What This Score Represents
This is not an estimate of what your property is worth. It is the probability that your property will sell at its current price — derived entirely from what has actually happened to similar properties in your local market.
The SellOdds v7.2 engine analyzes every comparable sale and every comparable failure within your immediate area. Properties that closed. Properties that expired. Properties that were withdrawn or canceled. It measures where your price falls against that reality and returns a single, calibrated probability score — along with a full price elasticity curve showing how that probability shifts at every price point above and below.
When the score is high, the market data says properties like yours, priced like yours, are selling. When it drops, the data says they aren’t.
How It Is Different
Most pricing tools are automated valuation models — AVMs. They estimate a dollar value for a property based on tax records, public data, and regional averages. They tell you what a home might be worth. They do not tell you whether it will sell.
SellOdds v7.2 is not an AVM. It does not produce a valuation. It answers a fundamentally different question: at this price, what is the likelihood of a successful transaction?
Where traditional models rely on cached data refreshed on weekly or monthly cycles, this engine queries live MLS records at the moment of analysis. Where national platforms apply broad regional averages, this engine locks to your city, your property type, your size segment, and your quality tier. Where other tools treat a lakefront cabin and a fixer on a steep lot as interchangeable data points, this engine reads the listing narrative, classifies features individually, and measures their actual impact on sale outcomes.
There is no pre-trained model. No static index. No algorithmic estimate disconnected from what buyers are actually doing in your market right now.
What Data Powers It
Every calculation begins with live MLS data pulled directly from CRMLS — the largest MLS in the nation. Nothing is cached. Nothing is estimated from tax rolls or public records.
The engine identifies feature-matched comparables using structured MLS fields — bedrooms, bathrooms, living area, property type, geographic coordinates — and then goes further. It reads agent remarks to detect features that have no structured field: lake rights, lakefront access, walk-to-village proximity, remodeled kitchens, level lots, steep terrain, and dozens of other property characteristics that shape buyer behavior.
Comparables are filtered through multiple layers of data integrity checks. Non-residential properties, manufactured homes, vacant land, commercial listings, and lease agreements are excluded. Economically irrelevant outliers — properties so far from the subject’s price range that they would distort the analysis — are removed. Structural anomalies are identified and isolated. Only verified, habitable residential transactions that are genuinely comparable enter the scoring model.
The result is a clean, curated dataset of real outcomes — not a raw data dump averaged into a number.
Full empirical data — including comparable details, feature impact breakdowns, and market leverage analysis — is viewable once you sign in and agree to the VOW IDX compliance terms.
Why It Matters
Pricing a home is the most consequential decision in a real estate transaction. Price too high and the listing stalls — accumulating days on market, attracting lowball offers, and eventually expiring. Price too low and the seller leaves money on the table.
This engine gives agents and sellers something that dollar-value estimates cannot: a direct reading of market tolerance at a specific price point. The interactive price elasticity curve shows exactly how probability responds as price moves — not in theory, but based on the exposed outcomes of every comparable transaction in the local market.
The system also accounts for factors that static models ignore entirely. A property that failed to sell twice before carries that history into the analysis. Extended days on market are detected and reflected. Properties are compared within their own quality tier — an updated home is measured against other updated homes, not averaged against fixers. Size segments are analyzed independently because a 650-square-foot cabin and a 2,200-square-foot home operate in fundamentally different price-per-square-foot realities.
This is pricing intelligence grounded in what the market has actually done — not what an algorithm thinks it should do.
About the SellOdds Engine
SellOdds represents a fundamental shift in real estate pricing — focused on a single objective: translating live market outcomes into actionable pricing intelligence. Built for professionals, the engine is designed for use in listing presentations, pricing strategy sessions, and seller consultations.
The v7.2 engine incorporates dynamic market calibration, quality-tier-aware comparable analysis, feature-level impact measurement, hyperlocal geographic boundaries, and multi-layer data hygiene — all operating against real-time MLS data rather than cached snapshots or pre-computed indices.
SellOdds is not available as a consumer tool. However, Gregory Anderson — developer and proprietor of MountainCribs.com — has integrated the SellOdds engine into this platform as a buyer-facing, added-value feature, bringing professional-grade pricing intelligence directly to the property search experience.
