Examples / Case Studies

We're new (founded in 2015), but first-hand results have started rolling in!


MNSC is owned and managed by Liberty Investment Properties, a privately held real estate investment, development and management firm focused on self-storage and hospitality. Since inception, they have developed over 40 self-storage facilities and 26 hotel properties.


The objective

Leverage data to help identify development opportunities with the highest latent, unmet demand, then lease-up as quickly as possible while optimizing rates.


The study


Customer analysis

We started by loading 8,768 of MNSC customer records across 16 sites (a simple rent roll, with name, address, duration of lease, total spent) into our platform, then matching as many as possible to our household demographic dataset. This produced an enhanced customer dataset for analysis (in other words, demographics were added to their customer records).


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Discovery of what makes the customer unique

We then compared the enhanced customer dataset to a large sample of households within a radius of each site, to understand what makes the customer unique. This produced stats and charts for exploration (click the chart to zoom in). In this example (on income), blue bars represent the income distribution of MNSC customers, red bars represent the income distribution of the local markets (for their 16 sites), and the yellow line represents the difference. When the yellow line is above zero, it is a demographic where they are proportionally outselling the market.


Computation of the demand model (for predicting households that will buy)

We also let the software do the heavy lifting of figuring out all of the combinations of income levels, marital status, duration of residence, dwelling type, household size, etc that set their customer apart from their local markets....to the point where we computed a 'predictor' that can be used to score any household in the U.S. to predict their likelihood of buying (in reality, the household's similarity to the traits that make their customer unique). If interested, we use machine learning algorithms.


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Applying the demand model to find unmet demand

We then studied the Orlando metro region to locate and quantify unmet demand. We applied the predictor to all of the households in the region and generated heatmaps to uncover the highest demand for storage. This reflects the density of households that scored the highest (even dense populations of the wrong demographic won't show up). We also leveraged data on competitive sites (with gross square footage) to account for the supply in the region. We then re-scored the demand at the household level to account for the amount of nearby competition. The end result was a guide on where to seek out development sites in Orlando that would give them the highest pricing power.


The exciting stuff....does it all really work?


Their latest facility: Lakeside

7902 Winter Garden Vineland Rd
Windermere, FL 34786

94,560 square feet
600 climate-controlled units
258-foot drive-through bay


Opened January 15th, 2016

Located right in the thick of an "unmet demand" hotspot.


Before they opened...

We identified the 30,652 households (with demographics) within a 5-mile radius (from our licensed consumer household dataset). Then we scored them. We did this to run direct mail and digital campaigns only to the highest-scoring households (series of mailers sent to the top 6,000 households).


If our calculations were correct...

lease-up would be fast (since located in a hotspot),
and actual customers would have high scores (thus receiving advertising)

Which would give us confidence in our predictor!


As of Apr 15th (first 90 days)...

230 leases (fastest initial lease-up for MNSC to date!)
In analyzing the rent roll:
   23% outside of 5-mile radius (other states, etc)
   8% are known businesses
   19% no match to scored file of households in 5-mile radius
   50% matched to scored file of households in 5-mile radius

For the matched customers (total of 115):
   46% in the top 10% of scored households
   67% in the top 20% of scored households
   86% in the top 30% of scored households


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