The self-storage industry is evolving fast, and one of the biggest challenges we struggle with is financial modeling today.

If you are in the acquisition business of self-storage, you have subscriptions to data platforms like Radius+, TractIQ, StorTrac or Yardi Matrix. We use the data to make assumptions such as rental rate, market health, how fast a project will absorb new square feet, etc.

Next, we plow those assumptions into Excel or something like the Storage World Analyzer and get some kind of financial model to predict the future.

This process has worked well over most of my career primarily due to the fact self-storage has many fewer variables than other asset classes like office, retail, or multifamily affecting the NOI line.

I spent years developing my skillset at this process and have made many good decisions using it (also a few wrong ones), raised millions of dollars over the years using it, and helped a lot of people make a good return.

It has served me well.

However, after reading an article by Kenidi Fay, who is a Marketing Coordinator at Radius+, I am starting to understand that this process has created a problem for us in today’s storage environment.

Without really realizing it, we create like a snapshot, albeit based on valid information and data, and design an entire future around it. Yes, we know intellectually things may change, but our strategy and decisions we make in running the project are all geared around the snapshot picture we made years ago using our financial model. Not to mention, investors lean heavily on the Proformas we present, as they should.

In the old days for the most part it worked for storage. Today I don’t think this process works so well.

Why?

There are just too many outside variables that change too rapidly for a snapshot to work.

We need something more like a movie, not a picture.

Trade Areas

One of the bottlenecks for self-storage today is that ….it…moves…very…slow.

Entitlements, design, fundraising, due diligence, not to mention loan approval, all move at a much slower pace than we expect.

I cannot tell you the number of instances that by the time a project we or someone I am helping developed, or even an expansion, by the time the space hits the market, the market, the rents and the demand have dramatically changed.

Our snapshot is no longer valid and we are working to get back to that model. Sometimes we can, but mostly we pivot and make in-the-field changes to the project, such as adding more parking, using aggressive rental rate adjustments to get bodies in hoping some will stay, selling, etc.

It’s not that our assumptions were wrong. The issue is the industry and markets change rapidly based on supply, revenue pricing practices (don’t get me started again), and the slowness of our asset due to the bottlenecks in our industry today beyond our control.

The Answer To The Issue?

The answer is not more data. We are swimming in it. It’s not even better data.

The answer is not improved dashboards. It’s not even new platforms.

I think the answer is going to come from AI.

I think the answer is some form of AI embedded in our existing platforms in such a way as to eliminate the friction between changing data and existing models (another bottleneck).

Rather than snapshots, our proformas can live and breathe on their own. As more historical data is gathered by AI, our Proformas are updated and delivered to us in real time so we can make adjustments and decisions based on the current knowledge of a trade area.

I see real-time pricing, monthly pricing trends, T-12 and T-25 historical averages for a trade area, facility-level pricing history, web and in-store rates simultaneously as they change all being able to be adjusted by our financial analysis platforms and automatically adjusted and updated and delivered immediately to us. Or perhaps we set the parameter to have it delivered quarterly.

Over time we will experience our Proformas as movies, not a historic snapshot of what we thought the future was going to be back when.

I have put the release of the updated Storage World Analyzer on hold and I am currently exploring how it would be possible to make this type of adjustment.

The reason is I believe the people who can make financial models closest to the actual market today, trade area by trade area, will be the winners. And each trade area is so different from the next, and even itself over time, a new type of model is necessary. I predict this type of financial modeling will emerge the winner.

So this is the game I am playing at this moment. Let’s see how it turns out.