Abstract: Systems and methods are disclosed for automatically projecting a number of days to pending for a real-estate property by receiving, by a server, real-estate property listing information of real-estate property activities associated with a plurality of real-estate properties and determining, for each of the plurality of real-estate properties, a days to pending amount indicating time between when a respective one of the plurality of real-estate properties was listed and when the respective real-estate property was sold. The systems and methods further train a machine learning technique to establish a relationship between the different types of real-estate property activities and the determined days to pending amounts. The trained machine learning technique is applied to new real-estate property activities associated with a new real-estate property to predict a length of time between a first time the new real-estate property was listed and a second time when the new real-estate property will be sold.

Abstract: Systems and methods are disclosed for automatically projecting a number of days to pending for a real-estate property by receiving, by a server, real-estate property listing information of real-estate property activities associated with a plurality of real-estate properties and determining, for each of the plurality of real-estate properties, a days to pending amount indicating time between when a respective one of the plurality of real-estate properties was listed and when the respective real-estate property was sold. The systems and methods further train a machine learning technique to establish a relationship between the different types of real-estate property activities and the determined days to pending amounts. The trained machine learning technique is applied to new real-estate property activities associated with a new real-estate property to predict a length of time between a first time the new real-estate property was listed and a second time when the new real-estate property will be sold.

Abstract: Systems and methods are disclosed for determining a valuation gain amount by receiving, by a server data corresponding to previously sold real-estate property listing is retrieved from a market database and calculating valuation data for each of the previously sold real-estate property listings for portions of a specified time period. Seasonality data is further extracted from the valuation data for each of the portions. The system receives adjustments from a client device to the seasonality data. The adjustments may relate to future real-estate property trends. A plurality of valuation gain amounts for a new real estate property listing is determined based on the adjusted seasonality data. The system generates a visualization representing a graphical trend and causes presentation of the visualization on a graphical user interface of a client device.