to quantify the value of sales improvement as best we can so we can decide appropriate levels of investment in our sales models, sales coaching and sales training systems.

So here’s a methodology we’ve developed for doing such an assessment. Let’s start with some basic information about each community (really the average for a portfolio) to set up the problem:

  • Average size: in this example a “typical” garden-style size of 250 units
  • Number of agents per community: we’ll use the “1 for 100” rule and assume 2.5 FTEs per community
  • Average occupancy: used to calculate base monthly revenue, so we’ll use a “healthy” 95% for our example
  • Average rent per units: let’s say $1200
  • Cap rate: used to convert a change in revenue (and thus NOI) into a change in value. We’ll plug in 6% since we’re talking about a garden community in this example
  • # of communities in the portfolio: used simply to calculate enterprise lift versus a single community. We’ll use a low end of the NMHC top for this example with 100 communities (25,000 units)

At 95%, we’re not going to get much occupancy lift from “better” sales. In fact, as mentioned earlier, with a good revenue management system, we should see rents rise and force occupancy back towards the 95% range (note that this is a “ballpark” statement as exact optimal occupancy would depend on many other factors beyond the scope of this blog).

So what we want to do with this analysis is to estimate what that rent lift would likely be if we did improve our sales effectiveness. To do this, we need only 2 more parameters:

  • Price sensitivity: we need a way to turn “sales improvement” into rent increase. While we can’t actually lease more apartments, we can take the “extra” demand from better sales processes and convert them to an equivalent price increase using the pricing theory that defines prices sensitivity as the % change in demand given a % change in price. In other words, we want to “depress” the “increase in demand” from better leasing to keep the example occupancy at 95%, and we do so by raising the rent. Based on many years of working with LRO, I’m confident in using a price elasticity of -8.
  • Actual improvement. Let’s use a very conservative 5% improvement (to put this in context, if the average leasing associate completes 5 leases a month, that’s only 3 additional leases per year (less than 1 per quarter)

So now we’re ready to work through the math and see what a 5% improvement in sales effectiveness is worth. With the price sensitivity at -8, our 5% improvement assumption leads to a 0.625% average rent increase. Given 250 units, 95% occupancy and average initial rent of $1200, this will result in an increase of $21,375 per property. This converts to a $356K improvement in value given our 6% cap rate assumption. Across 100 communities, our example portfolio would thus increase in value by roughly $35 million (NOI would be up north of $2.1 million).

Of course a) your community and portfolio size will vary and b) you may want to assume different cap rates, different improvement rates and different price sensitivities.

What’s leasing improvement worth to you? We’d love to hear back from you with your assumptions and thus your results.


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Donald, I am not familiar with the price sensitivity factor you mention now how the -8 number is used to calculate the % average rent increase. Can you explain in more detail? Thanks, Pete Sisson

  Pete Sisson
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In pricing theory, it's a way of understanding how a certain change in price will affect demand. So the equation is beta (price sensitivity) is equal to the %change in quantity divided by the %change in price. This creates a logarithmic...

In pricing theory, it's a way of understanding how a certain change in price will affect demand. So the equation is beta (price sensitivity) is equal to the %change in quantity divided by the %change in price. This creates a logarithmic relationship between actual price and demand which has generally been confirmed by actual data.

I'm not aware of any actual test/control studies in MFH to calculate beta. However, pricing theory has proved that the higher the total price of something the more sensitive people tend to be to a price change (up to certain limits, e.g. I don't think a small % change in the price of a Lamberghini would really affect demand). I.e. a 10% change in the price of gum probably would affect demand less than a 10% change in the price of an apartment. In the former, $1 goes to $1.10 while in the latter $1000 goes to $1100.

So a beta of -8 basically implies that a 12.5% increase in price would call a -100% change in demand (i.e. shut demand off). In my example, I'm saying a 0.625% increase in price will reduce demand by 5%. It's an assumption--but with 15 years of experience in pricing apartments, I'm confident it's close enough to be useful for analyzing the impact of sales on NOI. You could, of course, use -7 or -9 and see how that impacts the answer. I hope that answers your question

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  Donald Davidoff

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