Method, program, and system for calculating estimated prices of used vehicles.

Abstract

This invention provides a method for calculating the estimated price of a used vehicle that is efficient and reliable, taking into account a variety of factors in addition to the physical characteristics of the used vehicle. [Solution] A physical characteristics coefficient is calculated based on the vehicle type, year of manufacture, and mileage. A brand value coefficient is calculated based on the reliability of the vehicle manufacturer and the timing of vehicle model updates. A trend coefficient is calculated based on the frequency of SNS posts and the amount of advertising. An estimated used vehicle price is calculated based on these coefficients, the new vehicle price, the physical characteristics coefficient, the brand value coefficient, and the trend coefficient.

Description

Technical Field

[0004] , , ,

[0001] The present invention relates to a used vehicle estimated price calculation method for estimating the price of a used vehicle, its program, and its system.

Background Art

[0002] Conventional used vehicle appraisal systems determine the price of a used vehicle by directly referring to the auction winning price or the used vehicle market price. This approach has the problem that the accuracy decreases when market price fluctuations or data gaps occur. Specifically, due to the strong dependence on the auction price, it is sensitive to economic conditions and market fluctuations, thereby impairing the reliability of the appraisal results. Furthermore, conventional residual value models are limited to physical factors such as "model year", "mileage", and "grade", and have the limitation that they cannot reflect social trends and sentiment data (such as popularity on SNS and brand loyalty). As a result, it is highly likely that the appraisal results do not accurately reflect the actual market value. In addition, online appraisal services using the Internet have become widespread, but these services still remain at appraisals based on physical factors, and there is a need to consider more diverse factors.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=?]] In view of the problems of the above-described prior art, the present invention is made, and an object thereof is to provide a used vehicle estimated price calculation, its program, and its system that calculate an efficient and reliable used vehicle estimated price by considering various factors in addition to the physical factors of the used vehicle. ​[Means for solving the problem]

[0005] The present invention is a method for calculating the estimated price of a used vehicle, which is performed by a computer and comprises the following steps: a first step of calculating a physical characteristics coefficient based on the vehicle type, year of manufacture, and mileage; a second step of calculating a brand value coefficient based on the reliability of the vehicle manufacturer and the timing of the vehicle's model refresh; a third step of calculating a trend coefficient based on the frequency of SNS posts and the amount of advertising; and a fourth step of calculating an estimated used vehicle price based on the new vehicle price, the physical characteristics coefficient, the brand value coefficient, and the trend coefficient.

[0006] Preferably, the first step further calculates the physical characteristic coefficient using at least one of the vehicle image, maintenance history, equipment data, and energy correction coefficient.

[0007] Preferably, the second step generates the brand value coefficient using at least one of the vehicle's sales trends and social media impact.

[0008] Preferably, the third step further generates the trend coefficient using the search trend.

[0009] Preferably, the fourth step further comprises a fifth step of calculating a value retention and appreciation coefficient using data that quantifies at least one of rarity, durability, cultural value, hobby appeal, future suitability, maintenance costs, contrarian signals, and preservation, and the fourth step further calculates the estimated price of the used vehicle based on the value retention and appreciation coefficient calculated in the fifth step.

[0010] Preferably, the process further includes a sixth step of calculating a market deviation correction coefficient based on the estimated used vehicle price calculated in the fourth step and the market reference price of the vehicle, and the fourth step further calculates the estimated used vehicle price based on the market deviation correction coefficient calculated in the sixth step.

[0011] Preferably, the fourth step further comprises a seventh step of calculating a regional coefficient using data that quantifies at least one of the regional supply volume, seasonality, and regional population change rate of vehicles, and the fourth step further calculates the estimated price of the used vehicle based on the regional coefficient calculated in the seventh step.

[0012] Preferably, at least one of the physical characteristic coefficient, brand value coefficient, and trend coefficient is generated by a trained model.

[0013] The present invention comprises a first step of calculating a physical characteristic coefficient based on the vehicle type, year of manufacture, and mileage, The second step involves calculating a brand value coefficient based on the reliability of the vehicle manufacturer and the timing of vehicle model updates. This program causes a computer to perform a third step of calculating a trend coefficient based on the frequency of SNS posts and the amount of advertising, and a fourth step of calculating an estimated used vehicle price based on the new vehicle price, the physical characteristics coefficient, the brand value coefficient, and the trend coefficient.

[0014] The present invention is a used vehicle price estimation system comprising: a first means for calculating a physical characteristics coefficient based on the vehicle type, year of manufacture, and mileage; a second means for calculating a brand value coefficient based on the reliability of the vehicle manufacturer and the timing of vehicle model updates; a third means for calculating a trend coefficient based on the frequency of SNS posts and the amount of advertising; and a fourth means for calculating an estimated used vehicle price based on the new vehicle price, the physical characteristics coefficient, the brand value coefficient, and the trend coefficient. [Effects of the Invention]

[0015] According to the present invention, it is possible to provide a used vehicle price estimation calculator, a program, and a system that calculate an efficient and highly reliable used vehicle price estimation price by considering various factors in addition to the physical factors of the used vehicle. [Brief explanation of the drawing]

[0016] [Figure 1]FIG. 1 is a network configuration diagram of a vehicle appraisal auction system 1 according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram for explaining coefficients used when calculating the estimated price of a used vehicle. [Figure 3] FIG. 3 is a diagram for explaining coefficients used when calculating the estimated price of a used vehicle. [Figure 4] FIG. 4 is a flowchart for explaining the flow of calculating the estimated price of a used vehicle by the used vehicle estimated price calculation device 21 shown in FIG. 1. [Figure 5] FIG. 5 is a flowchart for explaining the physical characteristic coefficient calculation flow shown in FIG. 4. [Figure 6] FIG. 6 is a flowchart for explaining the brand value coefficient calculation flow shown in FIG. 4. [Figure 7] FIG. 7 is a flowchart for explaining the trend coefficient calculation flow shown in FIG. 4. [Figure 8] FIG. 8 is a flowchart for explaining the value maintenance and increase coefficient calculation flow shown in FIG. 4. [Figure 9] FIG. 9 is a functional block diagram of the used vehicle estimated price calculation device 21 shown in FIG. 1.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, a method for calculating the estimated price of a used vehicle, its program, and a system according to an embodiment of the present invention will be described. In the method for calculating the estimated price of a used vehicle according to this embodiment, various data related to the vehicle are acquired, and the physical characteristic coefficient, brand value coefficient, and trend coefficient of the vehicle are calculated. Then, based on these, the used vehicle price of the vehicle is calculated. Thereby, in addition to physical factors such as "model year", "mileage", and "grade", it is possible to estimate the used vehicle price that reflects social trends and emotional data (such as popularity on SNS, brand loyalty, etc.).

[0018] Figure 1 is a network configuration diagram of a vehicle appraisal auction system 1 according to an embodiment of the present invention. As shown in Figure 1, the vehicle appraisal auction system 1 includes, for example, a vehicle owner terminal device 11, a buyer terminal device 15, a used vehicle estimated price calculation device 21, and an auction system 25, which communicate via a network 9.

[0019] As shown in Figure 1, the used vehicle price estimation device 21 acquires various data related to the vehicle and calculates the vehicle's physical characteristics coefficient, brand value coefficient, and trend coefficient. Based on these, it then calculates the used vehicle price. The used vehicle estimated price calculation device 21 transmits the calculated used vehicle estimated price to the requesting device in response to requests from the vehicle owner terminal device 11, the buyer terminal device 15, and the auction system 25.

[0020] The following describes how each coefficient K is calculated using the used vehicle price estimation device 21. The used vehicle price estimation device 21 calculates the estimated price of a used vehicle using the following formula. (Estimated used vehicle price) = New car price × K (physical characteristics coefficient) × coefficient K (brand value) × coefficient K (trend) × coefficient K (value maintenance and increase coefficient) The new car price is the new car price published by the manufacturer and is obtained from outside the used vehicle estimated price calculation device 21.

[0021] Figures 2 and 3 are diagrams illustrating the coefficients used when calculating the estimated price of a used vehicle. As shown in Figure 2, the coefficient K (physical characteristics coefficient) used to calculate the estimated price of a used vehicle includes K (vehicle type), K (year of manufacture), K (mileage), K (equipment specifications), K (maintenance history), coefficient K (images), and coefficient K (energy correction).

[0022] Furthermore, as shown in Figure 2, the coefficient K (brand value) used when calculating the estimated price of a used vehicle includes K (manufacturer reliability), K (model refresh timing), K (sales trends), and K (SNS influence).

[0023] As shown in FIG. 3, the coefficient K (trend) used when calculating the estimated price of a used vehicle includes the coefficient K (SNS posting frequency) and the coefficient K (search trend). As shown in FIG. 3, the coefficient K (value maintenance and increase coefficient) used when calculating the estimated price of a used vehicle includes the coefficient K (rarity), the coefficient K (durability), the coefficient K (cultural value), the coefficient K (interestingness), the coefficient K (future compatibility), the coefficient K (maintenance cost), the coefficient K (catch-up signal), and the coefficient K (preservability).

[0024] Each coefficient shown in FIGS. 2 and 3 may be calculated by machine learning. In that case, the data used for calculating each coefficient is input, and a learned model is generated by supervised learning by looking at the output of the coefficient from the learning model, and it is used.

[0025] The used vehicle estimated price calculation device 21 calculates the estimated price of a used vehicle according to the flow shown in FIG. 4.

[0026] Hereinafter, each coefficient will be described. <K (physical characteristic coefficient)> K (physical characteristic coefficient) = K (vehicle type) × K (model year) × K (mileage) × K (equipment specification) × K (maintenance history) × coefficient K (image) × coefficient K (energy correction) The used vehicle estimated price calculation device 21 calculates K (physical characteristic coefficient) according to the flow shown in FIG. 5 at ST11 in FIG. 4. Also, the information necessary for the calculation is acquired from the outside.

[0027] K (vehicle type): The coefficient K (vehicle type) is an index indicating the value as a vehicle type, and is defined as follows, for example. Coefficient K (vehicle type) = 1 + (vehicle type evaluation score) / (maximum vehicle type evaluation score) The vehicle type evaluation score is the score of the comprehensive evaluation of the vehicle type by a specialized institution. This score has plus and minus values. Maximum vehicle type evaluation score: The maximum evaluation score in the past for vehicles in the same category.

[0028] K (model year): The coefficient K (year of manufacture) is an indicator that shows the vehicle's value from the perspective of its year of manufacture, and is defined, for example, as follows: Coefficient K (year of manufacture) = 1 + (number of years elapsed) / (maximum number of years elapsed) The newer the model year, the higher its value. Let's assume the maximum age is 10 years.

[0029] K (Distance traveled): The coefficient K (mileage) is an indicator that shows the value of a vehicle in terms of mileage, and is defined, for example, as follows: Coefficient K (distance traveled) = 1 + (distance traveled) / (maximum distance traveled) The lower the mileage, the higher the value. This is assuming a maximum mileage of 200,000 kilometers.

[0030] K (Equipment Specifications): The coefficient K (equipment specifications) is an index that indicates the vehicle's value from the perspective of its equipment specifications, and is defined, for example, as follows: Coefficient K (equipment specifications) = 1 + (equipment score) The equipment score increases with the quality of your equipment. The equipment score is calculated based on the number and quality of optional equipment, with the standard equipment being used as a baseline.

[0031] K (Maintenance History): The coefficient K (maintenance history) is an index that indicates the vehicle's value from the perspective of its maintenance history, and is defined, for example, as follows: Coefficient K (maintenance history) = 1 + (maintenance score) The better maintained it is, the higher its value. The maintenance score will be higher if regular maintenance is performed.

[0032] Coefficient K (image): The coefficient K (image) is an index that indicates the vehicle's value from the perspective of its external appearance, and is defined, for example, as follows: Coefficient K(image) = 1 + (image state score) The image condition score is higher the better the vehicle's condition is, based on image analysis. The image condition score is positive for good images and negative for poor images.

[0033] Coefficient K (energy correction): The coefficient K (energy correction) is an indicator that shows the vehicle's value from an energy perspective, and is defined, for example, as follows: Coefficient K (energy correction) = 1 + (energy correction) The energy correction score increases in value the better the level of energy consumption (energy conservation, etc.). The energy correction score is positive when the level is high and negative when the level is low.

[0034] <Coefficient K (Brand Value)> Coefficient K (brand value) = K (manufacturer reliability) × K (model refresh timing) × K (sales trend) × K (SNS impact)

[0035] Brand Value Index (BVI): This component quantifies brand value by integrating and weighting manufacturer credibility, model refresh cycle, sales trends, and social media influence. Search notes: Used cars, vehicle sales price, manufacturer reliability, model refresh timing, sales trends, SNS impact, coefficient definitions The following formulas define coefficients for estimating the resale price of used cars, taking into account factors such as the reliability of the manufacturer, the timing of vehicle model updates, sales trends, and the impact of social media.

[0036] The used vehicle price estimation device 21 calculates the coefficient K (brand value) in ST12 of Figure 4 according to the flow shown in Figure 6. The information necessary for this calculation is obtained from an external source.

[0037] K (Manufacturer reliability): K (Manufacturer Reliability) is a numerical representation of the reliability of a vehicle manufacturer, and is defined as follows based on past recall numbers and customer satisfaction surveys. K (Manufacturer reliability) = 1 - R / N R represents the number of past recalls, and N represents the total number of vehicles sold that are affected.

[0038] K (Model refresh timing): K (model refresh timing) is a coefficient based on the number of years since the vehicle model was last refreshed, and is defined as follows: K (model refresh timing) = max(0, 1 - Y / L) Y represents the number of years since the model was last updated. L represents the average lifecycle length of the model (e.g., 5 years).

[0039] K (Sales Trend): K (sales trend) is a coefficient based on the average number of vehicle sales over the past few years, and is defined as follows: K (Sales Trend) = A / B A represents the number of units sold over the past year. B represents the average number of units sold over the past three years.

[0040] K (SNS influence) is a coefficient based on the number of mentions and ratings on social media, and is defined as follows: K(SNS influence)=P / (P+N) The more positive mentions there are, the higher the rating. P represents the number of positive mentions on social media, and N represents the number of negative mentions on social media.

[0041] <Coefficient K (Trend)> The used vehicle price estimation device 21 calculates the coefficient K (trend) using the following formula. Coefficient K (trend) = Coefficient K (SNS posting frequency) × Coefficient K (search trend) The used vehicle price estimation device 21 calculates the coefficient K (trend) in ST13 of Figure 4 according to the flow shown in Figure 7. The information necessary for this calculation is obtained from an external source.

[0042] Coefficient K (SNS posting frequency): The coefficient K (SNS posting rate) is an indicator of interest in and popularity of a vehicle, and is defined, for example, as follows: Coefficient K (SNS posting frequency) = 1 + (number of posts) / (maximum number of posts) The number of posts refers to the number of posts about the vehicle in question made on social media during a specific period. Maximum number of posts: This is the highest number of posts ever made for a vehicle in the same category.

[0043] Coefficient K (search trend): The coefficient K (search trend) indicates market interest in vehicles. It is an index that shows search trends based on data such as search engine trends, and is defined, for example, according to the search volume within a specific period, as follows: Coefficient K (search trend) = 1 + (search volume) / (maximum search volume) Search volume indicates the number of search queries related to the vehicle in question. Maximum search volume indicates the highest historical search volume for vehicles in the same category.

[0044] <Coefficient K (Value maintenance and increase)> The coefficient K (Value Retention / Appreciation) is calculated by exponentially weighting eight factors (VRA: Value Retention / Appreciation Index): rarity, durability, cultural value, hobby appeal, future suitability, maintenance costs, contrarian signals, and preservation. Coefficient K (Value maintenance and increase) = Coefficient K (Rarity) × Coefficient K (Durability) × Coefficient K (Cultural value) × Coefficient K (Hobby value) × Coefficient K (Future suitability) × Coefficient K (Maintenance costs) × Coefficient K (Contrarian signal) × Coefficient K (Preservation) The used vehicle price estimation device 21 calculates the coefficient K (value maintenance and increase) in ST14 of Figure 4, according to the flow shown in Figure 8. The information necessary for this calculation is obtained from an external source.

[0045] Coefficient K (scarcity): The coefficient K (rarity) is a numerical representation of the rarity of a vehicle, and is defined as follows based on research into vehicle rarity. The coefficient K (scarcity) = 1 + (scarcity score) / (maximum scarcity score) The rarity score is the rarity assessment score of the vehicle by a specialized organization. The maximum rarity score is the highest rarity assessment score among vehicles in the same category.

[0046] Coefficient K (durability): The coefficient K (durability) is an indicator of how long a vehicle can be used, and is defined as follows: Coefficient K (durability) = 1 + (durability score) / (maximum durability score) The durability rating score is the score from a durability evaluation conducted by a specialized organization. The maximum durability rating score is the highest rating score ever achieved for a vehicle in the same category.

[0047] Coefficient K (cultural value): The coefficient K (cultural value) is an indicator of the historical or cultural significance of a vehicle, and is defined as follows: Coefficient K (cultural value) = 1 + (cultural value assessment score) / (maximum cultural value assessment score) The cultural value score is a score of cultural evaluation by experts and the community. The maximum cultural value score is the highest evaluation score ever achieved for a vehicle in the same category.

[0048] Coefficient K (hobby / interest-based): The coefficient K (hobby appeal) is an index that indicates the appeal of a vehicle based on an individual's tastes and preferences, and is defined as follows. Coefficient K (Hobby Factor) = 1 + (Hobby Factor Evaluation Score) / (Maximum Hobby Factor Evaluation Score) The hobby appeal score is a rating from the community and fans. The maximum hobby appeal score is the highest rating ever achieved by a vehicle in the same category.

[0049] Coefficient K (Future suitability): The coefficient K (future adaptability) is an indicator that shows how well a vehicle can adapt to future technological and market changes, and is defined as follows: Coefficient K (Future suitability) = 1 + (Future suitability assessment score) / (Maximum future suitability assessment score) The future suitability score is an evaluation score by experts. The maximum future suitability score is the highest evaluation score in the past for vehicles in the same category.

[0050] Coefficient K (maintenance cost): The coefficient K (maintenance cost) is an indicator that shows the cost of owning a vehicle, and is defined as follows: Coefficient K (maintenance cost) = 1 + (annual maintenance cost) / (maximum annual maintenance cost) The maintenance cost score represents the annual cost of maintaining the vehicle. The maximum annual maintenance cost is the highest historical maintenance cost for vehicles in the same category.

[0051] Coefficient K (contrarian signal): The coefficient K (contrarian signal) is an indicator that shows the cost of owning a vehicle, and is defined as follows: Coefficient K (contrarian signal) = 1 + (contrarian evaluation score) / (maximum contrarian evaluation score) The contrarian evaluation score is an evaluation score based on market analysis. The maximum contrarian evaluation score is the highest historical maintenance cost for vehicles in the same category.

[0052] Coefficient K (conservative): The coefficient K (conservation) is an indicator of the ability to maintain the vehicle's condition, and is defined as follows: Coefficient K (conservation) = 1 + (conservation evaluation score) / (maximum conservation evaluation score) The preservation score is an evaluation score regarding the vehicle's state of preservation. The maximum preservation score is the highest historical maintenance cost for vehicles in the same category.

[0053] <Coefficient K (Regional Characteristics)> The coefficient K (regionality) is defined, for example, as follows: Coefficient K (Regional characteristics) = Coefficient K (Regional supply) × Coefficient K (Regional seasonality) × Coefficient K (Regional population change rate) The coefficient K (regional supply) is a coefficient that reflects the balance between the supply and demand for used cars in a specific region. For example, in urban areas, demand is high, so the coefficient K (regional supply) is often > 1, while in rural areas, the coefficient K (regional supply) is often < 1.

[0054] <Coefficient K (Market deviation correction)> The coefficient K (market deviation correction) is defined, for example, as follows: Coefficient K (market deviation correction) = 1 / (1 + A × absolute value (estimated value - market value) / market value) Here, the estimated value is the estimated price of a used vehicle calculated in the past.

[0055] <Coefficient K (Regional Characteristics)> The coefficient K (regionality) is defined, for example, as follows: Coefficient K (Regional characteristics) = Coefficient K (Regional supply) × Coefficient K (Regional seasonality) × Coefficient K (Regional population change rate)

[0056] The coefficient K (regional supply) is a coefficient that reflects the balance between the supply and demand for used cars in a specific region. For example, in urban areas, demand is high, so the coefficient K (regional supply) > 1, while in rural areas, the coefficient K (regional supply) < 1. It is determined based on regional market research and statistical data.

[0057] The coefficient K (regional seasonality) is a coefficient that reflects changes in demand due to population growth or decline in a particular region. For example, vehicles with high demand in the summer (such as convertibles) will have a coefficient K (regional seasonality) > 1, while vehicles with low demand in the winter (such as SUVs) will have a coefficient K (regional seasonality) < 1. This is determined based on regional market research and statistical data.

[0058] The coefficient K (regional population change rate) is a coefficient that reflects the change in demand due to population growth or decline in a particular region. For example, in areas where the population is increasing, the coefficient K (regional population change rate) is > 1, and in areas where the population is decreasing, the coefficient K (regional population change rate) is < 1. It is determined based on regional market research and statistical data.

[0059] Furthermore, the estimated price of a used vehicle may be calculated by further applying the following macro-environmental correction coefficients to the new vehicle price. The macroeconomic environmental adjustment coefficient is generated by integrating fuel prices, exchange rates, policy support, and interest rate indicators across multiple variables, and is a price adjustment coefficient based on macroeconomic factors. The emotional evaluation coefficient is calculated by performing natural language analysis on a group of SNS posts, based on the rate of positive emotions, the ratio of long-term owned posts, and the strength of the fan base.

[0060] The used vehicle price estimation device 21 calculates each of the coefficients defined above using data obtained from an external source.

[0061] <Used Vehicle Estimation Price Calculation Device 21> Figure 9 is a functional block diagram of the used vehicle price estimation device 21 shown in Figure 1. As shown in Figure 9, the used vehicle estimated price calculation device 21 includes, for example, a communication unit 75, an input unit 77, a memory 79, and a processing unit 81.

[0062] The communication unit 75 communicates with the vehicle owner terminal device 11, the buyer terminal device 15, and the auction system 25. The input section 77 is a terminal or similar device for inputting data from an external source. Memory 79 stores the program that the processing unit 81 will execute. The processing unit 81 executes the program PRG stored in the memory 79 to perform the processing of the used vehicle estimated price calculation device 21 as defined in this embodiment.

[0063] As explained above, by using the used vehicle price estimation device 21 to calculate the estimated price of a used vehicle, it is possible to calculate an efficient and highly reliable estimated price of a used vehicle by taking into account not only the physical factors of the vehicle but also a variety of other factors.

[0064] In other words, the following effects can be obtained in calculating the estimated price of used vehicles. • No market data is required, and immediate appraisals are possible for new cars and unsold vehicles. • It reflects social media and search trends, allowing for predictions of future value. • It allows for the quantification of sensibilities and cultural values, and the ability to reflect people's likeability in evaluations. • Can handle vehicles that are increasing in value, such as premium cars and limited editions. • By entering market prices, the system automatically performs correction learning, continuously improving accuracy.

[0065] According to this embodiment, used car market data is not required, and immediate appraisals are possible even for new models and imported cars for which auction and used car market data does not exist. Furthermore, this embodiment enables trend reflection and future prediction. In other words, it can reflect changes in SNS and search trends in real time and predict future value.

[0066] According to this embodiment, emotional and cultural values ​​can be quantified. By training the model with SNS sentiment analysis and fan posts, it is possible to quantify "people's likeability," which could not be reflected in conventional residual value models. Furthermore, this embodiment can also handle premium and limited-edition vehicles. The "rarity" and "contrarian demand" coefficients can appropriately reflect the trend towards premiumization. Furthermore, according to this embodiment, correction learning is possible. That is, by inputting market prices, dynamic learning correction is performed, and accuracy continuously improves.

[0067] This invention is applicable to used car appraisal, lease residual value setting, insurance premium calculation, resale simulation, and, in the future, multi-asset value estimation for real estate, reused goods, construction machinery, EV batteries, etc. This service can be applied to real-time value estimation for immediate appraisals, lease residual value setting, appropriate residual value calculation based on future value forecasts, optimization of insurance premiums according to vehicle value, resale simulations, future value forecasting services at the time of purchase, and EV battery valuation.

[0068] The present invention is not limited to the embodiments described above. In other words, those skilled in the art may make various modifications, combinations, subcombinations, and substitutions with respect to the components of the embodiments described above, within the technical scope of the present invention or its equivalents. [Industrial applicability]

[0069] This invention is applicable to a system for calculating the estimated price of used vehicles. [Explanation of Symbols]

[0070] 11…Vehicle owner terminal device 15... Buyer terminal device 21... Used vehicle estimated price calculation device 25... Auction System

Claims

1. The first step involves calculating the physical characteristic coefficient based on the vehicle type, year of manufacture, and mileage. The second step involves calculating a brand value coefficient based on the reliability of the vehicle manufacturer and the timing of vehicle model updates. A third step involves calculating a trend coefficient based on the frequency of SNS posts related to the vehicle and the search trends related to the vehicle, A fourth step involves calculating the estimated used vehicle price of the vehicle based on the new vehicle price, the physical characteristics coefficient, the brand value coefficient, and the trend coefficient. A computer-based method for calculating the estimated price of a used vehicle.

2. The first step described above is, The physical characteristic coefficient is calculated using at least one of the vehicle's image, maintenance history, equipment data, and energy correction coefficient. The method for calculating the estimated price of a used vehicle according to claim 1.

3. The second step described above is: The brand value coefficient is generated using at least one of the vehicle's sales trends and its impact on social media. The method for calculating the estimated price of a used vehicle according to claim 2.

4. The third step described above is: The trend coefficient is generated using the search trends. The method for calculating the estimated price of a used vehicle according to claim 3.

5. The fifth step involves calculating the value retention rate using data that quantifies at least one of the following: rarity, durability, cultural value, hobby appeal, future suitability, maintenance costs, contrarian signals, and preservation. It further possesses, The fourth step involves calculating the estimated price of the used vehicle based on the value maintenance and increase coefficient calculated in the fifth step. The method for calculating the estimated price of a used vehicle according to claim 4.

6. A sixth step involves calculating a market deviation correction coefficient based on the estimated price of the used vehicle calculated in the fourth step and the market reference price of the vehicle. It further possesses, The fourth step involves calculating the estimated price of the used vehicle based on the market deviation correction coefficient calculated in the sixth step. The method for calculating the estimated price of a used vehicle according to claim 5.

7. The seventh step involves calculating a regional coefficient using data that quantifies at least one of the following: regional supply volume of vehicles, seasonality, and regional population change rate. It further possesses, The fourth step involves calculating the estimated price of the used vehicle based on the regional coefficient calculated in the seventh step. The method for calculating the estimated price of a used vehicle according to claim 6.

8. At least one of the physical characteristic coefficient, the brand value coefficient, and the trend coefficient is generated by the trained model. The method for calculating the estimated price of a used vehicle according to claim 1.

9. The first step involves calculating the physical characteristic coefficient based on the vehicle type, year of manufacture, and mileage. The second step involves calculating a brand value coefficient based on the reliability of the vehicle manufacturer and the timing of vehicle model updates. A third step involves calculating a trend coefficient based on the frequency of SNS posts related to the vehicle and the search trends related to the vehicle, A fourth step involves calculating the estimated used vehicle price of the vehicle based on the new vehicle price, the physical characteristics coefficient, the brand value coefficient, and the trend coefficient. A program that causes a computer to execute something.

10. A first method for calculating physical characteristic coefficients based on vehicle type, year of manufacture, and mileage, A second method for calculating a brand value coefficient based on the reliability of the vehicle manufacturer and the timing of vehicle model updates, A third method for calculating a trend coefficient based on the frequency of SNS posts related to the vehicle and the search trends related to the vehicle, A fourth means for calculating the estimated used vehicle price of a vehicle based on the new vehicle price, the physical characteristics coefficient, the brand value coefficient, and the trend coefficient. A system for calculating estimated prices for used vehicles, equipped with the following features.

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