What would you say is the most important key metric in predicting self-storage demand and potential rent growth? Answers might include:
- Population growth,
- Population density,
- Median household income,
- Average household income,
- Supply per capita,
- Incoming residential construction,
- Incoming self-storage construction, and
- Percent of area that is renter-occupied.
These are all important metrics to consider when evaluating storage investment opportunities. Median household income is commonly used to evaluate the potential of a market. It’s intuitive to think that an area with a greater household income can support higher rents, right? Let’s look at the data.
As you can see from the Median Household Income vs. Street Rate PSF (10-By-10 NCC) chart, there seems to be some correlation between street rates for 10-by-10 non-climate-controlled (NCC) units and median household income. We’ve calculated the R-squared value to determine the strength of the correlation. If you remember from your statistics class, R-squared is a number that tells you how well one variable in a model explains variation in another variable. A 0 percent R-squared value means the variables are not correlated at all. A 100 percent R-squared value means the variables are perfectly correlated. The correlation between street rates and median household income has an R-squared value of 30.9 percent.
Although there is some correlation between these variables, it does make sense that median household income shouldn’t be relied on in predicting street rates. The beauty of self-storage is that it attracts customers from all walks of life and all income levels. Moving, downsizing, renovating, decluttering, and of course things like death and divorce, are all reasons people rent self-storage. These major life events impact customers at every income level.
What if there was a better key metric for predicting street rates than median household income? Median home value is often overlooked and not discussed within the industry, but it is a much better predictor of street rates within a market.
As you can see in the Median Home Value vs. Street Rate PSF (10-By-10 NCC) chart, median home value is not a perfect predictor of street rates, but it is much better than median household income. The correlation between street rates and median home value has an R-squared value of 55.7 percent, nearly twice as predictive compared to median household income. Why is this? Home values encapsulate a broader picture of an area’s economic stability, desirability, and long-term wealth accumulation. Areas with higher home values generally can support higher rates.
Although it’s helpful to understand street rates and how median home value correlates, the real prize investors are after is achieved rates. Uncovering what the actual achieved rate facilities are producing within a market is one of the great challenges investors face. Most of this information is private and extremely hard to get. The publicly traded self-storage REITs do publish some of their achieved rate data, but it is generalized into large MSAs. We’ve compiled achieved rate data from the REITs and compared it to street rates.
As shown in the Achieved Rate vs. Street Rate chart, street rates are an excellent predictor of achieved rates within a given market. The correlation between street rates and achieved rates is very strong and has an R-squared value of 83.0 percent. This may come as a surprise because of the varied pricing strategies across the REITs. REITs have robust revenue management teams that change prices quickly and often when demand fluctuates. Also, REITs tend to be most aggressive in their price discounting and promotions. With street rates changing so much, it is interesting that they are still a great indicator of achieved rates within a market, according to the data.
The Achieved Rate PSF vs. Median Home Value chart shows the correlation between achieved rates and median home value. Unsurprisingly, median home value is well correlated with achieved rates and has an R-squared value of 54.3 percent, nearly identical to the median home value/street rate correlation.
To summarize, we’ve determined that median home value is a better predictor of street rates and achieved rates than median household income. But how well correlated is median home value to occupancy? Let’s explore.
As you can see from the data in the REIT Occupancy vs. Median Home Value chart, the correlation between median home value and occupancy is very weak (R-squared value of only 4.8 percent). You will notice that between 90 percent to 95 percent occupancy, there’s a wide range of median home values. One market can have home values of less than $200,000 and have the same occupancy as a different market with home values over $1 million.
It makes sense that median home value is not a driver of occupancy in self-storage. Occupancy is more likely tied to factors such as:
- Incoming self-storage supply,
- Competition (different pricing, promotion, and discounting strategies), and
- Accessibility and visibility from a major road with high daily traffic counts.
What does occupancy tell us about rates (achieved and street) in a given market?
You will notice in the Achieved Rate vs. Street Rate vs. Occupancy chart that occupancy is not a good predictor of rental rates (achieved or street). The R-squared value between the variables achieved rate and occupancy is only 6.2 percent. For the variables street rate and occupancy, the R-squared value is only 8.0 percent. Although rates and occupancy are not well correlated, there are a few interesting things we can see from this data.
- There are very few instances where rates are above $2.00 per SF in a market with sub-90 percent occupancy.
- Ninety-one percent to 95 percent occupancy appears to be optimal for pricing as there are many instances where rates reach above $2.00 per SF.
- In markets that are 95-plus percent occupied, there are zero instances where rates crest the $2.00 per SF mark. If a facility is approaching 100 percent occupancy, there is likely money left on the table. When a facility is highly occupied, operators can afford to sacrifice a little occupancy and more than make up for the revenue lost by raising rates on existing tenants or achieving higher move-in rates.
In conclusion, data can be a powerful tool in determining what key metrics to focus on when evaluating self-storage investments. The biggest takeaways from this article are summarized below.
- Median home value is a much better (almost two times) predictor than median household income of the rates (street and achieved) a market can sustain.
- Despite the various pricing strategies among operators, street rates are an excellent predictor of achieved rates. If a market has lower street rates, it’s likely the achieved rates aren’t much better.
- Median home value and rates are not very correlated to occupancy. To predict occupancy, consider looking at key metrics like incoming storage supply, competition, accessibility, and visibility.
This article is not meant to conclude that median home value trumps all other key metrics. It’s important to look at data for every key metric to uncover the full potential of a prospective investment. Better data leads to better decisions.
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Noah Starr is the CEO of Tract IQ
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