The Control Surface · Illustrative Model

Stabilized churn, as a system you can steer

Even a fully leased community never stays full for long. Each month a slice of leases comes up for renewal, and the residents who don't renew move out, so occupancy is something you re-earn every month rather than win once. Each move-out feeds a pipeline: it becomes a make-ready, then an available home, then a new lease, and each stage clears only so many homes a month. When move-outs outrun your turn and leasing capacity, vacant homes pile up even when demand is healthy, and holding renewals is the cheapest fix, because a vacancy costs 2-3 months of rent and a make-ready costs 1-2 months. This model runs the full chain through a Monte Carlo lifecycle engine to build intuition about each lever: the standing vacancy, the months of supply, and net revenue against gross potential rent. Move the three levers below and every curve recomputes live.

Operating levers

Share of expiring leases that stay. Lower → more move-outs into the pipeline.
Homes maintenance can make rent-ready per month. A staffing constraint, not seasonal.
Ready homes the leasing team can sign per month. Usually the tightest valve.
 

What your scenario does

Peak vacant units
vs base
Peak months of supply
vs base
24-mo net rental revenue
vs base

Projected vacant units

Standing vacancy over 24 months

Shaded band = P10–P90 across 400 Monte Carlo runs of the base case. Lines redraw with the levers.
MC range (base, 400 runs) Base case (P50) Your scenario

Months of supply

Vacant units ÷ leasing velocity. Above the dashed line, the pipeline is clogging faster than it drains.

Net rental revenue vs GPR

Gross potential rent minus vacancy loss at the GPR-implied $2,400/home.

The exogenous driver

Lease expiration schedule

Steady-state assumption: leases sit evenly across the year, so 1/12 of the portfolio (about 8.3 of 100 homes) comes up for renewal each month. The renewal lever decides how many of these become move-outs. A real portfolio carries a seasonal expiration curve; this model holds it flat.
Illustrative. Curves are the aggregate output of a portfolio lifecycle Monte Carlo engine run on a synthetic, anonymized 100-home community. Figures demonstrate the method; they are not a forecast of any specific property. No client or tenant data appears on this page.