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
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.
Shortest vacancy, mild rent reset, modest cap widening.
Longer vacancy, deeper rent reset, wider exit cap.
Extended vacancy, steep rent reset, materially wider cap.
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 fit each metric to a distribution across the scored universe and read where a deal lands. These are the real distributions, not illustrations: density on the vertical, the metric on the horizontal, one thin curve per cohort, n on each.
Asking cap rate by tenant sector
Asking cap rate by remaining lease term
Remaining lease term by tenant sector
Dark Shell composite by tenant-credit tier
Stress-case Dark Shell Score by tenant sector
"Where is this listing on the bell curve?" is the only question worth asking, asked of every metric.
Each curve is the gaussian fit to the cohort's real observations from the scored universe. Thin cohorts (n<4) surface as a mean tick rather than an asserted spread; nothing here assumes 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 We do not publish the proprietary weights, coefficients, or calibration that combine them.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
What the platform does not do
A model compresses the search; it does not replace the judgment. Shop 1031 reads every offering memorandum the same way, scores the downside the same way, and ranks the survivors against your exchange the same way, which is exactly what makes it useful: it removes the labor and the inconsistency from the part of the work that is mechanical. The part that is not mechanical it leaves where it belongs, with the people licensed and paid to carry it. Below is the honest inventory of what we hand back to you, and who you bring in to handle each.
Read this as the boundary line of the instrument, not a disclaimer buried for the lawyers. The deals that go wrong rarely go wrong on the math we automate. They go wrong on the judgment we do not, and cannot.
The political read on the deal
The memorandum tells you what the seller will put in writing. It does not tell you why the asset is on the market this quarter, which broker controls the relationship, whether the guidance price is a number or a wish, or how a competing offer will actually be weighed in the room. That intelligence lives in conversations the platform is not party to. A model that scored sentiment off an OM would be inventing it; we would rather say plainly that we cannot see it.
Bring in: your buy-side broker.
Walking the property
A 0–100 survival score is computed from documents. It does not stand in the parking lot at 7am, look at the condition of the roof and the seal of the dock doors, notice the vacancy filling up across the street, or register that the pylon sign is blocked by a tree the aerial does not show. Physical condition, deferred maintenance, and the texture of the immediate trade area are observed, not modeled. Order the inspection; send someone who has underwritten a hundred of these to stand on the site.
Bring in: a property-condition assessor and a broker who will walk it.
Renewal intuition
Dark Shell Scoring is built precisely because tenant intent is unknowable; it measures what your equity survives if the tenant goes dark, before the renewal story ever enters the model. That is its discipline, and also its boundary. Whether this operator actually renews, how the parent company is trending, what the store's four-wall sales look like, and whether the credit behind the lease is strengthening or quietly eroding are reads the platform does not make. The score is the floor under a bad outcome, not a forecast of a good one.
Bring in: a tenant-credit analyst or a broker with the operator relationship.
Environmental and physical title
A Phase I, a survey, an encroachment, an unrecorded easement, a use restriction in a decades-old reciprocal agreement: these surface in diligence, not in an OM, and they can move or kill a deal that scores well. The platform has no view into the ground or the title chain. Treat a strong score as permission to spend the diligence dollars, not as a substitute for spending them.
Bring in: an environmental consultant and a title attorney.
The exchange mechanics
Identification inside 45 days, closing inside 180, the qualified-intermediary structure, boot, debt replacement, related-party rules, and the continuity-of-intent posture that keeps the deferral intact are the difference between a closed exchange and a taxable event. Shop 1031 sizes deals to your equity and your timeline; it does not file your identification, hold your funds, or opine on your tax position. Those are licensed acts, and they are not ours.
Bring in: your qualified intermediary, your CPA, and your attorney.
Local market microstructure
Each listing is scored against its own asset-class and geography cohort, which is the right way to grade a cap rate. It is not the same as knowing the submarket: the rent comp that never reached a feed, the entitlement filed on the adjacent parcel, the road widening that changes access in eighteen months, the landlord two doors down quietly cutting rents. That texture is held by people who work the market every week. The score tells you where a deal sits in the distribution; the specialist tells you which way the distribution is moving.
Bring in: a local market specialist or appraiser.
Structure, financing, and the negotiation itself
How you take title, what the lender will require, which covenants bind, and what number the seller will actually take are settled in documents and in rooms, not in a model. Price is a negotiated outcome; the platform can tell you what a deal should be worth to you, and even recommend where to open, but it cannot read the counterparty or make the call when the counter comes back. That is the work you are paying a professional to do.
Bring in: your attorney, your lender, and your broker.
None of this is a hedge. It is the shape of the tool stated honestly. Shop 1031 takes the eighty percent of buy-side work that is normalization, underwriting, and ranking, and does it faster and more consistently than a person can, so that the twenty percent that requires a licensed human in the loop is the part that gets your attention. The platform earns its place by being precise about where it stops.
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.
A Hosios-optimal 1031 matching market
With the limits stated plainly above, the academic frame is context, not a sales pitch. 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.
References
The matching-market thesis rests on the search-and-matching lineage in economics. The Hosios condition is the keystone: it states when a decentralized matching market reaches the efficient frontier.
- Diamond, P. A. (1982). "Wage Determination and Efficiency in Search Equilibrium." Review of Economic Studies, 49(2), 217-227.
- Mortensen, D. T., & Pissarides, C. A. (1994). "Job Creation and Job Destruction in the Theory of Unemployment." Review of Economic Studies, 61(3), 397-415.
- Pissarides, C. A. (2000). Equilibrium Unemployment Theory (2nd ed.). MIT Press.
- Hosios, A. J. (1990). "On the Efficiency of Matching and Related Models of Search and Unemployment." Review of Economic Studies, 57(2), 279-298.