Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: A data system can maintain a targeted incentive database mapping product categories to targeted incentive levels and mapping the targeted incentive levels to a plurality of user segments corresponding to observable features of users. The data system can receive a user query from a user computer device, the query comprising product configuration information, apply a set of segment matching rules to match the user to a user segment from the targeted incentive database based on a set of observable features associated with the user, identify a targeted incentive level for a product category from a plurality of targeted incentive levels for the product category according to a mapping between the determined user segment and the identified targeted incentive level in the targeted incentive database and generate a responsive web page to display the identified targeted incentive level at the user computing device.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.

Abstract: An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

Abstract: Systems, methods and products for determining residual values of asset portfolios are disclosed. In one embodiment, a system includes a server computer coupled to a network and to a data storage device. The server generates residual value curves for each of a set of item types and stores them. The server computer also receives and/or maintains information defining a set of items in a portfolio. When an assessment of the portfolio is initiated, the server determines a residual value for each item in the portfolio by identifying a corresponding one of the item types, retrieving the residual value curve corresponding to the identified item type, and determining a future value of the item based on the retrieved residual value curve for the corresponding item type. The server then aggregates the individual residual values into a residual value for the portfolio and enables access to this value.

Abstract: Systems, methods and products for determining residual values of asset portfolios are disclosed. In one embodiment, a system includes a server computer coupled to a network and to a data storage device. The server generates residual value curves for each of a set of item types and stores them. The server computer also receives and/or maintains information defining a set of items in a portfolio. When an assessment of the portfolio is initiated, the server determines a residual value for each item in the portfolio by identifying a corresponding one of the item types, retrieving the residual value curve corresponding to the identified item type, and determining a future value of the item based on the retrieved residual value curve for the corresponding item type. The server then aggregates the individual residual values into a residual value for the portfolio and enables access to this value.

Abstract: Systems, methods and computer program products for forecasting future values of an item, where an initial value for the item is determined, and then a baseline forecast for a future reference period is computed based on factors that include microeconomic data which is specific to an industry of the item and macroeconomic data which is non-specific to the industry of the item. The forecast may also be adjusted based on data for a set of competitive items. The forecast for the item is stored and is then made available to clients that can access the forecast to determine the expected future value of the item at some point in the future.