Self-Storage Market Conditions • Section 13 As
investor interest in the self-storage asset class continues to increase due to superior returns, the competitive landscape also increases. And, as com-
petition increases, the level of sophistication of investment decisions increases.
In “Self-storage Economics and Appraisal,” market condi-
tions is outlined as the core of self-storage economics. It is described as an analysis of the market conditions that affect value using both qualitative and quantitative techniques. One tool, benchmarking, can be a starting point of analysis. For example, a measure of the total self-storage supply per person in the local trade area can be benchmarked to core- based statistical area (CBSA) data published by the Almanac. Another tool, the Cost of Occupancy, can measure rents as a ratio of average household income to CBSA data also pub- lished in the Almanac.
Moreover, as the industry continues its mainstream maturation, and
product awareness on its own grows the demand side of the economics, a greater percentage of the tenant base at a given facility will source from within a much tighter radius than three miles.
CBSA Analysis The CBSA table (13.1) on pages 125 and 126 can be used for comparisons and benchmarking; however, it does not address local self-storage market conditions. Studies and research have shown that demand for a typical self-storage facility is local. On average, most facilities draw at least 75 percent of customers from within a three-mile radius. More- over, as the industry continues its mainstream maturation, and product awareness on its own grows the demand side of the economics, a greater percentage of the tenant base at a given facility will source from within a much tighter ra- dius than three miles. This is especially true in urban markets and in high-density suburban markets where customers may come from inside a 1.5-mile radius. Add to that the reality that demand for self-storage is difficult to induce from out- side the local sub-market trade area, finite due diligence on a specific trade area is paramount to success. It is important to understand the general market characteristics within the CBSA and then reduce the apparent demand behavior within the micro local trade area specific to the subject property. Supply data by CBSA comes directly from the proprietary da- tabase of Radius+ with known self-storage locations based
upon latitude and longitude confirmations. The Radius+ da- tabase also includes actual square footage data; therefore, the square footage contained in the Almanac is not reported on a site-specific basis rather than on an industry average.
Determinants of the self-storage market relate to the forces of supply and demand, as is the case with other types of real estate. The analysis of demand generators, however, is fo- cused on four key variables:
• Population • The percentage of renters • Average household size • Average household income
A simple econometric model can be used to estimate
self-storage demand. Exhibit X shows the results of regres- sion analysis using a proprietary model registered with the Library of Congress. However, this data can be easily dupli- cated in spreadsheet software or statistical packages. In the multiple regression model, the dependent variable is square feet of self-storage per person. The independent variables are the demographic variables by CBSA: population, per- centage of renters, average household size, and average household income. Testing these variables for relationships and rank indicates a moderate correlation with a multiplier coefficient of 0.5079 and an r-squared of 0.2580. Comparing existing supply to demand can be used as a benchmark to determine if a CBSA is under-supplied, over-supplied, or at equilibrium.
Cost of Occupancy Analysis As a test of reasonableness, the cost of occupancy by CBSA based on market rents (average annual unit price of the mar- ket rent divided by the average household income of the trade area) was calculated. As an example, if an average unit rent is $100 a month or $1,200 a year, and average house- hold income is $60,000, the cost of occupancy is 2.0 percent. For self-storage, trade areas below 2.5 percent generally have room to improve rental rates through revenue en- hancement. The CBSA data is skewed downward from trade area analysis because of outliers or rents that are included in more suburban or rural markets. In a local trade area, a two percent cost of occupancy is considered good, with room for revenue enhancement. In most trade areas, rents peak near 2.5 percent. However, in urban markets we have seen a cost of occupancy exceed three percent. The cost of oc- cupancy is a test of reasonableness of market conditions on a relative basis to other CBSAs. In practice, the cost of occu- pancy in the local trade area is best. Take note that complex algorithms or pricing models are dynamic, and asking rents can change 24 hours a day, seven days a week. Therefore, the cost of occupancy should be considered a benchmark. The data is shown in Table 13.1a on page 125 and Table 13.1b on page 126.
2021 Self-Storage Almanac 123
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