Abstract:
Embodiments disclosed herein can provide consumers with an effective tool for evaluating the negotiability of prices for vehicles in the marketplace. The tool may include a Price Flexibility Score which measures the elasticity of transaction prices by vehicle model. Specifically, a method may dynamically incorporate factors that affect price variance, convert those factors into variables, generate order statistics for each of the variables, apply a weighting factor to the variables to generate a price flexibility score for each make-model, and determine a negotiability index utilizing the price flexibility score. In one embodiment, the process of determining the negotiability index may be fully driven by a price flexibility model that incorporates a plurality of factors.
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.
Abstract:
Natural language processing (NLP) approaches may be utilized to map two strings. The strings may come from sources utilizing different naming conventions. One example may be a data aggregator that collects used car transaction information. Another example may be a comprehensive database listing all possible manufacturer-defined vehicle options. A NLP system may operate to determine whether a source string is present in a target string and outputting a match containing the source string and the target string if the source string is present in the target string or computing a similarity factor if the source string is not present in the target string. The similarity factor representing a measure of similarity between two strings may be computed based on a plurality of parameters, including a Levenshtein edit distance parameter. The computed similarity can be used to find pricing information, including trade-in, sale, and list prices, across disparate naming conventions.
Abstract:
Embodiments disclosed herein can provide consumers with an effective tool for evaluating the negotiability of prices for vehicles in the marketplace. The tool may include a Price Flexibility Score which measures the elasticity of transaction prices by vehicle model. Specifically, a method may dynamically incorporate factors that affect price variance, convert those factors into variables, generate order statistics for each of the variables, apply a weighting factor to the variables to generate a price flexibility score for each make-model, and determine a negotiability index utilizing the price flexibility score. In one embodiment, the process of determining the negotiability index may be fully driven by a price flexibility model that incorporates a plurality of factors.
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.
Abstract:
Natural language processing (NLP) approaches may be utilized to map two strings. The strings may come from sources utilizing different naming conventions. One example may be a data aggregator that collects used car transaction information. Another example may be a comprehensive database listing all possible manufacturer-defined vehicle options. A NLP system may operate to determine whether a source string is present in a target string and outputting a match containing the source string and the target string if the source string is present in the target string or computing a similarity factor if the source string is not present in the target string. The similarity factor representing a measure of similarity between two strings may be computed based on a plurality of parameters, including a Levenshtein edit distance parameter. The computed similarity can be used to find pricing information, including trade-in, sale, and list prices, across disparate naming conventions.