System and method for analysis and presentation of used vehicle pricing data

Abstract

Systems and methods for the aggregation, analysis, and display of data for used vehicles are disclosed. Historical transaction data for used vehicles may be obtained and processed to determine pricing data, where this determined pricing data may be associated with a particular configuration of a vehicle. The user can then be presented with an interface pertinent to the vehicle configuration utilizing the aggregated data set or the associated determined data where the user can make a variety of determinations. This interface may, for example, be configured to present the historical transaction data visually, with the pricing data such as a trade-in price, a list price, an expected sale price or range of sale prices, market low sale price, market average sale price, market high sale price, etc. presented relative to the historical transaction data.

What is claimed is: 
     
         1 . A vehicle data system comprising:
 a processor;   a non-transitory computer readable medium comprising computer code for processing distributed vehicle data, the computer code comprising code for:   implementing a backend process comprising:
 obtaining a set of historical transaction records from a set of distributed sources, the set of historical transaction records comprising individual historical sales transactions for a set of vehicles and including vehicle information, sale price and geographical information; 
 applying binning rules to bin vehicles in the set of vehicles as a function of vehicle configuration and geography such that each bin has an associated set of historical transaction records and a corresponding vehicle configuration; 
 generating a set of depreciation functions, each depreciation function defining a current value for a vehicle as a function of age, mileage, condition and geography relative to the vehicle's value when new, wherein generating a depreciation function for a vehicle comprises:
 generating an exponential decay estimate model that models a depreciation value of the vehicle configuration for given age inputs based on fitting an exponential decay curve to depreciated valuations for the vehicle configuration; 
 building a residual value model for the vehicle configuration, the residual value model modeling residual value as a function of predicted residual value according to the exponential decay estimate model, vehicle model, mileage, condition and geography; 
 
 generating a price ratio model based on multivariable regression analysis of a set of vehicle attributes, usage data, geo-specific data and days-to-sell data, the price ratio model defining price ratio as a function of a first set of regression variables representing vehicle attributes in the set of vehicle attributes, usage data, geo-specific data and days-to-sell data and fitted to the set of vehicle attributes, usage data, geo-specific data and days-to-sell data to find a set of regression coefficients; 
   providing a web page to a client computer, the web page having one or more input fields for a user to provide a user-specified vehicle configuration comprising a set of user-specified vehicle attributes;   receiving over a network via the web page the set of user-specified vehicle attributes;   implementing a front end process in response to receiving the set of user-specified vehicle attributes, the front end process comprising:
 applying a first set of rules to select a bin based on the set of user-specified vehicle attributes, the bin selected based on at least one user-specified vehicle attribute and geography; 
 applying a second set of rules to determine a second set of regression variables from the first set of regression variables based on the set of user-specified vehicle attributes; 
 determining the regression coefficients corresponding to the second set of regression variables; 
 applying a third set of rules to select a residual value model and applying the residual value model to determine a depreciated value for the user-specified vehicle; 
 applying the price ratio model using user-specified vehicle attribute values for values of the second set of regression variables and the determined regression coefficients to determine a predicted price ratio; 
 generating an expected price for the user-specified vehicle based on the predicted price ratio, a weighted average of price ratios for the selected bin and the depreciated value for the user-specified vehicle; 
 generating HTML to cause a browser at the client computer to display expected price; 
 sending the HTML to the client computer. 
   
     
     
         2 . The vehicle data system of  claim 1 , further comprising code for:
 receiving updated user input data comprising an updated set of user-specified vehicle attributes;   adjusting the regression variables, and adjusting the expected price based on the updated user input data using at least one of the set of depreciation functions and price ratio model;   dynamically generating new HTML to cause the browser at the client computer to display the updated expected price; and   sending the new HTML to the client computer.   
     
     
         3 . The vehicle data system of  claim 1 , wherein the binning rules comprise rules to cluster vehicles as a function of body type, vehicle type, engine or transmission. 
     
     
         4 . The vehicle data system of  claim 1 , further comprising code for:
 applying a fourth set of rules to construct research datasets using temporally weighted historical data, the research data sets comprising the geo-specific data, vehicle-specific attributes, usage data and days-to-sell data.   
     
     
         5 . The vehicle data system of  claim 1 , wherein the second set of regression variables further comprise regression variables representing usage data. 
     
     
         6 . The vehicle data system of  claim 1 , wherein the second set of regression variables further comprise regression variables representing geo-specific data. 
     
     
         7 . A vehicle data system comprising:
 a processor;   a non-transitory computer readable medium comprising computer code for processing distributed vehicle data, the computer code comprising code for:   
       implementing a backend process comprising:
 obtaining a set of historical transaction records from a set of distributed sources, the set of historical transaction records comprising individual historical sales transactions for a set of vehicles and including vehicle information, sale price and geographical information; 
 applying binning rules to bin vehicles in the set of vehicles as a function of vehicle configuration and geography such that each bin has an associated set of historical transaction records and a corresponding vehicle configuration; 
 generating a set of depreciation functions, each depreciation function defining a current value for a vehicle as a function of age, mileage, condition and geography relative to the vehicle's value when new, wherein generating a depreciation function for a vehicle comprises:
 generating an exponential decay estimate model that models a depreciation value of the vehicle configuration for given age inputs based on fitting an exponential decay curve to depreciated valuations for the vehicle configuration; 
 building a residual value model for the vehicle configuration, the residual value model modeling residual value as a function of predicted residual value according to the exponential decay estimate model, vehicle model, mileage, condition and geography; 
 
 generating a price ratio model based on multivariable regression analysis of a set of vehicle attributes, usage data, geo-specific data and days-to-sell data, the price ratio model defining price ratio as a function of a first set of regression variables representing vehicle attributes in the set of vehicle attributes, usage data, geo-specific data and days-to-sell data and fitted to the set of vehicle attributes, usage data, geo-specific data and days-to-sell data to find a set of regression coefficients; 
 providing a web page to a client computer, the web page having one or more input fields for a user to provide a user-specified vehicle configuration comprising a set of user-specified vehicle attributes; 
 receiving over a network via the web page the set of user-specified vehicle attributes; 
 implementing a front end process in response to receiving the set of user-specified vehicle attributes, the front end process comprising:
 generating a responsive web page in response to the user submitting the user-specified vehicle attributes, the responsive web page comprising HTML to cause a browser at the client computer to display an expected price for the user-selected configuration the expected price based on applying a selected residual value model selected based on at least a user-specified vehicle configuration and applying a selected price ratio model corresponding to a selected bin selected based on at least a user-specified geography to a set of historical transaction records corresponding to the selected bin; 
 communicating the responsive web page to the client computer. 
 
 
     
     
         8 . The vehicle data system of  claim 7 , wherein:
 the code for implementing the backend process further comprises code for:
 applying a first set of rules to select the bin based on the set of user-specified vehicle attributes; 
   the code for implementing the front end process further comprises code for:
 applying a second set of rules to determine a set of regression variables for the price ratio model; 
 determining a set of pre-calculated regression coefficients corresponding to the set of regression variables; 
 applying a third set of rules to select the residual value model and wherein:
 applying the residual value model comprises applying the residual value model to determine a depreciated value for the user-specified vehicle; 
 applying the price ratio model comprises applying the price ratio model using user-specified vehicle attribute values for values of the set of regression variables and the determined regression coefficients to determine a predicted price ratio; 
 generating the expected price for the user-specified vehicle comprises generating the expected price based on the predicted price ratio, a weighted average of price ratios for the selected bin and the depreciated value for the user-specified vehicle. 
 
   
     
     
         9 . The vehicle data system of  claim 8 , further comprising code for:
 receiving updated user input data comprising an updated set of user-specified vehicle attributes;   adjusting the regression variables;   adjusting the expected price based on the updated user input data using at least one of the set of depreciation functions and the price ratio model;   dynamically generating new HTML to cause the browser at the client computer to display the updated expected price; and   sending the new HTML to the client computer.   
     
     
         10 . The vehicle data system of  claim 8 , wherein the binning rules comprise rules to cluster vehicles as a function of body type, vehicle type, engine or transmission. 
     
     
         11 . The vehicle data system of  claim 8 , further comprising code for:
 applying a fourth set of rules to construct research datasets using temporally weighted historical data, the research data sets comprising the geo-specific data, vehicle-specific attributes, usage data and days-to-sell data.   
     
     
         12 . The vehicle data system of  claim 8 , wherein the second set of regression variables further comprise regression variables representing usage data. 
     
     
         13 . The vehicle data system of  claim 8 , wherein the second set of regression variables further comprise regression variables representing geo-specific data. 
     
     
         14 . A vehicle data system comprising:
 a processor;   a non-transitory computer readable medium comprising computer code for processing distributed vehicle data, the computer code comprising code for:   implementing a backend process comprising:
 obtaining a set of historical transaction records from a set of distributed sources, the set of historical transaction records comprising individual historical sales transactions for a set of vehicles and including vehicle information, sale price and geographical information; 
 applying binning rules to bin vehicles in the set of vehicles as a function of vehicle configuration and geography such that each bin has an associated set of historical transaction records and a corresponding vehicle configuration; 
 building a residual value model for each vehicle configuration, each residual value model modeling residual value as a function of predicted residual value according to the exponential decay estimate model, vehicle model, mileage, condition and geography; 
 generating a price ratio model based on multivariable regression analysis of a set of vehicle attributes, usage data, geo-specific data and days-to-sell data, the price ratio model defining price ratio as a function of a first set of regression variables representing vehicle attributes in the set of vehicle attributes, usage data, geo-specific data and days-to-sell data and fitted to the set of vehicle attributes, usage data, geo-specific data and days-to-sell data to find a set of regression coefficients; 
   providing a web page to a client computer, the web page having one or more input fields for a user to provide a user-specified vehicle configuration comprising a set of user-specified vehicle attributes;   receiving over a network via the web page the set of user-specified vehicle attributes;   implementing a front end process in response to receiving the set of user-specified vehicle attributes, the front end process comprising:
 applying a residual value model and a price ratio model selected based on the user-specified vehicle attributes to a set of historically weighted transaction records for a bin corresponding to the user-specified vehicle attributes to generate an expected price for the user-specified vehicle; 
 generating HTML configured to cause a browser at the client computer to display the expected price; 
 sending the HTML to the client computer in response to receiving the user-specified attributes. 
   
     
     
         15 . The vehicle data system of  claim 14 , wherein:
 the code for implementing the backend process further comprises:
 applying a first set of rules to select the bin based on the set of user-specified vehicle attributes; 
   the code for implementing the front end process further comprises:
 applying a second set of rules to determine a set of regression variables for the price ratio model; 
 determining a set of pre-calculated regression coefficients corresponding to the set of regression variables; 
 applying a third set of rules to select the residual value model and wherein:
 applying the residual value model comprises applying the residual value model to determine a depreciated value for the user-specified vehicle; 
 applying the price ratio model comprises applying the price ratio model using user-specified vehicle attribute values for values of the set of regression variables and the determined regression coefficients to determine a predicted price ratio; 
 generating the expected price for the user-specified vehicle comprises generating the expected price based on the predicted price ratio, a weighted average of price ratios for the selected bin and the depreciated value for the user-specified vehicle. 
 
   
     
     
         16 . The vehicle data system of  claim 14 , further comprising code for:
 receiving updated user input data comprising an updated set of user-specified vehicle attributes;   adjusting the regression variables, and adjusting the expected price based on the updated user input data and using at least one of the set of depreciation functions and price ratio model;   dynamically generating new HTML to cause the browser at the client computer to display the updated expected price; and   sending the new HTML to the client computer.   
     
     
         17 . The vehicle data system of  claim 14 , wherein the binning rules comprise rules to cluster vehicles as a function of body type, vehicle type, engine or transmission. 
     
     
         18 . The vehicle data system of  claim 14 , further comprising code for:
 applying a fourth set of rules to construct research datasets using temporally weighted historical data, the research data sets comprising the geo-specific data, vehicle-specific attributes, usage data and days-to-sell data.   
     
     
         19 . The vehicle data system of  claim 14 , wherein the second set of regression variables further comprise regression variables representing usage data. 
     
     
         20 . The vehicle data system of  claim 14 , wherein the second set of regression variables further comprise regression variables representing geo-specific data.