Sell Score is a predictive analytics tool that identifies which off-market residential properties are likely to transact sooner than others in their area, measured on a High, Medium, and Low scale. Sell Score is attached to properties, whereas Buy Score is attached to individuals. When you see a Sell Score attached to an individual on the People tab of the Track page, that Sell Score corresponds to the latest known associated property for the person.
Note: We disable Sell Score for active listings, and leave it disabled until 6 months after the property has sold, the listing has expired, or the listing has been canceled.
You can use the Sell Score filter on the Discover map to quickly identify properties with different Sell Score levels. See the What are the available layers? article to read about all the available layers in Remine. You can also filter your tracked properties by Sell Score on the Track page.
Predictive analytics involves using historical data with machine learning and artificial intelligence to predict what will happen in the future. We analyze historical data with a mathematical model that considers key trends and patterns. We then apply this model to current data to predict what will happen next.
The Sell Score model searches for underlying patterns based on available data. This means that agents will often have important information about a property that isn't included in the Sell Score model. For this reason, we recommend combining Sell Score with other layers, like Home Equity, to most effectively identify your target market.
Properties can have a Sell Score of High, Medium, or Low.
We calculate Sell Score using weighted values, so you may occasionally come across some surprising results (e.g., a property that transacted within the last year with a high Sell Score).
The following are the primary variables that affect the Sell Score:
When any of these data points change, the Sell Score changes accordingly.
We calculate Sell Scores biweekly to keep the scores as up to date as possible. Not every property will see a difference with each Sell Score update, but an average of about 5% of properties change each month.
A property's Sell Score may change over time as we receive new or updated information. The Sell Score model is always changing and improving, so Sell Score changes are a good thing.
A High Sell Score property is 2 to 6 times more likely to transact in the next 6 months than a randomly selected property.
If you select 100 off-market homes at random, on average, 2 to 3 of those properties will likely sell in the next 6 months.
If you select 100 off-market homes with a High Sell Score, 5 to 20 of those properties will likely sell in the next 6 months.
Tip: Using the Sell Score in conjunction with other layers, like Home Equity and Ownership Time, can increase the probability that you're targeting properties that are likely to sell.
We measure the Sell Score's performance through an equation that compares properties with a High Sell Score to the homes that have actually sold. We run this equation in limited geographic areas to calculate accuracy among various markets.
The purpose of predictive analytics is to help you prioritize your time, so you can reach out to the right people with the right message. The Sell Score is a tool to help with prioritization. While you may not have the time or resources to send out 10,000 mailers to build your brand, you can instead focus on 100 properties with High Sell Scores and yield better results.