Advanced data science systems and methods useful for auction pricing optimization over network
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.
1. A method for auction pricing optimization over a network, comprising:
receiving, by a data platform from a client device, an asset pricing request containing information about an asset, the data platform operating on at least one server machine and supporting a network site, the client device communicatively connected to the data platform over the network;
determining, by an optimization engine running on the data platform, a predicted price for the asset at each of a plurality of locations at each of a plurality of times;
computing, by the optimization engine, a price matrix containing a plurality of values for the predicted price, each value of the plurality of values associated with a specific location of the plurality of locations at a specific time of the plurality of times;
identifying, by the optimization engine from among the plurality of values at the plurality of locations relative to the plurality of times in the price matrix, an optimized predicted price for the asset by:
for a given location of the plurality of locations, comparing values associated with different times of the plurality of times;
for a given time of the plurality of times, comparing values associated with different locations of the plurality of locations; and
for a given location of the plurality of locations at a given time of the plurality of times, comparing values associated with different locations of the plurality of locations at different times of the plurality of times;
generating a view for presentation of the optimized predicted price for the asset on the client device; and
communicating the view to the client device over the network.
2. The method according to claim 1 , wherein the determining comprises:
determining a predicted price for the asset at a given location at each of the plurality of times; and
determining a predicted price for the asset at different locations of the plurality of locations other than the given location at each of the plurality of times.
3. The method according to claim 1 , wherein the determining comprises:
computing a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising a first input variable representing supply of the asset at a given location of the plurality of locations, a second input variable representing supply of the asset at different locations of the plurality of locations other than the given location, and a third input variable representing supply of competitive assets at the given location.
4. The method according to claim 1 , wherein the asset is a vehicle.
5. The method according to claim 4 , wherein the determining comprises:
computing 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.
6. The method according to claim 1 , wherein the asset pricing request is for pricing a plurality of assets, wherein the information contained in the asset pricing request further includes information about each of the plurality of assets, wherein the determining, the computing, and the identifying are performed for each of the plurality of assets, and wherein the view comprises an optimized predicted price for each of the plurality of assets.
7. The method according to claim 1 , wherein the asset comprises a set of vehicles, wherein the information contained in the asset pricing request further includes information about each of the set of vehicles, wherein the determining comprises performing a valuation of the set of vehicles with respect to the plurality of locations and the plurality of times.
8. A system for auction pricing optimization over a network, comprising:
a data platform operating on at least one server machine and supporting a network site, each of the at least one server machine comprising at least one processor and at least one non-transitory computer readable medium storing instructions translatable by the at least one processor to perform:
receiving, from a client device, an asset pricing request containing information about an asset, the client device communicatively connected to the data platform over the network;
determining a predicted price for the asset at each of a plurality of locations at each of a plurality of times;
computing a price matrix containing a plurality of values for the predicted price, each value of the plurality of values associated with a specific location of the plurality of locations at a specific time of the plurality of times;
identifying, from among the plurality of values at the plurality of locations relative to the plurality of times in the price matrix, an optimized predicted price for the asset by:
for a given location of the plurality of locations, comparing values associated with different times of the plurality of times;
for a given time of the plurality of times, comparing values associated with different locations of the plurality of locations; and
for a given location of the plurality of locations at a given time of the plurality of times, comparing values associated with different locations of the plurality of locations at different times of the plurality of times;
generating a view for presentation of the optimized predicted price for the asset on the client device; and
communicating the view to the client device over the network.
9. The system of claim 8 , wherein the determining comprises:
determining a predicted price for the asset at a given location at each of the plurality of times; and
determining a predicted price for the asset at different locations of the plurality of locations other than the given location at each of the plurality of times.
10. The system of claim 8 , wherein the determining comprises:
computing a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising a first input variable representing supply of the asset at a given location of the plurality of locations, a second input variable representing supply of the asset at different locations of the plurality of locations other than the given location, and a third input variable representing supply of competitive assets at the given location.
11. The system of claim 8 , wherein the asset is a vehicle.
12. The system of claim 11 , wherein the determining comprises:
computing 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.
13. The system of claim 8 , wherein the asset pricing request is for pricing a plurality of assets, wherein the information contained in the asset pricing request further includes information about each of the plurality of assets, wherein the determining, the computing, and the identifying are performed for each of the plurality of assets, and wherein the view comprises an optimized predicted price for each of the plurality of assets.
14. The system of claim 8 , wherein the asset comprises a set of vehicles, wherein the information contained in the asset pricing request further includes information about each of the set of vehicles, wherein the determining comprises performing a valuation of the set of vehicles with respect to the plurality of locations and the plurality of times.
15. A computer program product for auction pricing optimization over a network, the computer program product comprising at least one non-transitory computer readable medium storing instructions translatable by the at least one processor to perform:
receiving, from a client device, an asset pricing request containing information about an asset, the client device communicatively connected to a data platform over the network, the data platform operating on at least one server machine and supporting a network site, the at least one server machine comprising the at least one processor and the at least one non-transitory computer readable medium;
determining a predicted price for the asset at each of a plurality of locations at each of a plurality of times;
computing a price matrix containing a plurality of values for the predicted price, each value of the plurality of values associated with a specific location of the plurality of locations at a specific time of the plurality of times;
identifying, from among the plurality of values at the plurality of locations relative to the plurality of times in the price matrix, an optimized predicted price for the asset by:
for a given location of the plurality of locations, comparing values associated with different times of the plurality of times;
for a given time of the plurality of times, comparing values associated with different locations of the plurality of locations; and
for a given location of the plurality of locations at a given time of the plurality of times, comparing values associated with different locations of the plurality of locations at different times of the plurality of times;
generating a view for presentation of the optimized predicted price for the asset on the client device; and
communicating the view to the client device over the network.
16. The computer program product of claim 15 , wherein the determining comprises:
determining a predicted price for the asset at a given location at each of the plurality of times; and
determining a predicted price for the asset at different locations of the plurality of locations other than the given location at each of the plurality of times.
17. The computer program product of claim 15 , wherein the determining comprises:
computing a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising a first input variable representing supply of the asset at a given location of the plurality of locations, a second input variable representing supply of the asset at different locations of the plurality of locations other than the given location, and a third input variable representing supply of competitive assets at the given location.
18. The computer program product of claim 15 , wherein the asset is a vehicle and wherein the determining comprises:
computing 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.
19. The computer program product of claim 15 , wherein the asset pricing request is for pricing a plurality of assets, wherein the information contained in the asset pricing request further includes information about each of the plurality of assets, wherein the determining, the computing, and the identifying are performed for each of the plurality of assets, and wherein the view comprises an optimized predicted price for each of the plurality of assets.
20. The computer program product of claim 15 , wherein the asset comprises a set of vehicles, wherein the information contained in the asset pricing request further includes information about each of the set of vehicles, wherein the determining comprises performing a valuation of the set of vehicles with respect to the plurality of locations and the plurality of times.