The Plays

Signals are raw material. A play is a strategy that turns a specific stack of signals into a specific kind of deal. Each play below is data-winnable — meaning the edge comes from the engine, not from luck or a Rolodex.

Five plays, ranked by margin and by difficulty of the data. Then the call on where to start.

See also: the-deal-engine · the-intelligence-layer · signals-and-data · kpis-and-reports · deal-lifecycle · two-lanes

A) Off-market motivated sellers

Edge: highestData: hardestLane: both


Targets	Owners with a reason to sell before they list — and equity to discount
Data	Absentee/out-of-state + high equity + long hold + a distress flag (tax delinquency, code violation, vacancy)
Edge	No competition. You're talking before the listing exists. Margin lives here.
Difficulty	Hard — requires stacking owner + distress signals and skip-tracing contact

This is where the real money is. When you reach a tired absentee landlord with high equity and a code-violation notice piling up, you're not bidding against anyone — you're solving their problem. The data is the hardest to assemble, which is exactly why the margin survives.

B) Mispriced on-market listings

Edge: mediumData: easiestLane: cashflow


Targets	Listed properties priced below our modeled value
Data	Our AVM vs list price; stale days-on-market; price-cut history
Edge	Fastest way to validate the engine — listings are public and the feedback loop is days, not months
Difficulty	Easiest — on-market data is the most accessible signal we have

Lower margin than off-market, but the data is clean and the loop is fast. This is the proving ground for the underwriting-and-scoring engine.

C) Cashflow-market screening

Edge: mediumData: mediumLane: cashflow


Targets	Whole markets, ranked before any single property
Data	Cap rate + rent-to-price ratio by metro/zip; then hunt inside the winners
Edge	Pre-filters the universe — you only run expensive plays where the yield is structurally there
Difficulty	Medium — market data is free but ranking models take tuning

Not a deal-finder on its own; it's the funnel that points the other plays at the right geography. Top-of-stack to Play A and B.

D) Value-add rent-gap

Edge: highData: mediumLane: cashflow → equity


Targets	Properties renting below market where light reno forces appreciation
Data	Current rent vs HUD FMR / market comps; condition (age, permits); reno cost estimate
Edge	You manufacture the equity — forced appreciation isn't dependent on the market rising
Difficulty	Medium — rent-gap is computable, but condition data is fuzzier

The rent gap is the signal; the light renovation is the lever. Buy the gap, close it, force the value.

E) Path-of-progress

Edge: highest (long)Data: hardest to predictLane: equity


Targets	Zips about to appreciate — bought before the wave
Data	Leading indicators: new transit, rezoning, permit velocity, business openings, in-migration
Edge	Buy ahead of the crowd; ride the appreciation the indicators predict
Difficulty	Hardest to predict — you're betting on a trajectory, not a current number

The longest horizon and the appreciation play. Suits the equity lane, not the monthly-cashflow lane.

Difficulty vs edge

The five plays, mapped

A — OFF-MARKET Highest margin · hardest data · no competition

D — RENT-GAP High edge · medium data · forced appreciation

E — PATH-OF-PROGRESS Highest long-run edge · hardest to predict

C — MARKET SCREEN Medium edge · the funnel, not the deal

B — MISPRICED ON-MARKET Medium edge · easiest data · fastest to validate

Fig. 1 — Margin and data difficulty move together. Start where validation is fast, finish where margin is fat.

Where to start — the call

Don't chase the highest margin first. Prove the engine where the data is easiest and the feedback loop is fastest, then push into the margin.

Start sequence: B → A

Play B — Mispriced on-market

Accessible data, days-not-months feedback. Validate the underwriting + scoring loop on listings you can check against real outcomes.

Play A — Off-market motivated sellers

Once the engine underwrites and scores reliably, point it at the hard data where the real margin lives.

Fig. 2 — Earn the right to the hard play by nailing the easy one first.

The call: Play B first. Mispriced on-market listings have the most accessible data and the fastest validation — exactly what you need to prove the underwriting-and-scoring loop works. Once it does, push into Play A (off-market motivated sellers) for the real margin. B proves the engine; A pays for it.

Plays, buy-box, and the two lanes

Every play resolves to the same discipline: does the deal fit the buy-box? The buy-box is the per-lane filter — price band, target return, condition tolerance, hold horizon — that turns a scored lead into a "yes." A play surfaces candidates; the buy-box says which ones clear.

And the plays aren't one-size-fits-all across two-lanes:

Play	Cashflow lane	Equity / appreciation lane
A — Off-market	✓ discounted cashflow	✓ deep-value entry
B — Mispriced on-market	✓ primary	—
C — Market screen	✓ ranks for yield	partial
D — Rent-gap	✓ forces both	✓ forced appreciation
E — Path-of-progress	—	✓ primary

The point: the cashflow lane lives in B, C, and D — provable monthly yield. The equity lane reaches into D, A, and E — manufactured and predicted appreciation. Same engine, same signals from signals-and-data, different buy-box per lane.

Back to: the-deal-engine · the scoring mechanics live in the-intelligence-layer · what we measure in kpis-and-reports.