Storage occupancy, rental demand, billing and delinquency, and lien auctions
Industry
Self-Storage (multi-facility operator)
Company size
22 storage facilities with 6,681 rentable units across six regions
Packaged scale
About 150K rows across 7 Starter-tier tables, including 8,000 rentals and 460 lien auctions
Revenue scale
Roughly $18.2M billed across rent and tenant protection, about $9.1M per year at the current run-rate
Occupancy
Near 81 percent physical occupancy, holding within a two-point band
Tenant mix
Residential households lead, then small business, students, vehicle and RV storage, and military relocations
Pinhollow Self Storage is a fictional multi-facility self-storage operator created for synthetic BI analysis. The company runs a regional network of drive-up and climate-controlled storage facilities, and the data tells the everyday story of that business: units filling and emptying, tenants moving in and out, rent getting billed and collected, a slice of accounts falling behind, and a small number of severely delinquent units going to lien auction. None of the facilities, tenants, or staff are real.
These are the operating questions this kit is optimized to answer first.
Occupancy spreads about four points across sites but only about two across size classes, so the size-class story lives on rate.
The occupancy snapshot rolls up the same monthly billing ledger, so dashboards trace network figures down to unit-month detail.
Move-ins peak in summer and trough in winter while occupancy barely moves, because move-outs run at nearly the same pace.
Autopay runs about a third the delinquency of manual payers, late fees hit only past-due invoices, and every lien is its ledger's arrears.