"Because what a guesstimate tells you and what a property will actually sell for are now two entirely different conversations."
You're either preparing to list your property, or perhaps considering a purchase of one here. Pasadena, South Pasadena, Altadena, Sierra Madre, San Marino, Alhambra: 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, a Craftsman in Bungalow Heaven and a Craftsman in Altadena share an architectural style and almost nothing else. A historic-district designation moves the price one direction. The Mills Act moves it another. Lot configuration, school boundary, walking distance to the village center, foothill aspect: every one of these is a number that moves the price, and none of them is what a guesstimate sees.
Most 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 in the San Gabriel Valley has to make this call blind anymore.
The engine that runs underneath this site is called Sell Odds. It is a probability system I spent a year building, because the industry has spent decades answering the wrong 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 is built to measure the second one. That is the only question it asks.
Sell Odds runs on a live connection to the California Regional MLS, the largest MLS in the United States. It uses 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 only empirical record of what the market did and did not pay. Eight filtering gates run on every score. A 201-point 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. What runs on Arroyo Casa is the same engine, calibrated to the cities I serve in the San Gabriel Valley as a REALTOR. Same math. Same data discipline. The output is the number you see in the Crystal Ball.
To see it in action, find any property on Sell Odds and click Get Probability Score. The Crystal Ball runs Sell Odds live against the comp set selected for that property and returns the result.
What the score means. The percentage inside the Crystal Ball is a direct measurement: out of all comparable properties in the immediate area listed within this price range, this is the percentage that successfully closed versus the percentage that failed. A high number means the market has a strong track record of closing at this level. A low number means properties in this range have a history of sitting, expiring, or being pulled.
The Crystal Ball doesn't tell you what to do. It shows you what the market has done.
I built this system from the ground up to do one thing: measure the real probability of sale at any given price point using only empirical market data from the California Regional Multiple Listing Service. No guesswork. No estimates. No black-box algorithm generating a number no one can explain.
Every probability you see is derived from actual closed sales and actual market failures within the immediate area.
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 number.
Move the slider across the price range and the Crystal Ball recalculates at every point: 201 individual price points, each computed in real time. 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.
Every agent talks about running a CMA, a Comparative Marketing 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 don't have access to the underlying data, and even if they did, they don't have the tooling to turn it into anything useful. So 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 a year to build the engine that runs it, a system that queries the MLS live, applies appraiser-grade filters, and produces a real probability of sale at any given price point. Not a guess. Not a vibe. A measurement.
When you open Comps and Analysis on any property, you see the sold comparables the engine used to calculate the probability score. Each comp carries a confidence score showing how closely it matches the subject property: square footage, bedrooms, bathrooms, structural type. Click compare on any one of them and the engine shows you exactly where it differs from the subject. Sold data, appraiser-grade, on demand, free.
Then there's the data the engine uses that you can't see here.
I bring the data no one else can show you. The most valuable data in real estate isn't what sold. It's what didn't. Failed listings. Expired listings. Withdrawn listings. The properties that came on the market, sat, and walked away unsold, and the patterns that broke each one. This data is governed under IDX and VOW regulations and cannot be displayed to the general public. All agents have access to it, but none of them have the means to produce an analytics score with it. They don't have a data analyst's discipline, nor an appraiser's. That's the gap. That's what it took me a year to build.
This is the analysis I deliver in person. Not a typical comps package. Not a printed report ChatGPT now gives away for free to any agent willing to type a prompt. The hyperlocal heat index calibrated to the immediate neighborhood, the failed-comp forensics on directly comparable properties, the pricing pressure curve calibrated to that specific address, built live from the engine, walked through with you in person, with the data on the screen and the property at the center of the conversation.
The Analysis button opens a Market Leverage Analysis that measures the current balance of power in your specific market. It calculates four independent drivers and produces a single leverage reading that shows you which side of the table holds the advantage and by how much.
The citywide view runs free on every property page. The hyperlocal view, the 1-mile heat index calibrated specifically to the property you're looking at, is part of the in-home analysis. The delta between citywide and hyperlocal is almost always significant, and that delta is the entire reason this conversation matters.
The full picture, before you decide. Traditional real estate has shown consumers half the data. Sold listings only. No failures. No leverage metrics. No probability. The full analysis has been reserved for after you've already committed: after you've signed a listing agreement, after you've made an offer, after the appraiser shows up in escrow.
This system inverts that. The probability engine, the sold comps, the citywide leverage, the price elasticity: all of it runs free, on any property, on demand. The hyperlocal layer and the failed-comp forensics, I deliver in person. Together, they're the most complete property-specific pricing analysis a consumer has ever had access to.
Every data point comes directly from the California Regional Multiple Listing Service, the largest MLS in the United States. The data is not scraped, estimated, or algorithmically generated. It is the same data that licensed real estate professionals use to price, market, and sell homes every day. The engine queries it live for every inquiry. No stale cache. No pre-computed estimate. Every score is calculated fresh, for the property you're looking at, at the moment you request it.
Pick any property in the SGV. Run the probability. Open the comps. Check the leverage. Everything you just read about is live, right now.