Our Likely to Sell methodology captures trends in the market on a national scale and works with local county level geographic attributes of the market in order to assign a likelihood score for each property to be sold within the next 2 years.

While it is difficult to pinpoint all of the reasons that drive the selling decision of a property, by leveraging more than 30 years of sales data on 52M+ properties across the U.S, our Machine Learning model is able to capture the owners’ response to the subtle geographical and historical market changes surrounding the property.

This way, our unique technology identifies historical trends inherent to specific Zip Codes and Metropolitan Statistical Areas (MSAs) spotting areas in the country with higher or lower selling rate compared to the overall market. This provides our users with a unique likelihood score for each property assisting them in ranking and prioritizing opportunities on the market.

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