Abstract: Method, systems, and apparatus for identifying a plurality of properties based at least on a location; determining, for each of the plurality of properties using a machine-learned model, a vacancy metric representing a duration the respective property will be available for rent, wherein the machine-learning model is trained using availability data for the plurality of properties; sending, for display to a user, a user interface comprising the vacancy metric.

Abstract: Method, systems, and apparatus for identifying engagement data representing user interest in renting or purchasing a property, wherein the engagement data comprises impression data, tenant application data, messaging data, appointment data, or rental data; calculating, using a machine-learning model, a price adjustment for the property based on at least the engagement data; and adjusting a price for the property based on the price adjustment

Abstract: Due to the noisy nature of global positioning systems (GPS), tracing a signal from a device of a delivery provider may be inadequate for the task of determining a best dispatch time. However, leveraging motion data from mobile devices provides a more detailed picture of when a delivery provider is on the road, walking, or waiting. Using this data, an example embodiment creates a trip state model that enables segmenting out each stage of a trip. The trip state model enables collection and use of historical data for individual restaurants, which allows a dispatch system to optimize pickup and delivery times for both delivery providers and consumers.