Shop 1031
Methodology · public

Dark Shell Scoring

Sub-institutional 1031, underwritten.

DSS does not ask whether the tenant will renew. It asks what the buyer owns if the tenant does not renew: how much invested equity survives a dark shell.

By The Shop 1031 Research Desk · Updated

Scored to date
Scored listings
39
net-lease, modeled tenant-dark
Median asking cap
6.00%
across the scored universe
Markets
18
states with scored inventory
Median composite
67
downside-weighted · 0–100
DSS composite across the universe, best to worst

Each point is one scored listing. The dashed line is the 90 service-standard target.

Underwriting score · live example
Taco Bell
Decatur, AL · Quick-Service Restaurant · 5.25% cap
75 / 100
DSS composite · target 90
Base 100/100
Bear 82/100
Stress 60/100

Modeled per scenario; the dashed marker is the 90 target. Base looks strong; the stress score is where the dark-shell discipline shows.

The survival ratio

DSS normalizes a single quantity: how much of your original equity survives if the tenant vacates at lease expiration and you must re-lease a dark shell.

Survival Ratio = Net Terminal Equity ÷ Original Equity

Worked example · base scenario Slim Chickens · Aubrey, TX
Terminal value (re-leased)$3,316,791
Less: remaining debt($1,687,500)
Less: vacancy carry($68,344)
Less: re-leasing cost($116,017)
Less: sale cost($132,672)
Net terminal equity$1,312,259
Original equity$2,062,500
Survival ratio0.64×
Dark Shell Score (base)96

DSS = the survival ratio normalized to a 0–100 score, computed per scenario. Modeled estimate under stated assumptions; not an appraisal.

Three scenarios

Every property is scored across three vacancy scenarios. They differ by how long the shell stays dark, how far re-leasable market rent resets below in-place rent, and how much the exit cap widens.

Base

Shortest vacancy, mild rent reset, modest cap widening.

Bear

Longer vacancy, deeper rent reset, wider exit cap.

Stress

Extended vacancy, steep rent reset, materially wider cap.

Base 0.64× · DSS 96
Bear 0.39× · DSS 76
Stress 0.14× · DSS 57

Surviving equity as a fraction of capital invested (Slim Chickens worked example), per vacancy scenario.

The headline band is a downside-weighted composite of all three, so the stress case dominates without ignoring base-case resilience.

Where each metric sits on the curve

The same question, asked of every number on a listing: is this typical, cheap, or weirdly cheap? We model each metric as a distribution and read where a deal lands. The math is identical; the plain-English read changes per metric.

Cap rate

Typical, cheap, or weirdly cheap for the asset class?

$ / SF

Is the basis priced fairly, or are you overpaying per foot?

Lease term

How much runway does the lease have versus typical?

Credit tier

Is the tenant credit better, worse, or typical?

Days on market

Listed forever usually means the deal or the price is off.

NOI yield

What return are you getting on net operating income?

Rent bumps

Does rent grow with inflation, or are you locked at today?

DSS composite

All seven above, downside-weighted, into one score.

"Where is this listing on the bell curve?" is the only question worth asking, asked eight different ways.

Illustrative shapes. Each listing is scored against its own asset-class and geography cohort, not a single market median.

Assumption categories

We publish the categories of assumption that move a score. We do not publish the proprietary weights, coefficients, or calibration that combine them. Those are the model.

  • ·Vacancy period (months tenant-dark)
  • ·Market-rent reset vs. in-place rent
  • ·Exit-cap adjustment (widening)
  • ·Owner carry cost during vacancy
  • ·Re-leasing cost (TI + leasing commissions)
  • ·Sale cost on terminal disposition
  • ·Debt assumption (modeled debt, interest-only)
  • ·Tenant credit tier & remaining-term cushion

A Hosios-optimal 1031 matching market

A Hosios-optimal 1031 matching market is a 1031-exchange matching market designed so replacement-property supply is matched to exchanger demand at the efficiency frontier described by the Hosios condition in search-and-matching theory (Diamond 1982, Mortensen and Pissarides 1994, Pissarides 2000), minimizing search friction under the 45/180-day exchange deadline. Achieving the Hosios condition exactly is mathematically impossible in CRE because the bargaining set is not fully observable; we make the narrower, provable claim that by reducing search friction (letting buyers search against their actual need rather than against listing metadata) Shop 1031 approaches it. DSS is the friction-reducing instrument on the buyer side.

What DSS is not

DSS is a modeled point estimate under stated assumptions. It is not an appraisal, not a prediction of tenant behavior, and not a guarantee of capital preservation. Different buyer profiles produce different distributions; there is no universal "good deal," only good for your exchange. Verify all OM facts independently before transacting.