We're Changing the Game.

Real Estate Will Never Be One Market.

You're either preparing to list your property, or perhaps considering a purchase of one. A city, a suburb, a lake town, a mountain community: none of these is a single market, and none of them behaves the way the AVMs (Automated Valuation Models) tell you. In fact, they all disagree with themselves in plain sight. The truth is, two homes with the same bedrooms and the same square footage can share a zip code and almost nothing else. A historic-district designation moves the price one direction. A road-association assessment moves it another. Lot configuration, school boundary, septic versus sewer, walking distance to the village center, winter access: every one of these is a number that moves the price, and none of them is what a guesstimate sees.

Many properties don't fail because they couldn't sell. They fail because they were priced or positioned without the data that would have told you what was likely to happen. What you're about to read is the data system I built so that no buyer or seller has to make this call blind anymore.

Built from the Ground Up.

The engine that runs underneath this site is called Sell Odds. It is a valuation and probability system I spent eighteen months building, because the industry has spent decades answering only half the question. Worth is one number. Whether a property will actually sell, at a given price, against the empirical record of what the market has already done, is a different number entirely. Sell Odds answers both. It estimates what the property is worth, then estimates the probability it sells at the price you put on it.

Sell Odds is built on the California Regional MLS, the largest MLS in the United States. The value is built from closed sales. The odds are built from sold and unsold outcomes only. Active listings are excluded because price on the market proves nothing about market acceptance. Pending listings are excluded because they have not closed. What's left is the empirical record of what the market did: what sold, and what came to market and ended without a sale. Eight filtering gates run on every score. A price elasticity curve runs underneath each one.

Sell Odds began as its own product, separate from any single market. The lab, the proving ground, and the full development story all live at sellodds.com. The engine runs the same way across the entire CRMLS, with no thumb on the scale for any city. Same math. Same data discipline. The output is the number you see in the Crystal Ball.

See the Odds Before You Decide.

To see it in action, find any property on Sell Odds and click Get Sell Odds Probability Score. The Crystal Ball runs Sell Odds against the comp set selected for that property and returns the result.

This Is Not a Guesstimate. It's a Tested Model.

I built this system from the ground up to answer two questions with the discipline each one deserves, from the empirical market data of the California Regional Multiple Listing Service. No guesswork. No borrowed estimates. No number that can't show its work.

The value starts where an appraiser starts: the most comparable recent sales, each adjusted to the property for market movement since the sale, for differences such as size, rooms, age, lot, garage, pool and view, and for location. Then the engine checks itself against independent methods, including a gradient-boosted machine learning model trained on two years of sales across the CRMLS and a feature-pricing model of what buyers in that zip code pay for each feature. Each method is weighted by how accurate it has proven on homes like this one, and a final model corrects for what they tend to miss. The valuation architecture was informed by the published technical overview of the Quantarium Residential Valuation Model. Every value carries a range and an estimate strength, and when the evidence is thin, the page says so.

Every probability you see is derived from actual closed sales and actual market failures around the property, and it is tested on listings it has never seen. If it can't beat a simple local average, it doesn't print.

Eight Filtering Gates. Zero Exceptions.

Every property gets its own custom analysis based on where it sits, what it is, and what the surrounding market has done. Here's what the engine runs before it produces a single probability.

See How Price Affects Probability. Across the Range.

Move the slider across the home's price range, from the low end of the value's range to the high end, and further out when the list price sits outside that range. The Crystal Ball shows the probability at every price it can score, with the same tested model behind every price. You can see exactly where the probability of sale begins to drop and how sharply it falls.

This is price elasticity made visible. Most consumers have never seen what happens to a property's odds when it's overpriced by $10,000 or $20,000. Now they can.

Comps You Can Actually Trust.

Every agent talks about running a CMA, a Comparative Market Analysis. They pull it together in your living room the day they're trying to win your listing. The problem is that running comps properly is an appraiser's discipline. It requires structural matching, geographic precision, time-window controls, and an understanding of which properties are actually comparable and which ones just happen to be nearby. Most agents don't have that discipline. They have the data. What they don't have is the tooling to turn it into anything useful. So too often, what you get is photos of houses down the street with similar bedrooms and bathrooms, presented as analysis.

This is a PMA. A Probabilistic Marketing Analysis. Built from data, not photos. It took eighteen months to build the engine that runs it, a system that values the property from closed sales, applies defined, auditable comparable-sale rules, and produces a tested probability of sale wherever the evidence supports one. Not a guess. Not a vibe. Evidence.

On any property, How the value and the Sell Odds score were worked out shows the sold comparables the engine chose for it, mapped around the home. Each one is adjusted to the subject line by line: market movement since the sale, living area, lot, year built, bedrooms, bathrooms, garage, pool, view and location. You see every comp's weight and its distance from the home. Sold data, auditable rules, on demand, free.

Then there's the data the engine uses that the public never sees.

Who Has the Power. Buyers or Sellers.

Every property page carries a Market Friction Index that measures the current balance of power in your specific market. It reads four independent drivers against a normal market, across every attempt to sell that ended in the past twelve months, and shows you, driver by driver, which side of the table holds the advantage and by how much.

The city view runs free on every property page. The hyperlocal view, the readings right around the property you're looking at, opens through a client login under CRMLS VOW rules, and it's part of every in-home analysis. The delta between the city and the hyperlocal reading can be significant, and that delta is the entire reason this conversation matters.

Sourced. Verified. Unmanipulated.

Every sale, every listing and every outcome comes directly from the California Regional Multiple Listing Service, the largest MLS in the United States. The records are not scraped from consumer websites. They are the same records licensed real estate professionals use to price, market, and sell homes every day. Sell Odds uses them to generate its value and probability estimates. The property's own record is read live the moment you open it. The valuation models are rebuilt every week from the newest closings and tested against the last 90 days of sales across the CRMLS. Every score is calculated fresh, for the property you're looking at, at the moment you request it.