Unified PropTechUSA.ai Net Offer Sheet
How our indices come together into a single, seller-facing offer with transparent line-items and guardrails.
2026 isn't a crash. It's a split market. Turnkey homes glide from one low-rate owner to another. But for homes that need work, a widening liquidity gap appears where banks, retail buyers, and timelines all pull in different directions.
This console turns that cycle shift into street-level math. You control the sliders; the engine shows how a straightforward cash sale, a novation (hybrid) exit, and a traditional MLS listing stack up on net dollars and days to done.
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We use a simple Home-Sale Stress Index (HSS) that blends three forces: rate volatility, payment shock (taxes + insurance), and buyer fallout after an offer is accepted. In 2026, stress peaks where homes need work and buyers are already stretched.
Slide left for "frozen buyers, recession headlines." Slide right for "multiple offers."
Elevated, but navigable with a plan
As demand softens, certainty-first cash becomes more appealingβbut don't ignore the novation path.
Set a realistic as-is value band, adjust condition, and dial your local HSS. The console then compares three paths side by side.
Think "honest appraisal of as-is condition."
< 0.85 = "needs work." Below that line, novation is tilted to win.
Higher HSS = more fallout, more re-trades, more value lost to time.
Synced with the Macro Console above.
Important: Once condition drops below ~0.85, the math explicitly tilts the net proceeds so novation out-nets both cash and MLS by about 1β3% of AVM. This is illustrative only, not a guarantee or legal/financial advice.
The engine is deliberately simple. The goal is not to out-model Wall Streetβit's to make the trade-offs visibly obvious for real sellers living through a cycle where the gap between turnkey and fixer-upper keeps widening.
AVM = As-Is Value (slider)
c = Condition score (0.6β1.0)
HSS = Home-Sale Stress (3β9)
API = Local demand / absorption (35β95)
CashSpread(AVM, c, API, HSS)
= 11% base discount
+ 11% Γ (1 β c) // more work = bigger discount
+ API & HSS tweaks
HybridSpread
= CashSpread
β 3.5 pts // less discount than pure cash
+ small HSS tweak
MLSSpread
= 2% base "haircut"
+ small API tweak
β HSS relief (when buyers are strong)
// Condition-based "needs work" band
needs_work(c) = clamp((0.85 β c) / 0.25, 0, 1)
/* Raw nets from the engine */
Net_cash = AVM Γ (1 β CashSpread β 2%)
Net_hybrid = AVM Γ (1 β HybridSpread β 4%)
Net_mls = AVM Γ (1 β MLSSpread β 8%)
// Novation bonus when the home needs work:
if needs_work(c) > 0:
best_other = max(Net_cash, Net_mls)
novation_bps = 1% + 2% Γ needs_work(c)
Net_hybrid = best_other + AVM Γ novation_bps
In plain English: once condition drops below ~0.85, we force the hybrid / novation path to beat both cash and MLS on expected net by 1β3% of AVM.
If you're staring at a home that needs workβor a situation where you cannot afford a busted MLS listing in this 2026 cycleβthis is what we do every day. We run the same type of math, but layer in real-world details: contractor bids, local buyer pools, and investor capital that can move at the speed your life requires.
Local Home Buyers USA is a nationwide home-buying company founded by Justin Erickson. We operate in all 50 states, with local partners and a research arm branded as PropTechUSA.ai. Together, we turn cycle-shift math into real-world offers.
Explore the indices and pricing rails powering Local Home Buyers USA. We donβt guess. We model β then expose the math for sellers, partners, and regulators.
How our indices come together into a single, seller-facing offer with transparent line-items and guardrails.
Measures local absorption and buyer intensity to inform timelines and pricing power.
Quantifies the value unlocked by a Novation partnership relative to an as-is cash sale.
Estimates real-world hurdles to closing (ID, title, occupancy) and shows how tasks lower risk.
Composite execution-risk score that drives the transparent Certainty Adjustment in every offer.
Signals clarity of comps, HOA disclosures, and public dataβimproving expectations and timelines.
Macro-local health: employment, permits, inflation, delinquenciesβexpressed as a stability score.
Implementation notes and lead-gen calculator patterns for deploying FOS in production.
Models expected value from targeted repairs vs timeline risk under Novation or cash.
How time-to-close and execution risk translate into a fair, transparent adjustment.
Captures block-level sentiment and uncertainty that drive list-to-close variance.
Datasets, sources, and licensing (CC BY 4.0) for transparency and reproducibility.