The Power of Data - Move-In Move-Out Equilibrium or Conundrum

The Power of Data: The Move-In Move-Out Equilibrium, or Conundrum?

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We continue in our series of blogs on the power of data. We looked in the past at new customer move-ins and existing customer move-outs revenue per square foot. As there is a certain equilibrium between new customer move-ins and existing customer move-outs, we now look at the two key performance indices (KPIs) together, along with existing customer revenue per square foot.

Centered on revenue per square foot (based on total square feet of a facility), we look at the following:

  • New customer move-ins.
  • Existing customer move-outs.
  • Existing, occupied units.

Dashboard Graph Example

Taking all the KPI’s together, we can observe the following.

Only new customer move-ins exhibit a possible cycle.

Given that the data only covers 14 months, we can only hypothesize. We note in our previous blog, The Power of Data: Understanding New Customer Move-Ins, that January 2021 and 2022 both had dips in new customer move-ins (revenue per square foot). We looked into factors of how we can mitigate those dips.

At the same time, the focus on January provides some context for looking at the overall picture, as we shall see.

Existing occupancy revenue per square foot steadily increases over time.

This is a good trend, as long as it exceeds that of inflation. Because of its almost straight-line profile, it visually forms a reference point for new customer move-ins and existing customer move-outs.

What’s really happening is that we see that both new customer move-ins and existing customer move-outs revenue per square foot almost always exceeds that of occupied units. Higher new customer move-ins revenue per square foot serves to “lift” the occupied units trend line, while existing customer move-outs exerts a downward pressure. The result of the two nets out an overall increase of occupied units over time.

In order for trend line to increase, new customer move-ins need to be higher the existing customer move-outs.

Remember, these KPIs are in revenues per square foot. A smaller unit may yield less rent per month than a larger one; however, on a per square foot basis, it may be larger. And vice-versa. So, new customer move-ins dollar per square foot, on average, needs to exceed that of move-outs dollars per square foot. This, in order to move to occupied dollars per square foot upward over time.

In the graph above, we see that there are three months where move-outs exceeded that of move-ins: January 2021, March 2021, and February 2022. There are important months to consider, because these are the months that exert downward pressure on the overall occupied units trend line.

If move-outs continue to exceed move-ins in greater frequency and difference, then the occupied units trend line may very trend downwards.

A focus on winter months?

We note that all three months happen in the first quarter. Winters may be “slow” months. This corresponds with new customer move-in dips in January, mentioned earlier. Consider adopting a two-pronged strategy of reducing the “dips” of new customer move-ins while simultaneously mitigating customer move-outs.

To reduce the dips of new customer move-ins, we had discussed considerations such as:

  • Are end-of-year holiday specials being left on too long?
  • Are move-in concessions being offered too frequently or too aggressively?

To reduce customer move-outs, we had discussed the following:

  • For the winter months, are price increases too aggressive to cause undue move-outs?
  • Are there particular unit types that are more sensitive to move-outs, where reduced price increases can be more targeted?

The Power of Data.

By proactively looking at the data and taking relatively small, targeted and disciplined price adjustments, you can “build” your existing occupied revenue per square foot upwards. You can see how this trend line, over a relatively short period of time, can increase your overall revenues dramatically.