Car Prices Research Articles

An Analytical Study on Global Automotive Price Trends and Influencing Factors

This study analyzes global automotive price trends and the key factors influencing these trends from 2018 to 2023. Using a mixed-method approach, it examines historical data, market conditions, and future projections. The findings highlight the impact of technological advancements, economic conditions, government policies, and changing consumer preferences on automotive prices. The study reveals a consistent increase in electric vehicle (EV) adoption, driven by government incentives and environmental awareness, alongside rising new car prices influenced by higher production costs and advanced features. Inflation and raw material costs have significantly impacted vehicle prices, while government subsidies have facilitated the broader acceptance of EVs. The results underscore the importance of understanding these dynamics for stakeholders to navigate the automotive market effectively. Future research should explore the long-term effects of emerging technologies and evolving regulations on automotive prices. Insights from this study are useful for manufacturers, policymakers, and consumers to make informed decisions in a rapidly evolving industry.

Stacked Generalization with Sequential-Model Based Optimization for estimating Used Car Valuation in Indonesia

In Indonesia, the purchase and sale of used vehicles is a common practice. The valuation of a used vehicle is influenced by several factors, making it challenging to determine an appropriate selling price. To address this issue, this study employs a stacked generalization (stacking) algorithm to integrate Machine Learning (ML) techniques that have demonstrated efficacy in prior research on used car valuations. The Sequential Model-Based Optimization (SMBO) algorithm is employed to achieve high accuracy while ensuring an efficient hyperparameter optimization process. The initial price of a vehicle is undoubtedly a significant determinant of its resale value. However, this fact is frequently overlooked in previous studies on developing car price estimation models. This study makes a contribution to the field by addressing this issue. The use of the initial price as an input for the model enables two distinct types of analysis: one for the assessment of used car prices and the other for the measurement of the degree of residual valuation of used cars in relation to their initial costs. The results demonstrated that the optimized stacking model exhibited superior predictive ability compared to the other algorithms in both analyses. Feature analysis substantiated the considerable influence of the initial price on the used car's price. This study also corroborates the assertion that accurately predicting the valuation of a used car cannot be achieved by solely considering the usage of the previous owner, such as the car's age and mileage. It is crucial to take into account the car's original attributes, particularly its initial price.

Improve Metode Lightgbm untuk Prediksi Harga Mobil Bekas Menggunakan Hyper-Parameter Tuning

This study aims to predict used car prices using the LightGBM method and hyperparameter tuning techniques in the context of data science. The analysis process includes collecting historical data on used cars, preprocessing the data to clean and encode variables, and splitting the data into training and testing sets. The LightGBM model was trained and optimized through hyperparameter tuning using GridSearchCV to improve model performance. The model was evaluated using metrics such as Mean Squared Error (MSE) and R-squared. The results indicate that the well-optimized LightGBM model can accurately predict used car prices with high accuracy. The low MSE value (35207938112.028404) and high R-squared value (0.9462871489515565) demonstrate the model's excellent predictive quality. This research provides deeper insights into the factors influencing used car prices and contributes to the development of effective and reliable predictive models.

Investigation and Analysis of the Contribution of Chinese Electric Vehicle Social Organizations’ Standardization Innovation to Intelligent Optimization Research and Development Investment

Intelligent design has been the direction pursued by international electric vehicle (EV) research and development (R&D) teams in recent years. This paper analyzes the problems of unsustainable development in the current product design of EVs in China, such as high R&D investment, high innovation risks, and low R&D input–output ratios. It explores the issues related to intelligent design, R&D investment, car prices, and safety in the field of EVs in China, and it proposes the concept of optimizing intelligence to optimize the design investment of EVs in China. On the basis of the development situation and the existing problems of social organization standards that gather innovative technologies for EVs, this paper used data from the national social organization standard information platform as the research object and analyzed important data, such as the quantity of the information of relevant social organizations and professional fields of social organization standards, through mathematical methods. The article proposes an optimization design scheme for EV products in China, combining intelligence and practicality from the perspective of the optimizing intelligent design, and it models the construction of EV optimization design. The quantitative relationship between the two schemes before and after optimization design is compared in terms of cost savings in intelligent design, the improvement of social benefits, and the enhancement of EV cost performance. The comparative study found that intelligent optimization design reduced the R&D cost of EVs by 45.24%, and the social benefits of R&D investment increased by 29.51%.

Car Price Prediction Using Enhanced Technique

The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs. The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs.

Comparative Analysis of Machine Learning Algorithms for Used Car Price Prediction

After 2021, over 90 million passenger automobiles were produced, marking a significant increase in auto production. This growth has led to a flourishing used car market, which has become a highly lucrative sector. One of the most critical and fascinating areas of research within this market is automobile price prediction. Accurate price prediction models can greatly benefit buyers, sellers, and businesses in the used car industry. This paper presents a detailed comparative analysis of two supervised machine learning models: K-Nearest Neighbour and Support Vector Machine regression techniques, to predict used car prices. We utilized a comprehensive dataset of used cars sourced from the Kaggle website for training and testing our models. The K Nearest Neighbour algorithm is known for its simplicity and effectiveness in regression tasks. On the other hand, the Support Vector Machine regression technique uses a different approach, finding the optimal hyperplane that best fits the data. Both methods have their strengths and weaknesses, which we explored in this study. Our results indicated that both KNN and SVM models performed well in predicting used car prices, but with slight variations in accuracy. Consequently, the suggested models fit as the optimum models and have an accuracy of about 83 percent for KNN and 80 percent for SVM. The results indicate that the KNN model slightly outperforms the SVM model in predicting used car prices.

Second-hand car price prediction based on multiple linear regression and random forest

The second-hand car market is a hot topic. Buying a second-hand car has advantages in price and many other aspects. Therefore, it is important to establish a good price prediction model. This paper will explore the factors that affect the price of second-hand cars. After analyzing and learning many kinds of literature, this paper establishes a multiple linear regression model and a random forest model and makes a comparative analysis of the model effect. The sum of the square error and R-square value of the random forest are better than the multiple linear regression model. Among the factors affecting the price of second-hand cars, the year of production has the greatest impact on the price, which shows that the age of the year is an important factor in determining the price of second-hand cars. The next most important factor is the number of kilometers traveled, followed by fuel type and transmission type-finally, engine displacement, number of transfers and number of seats. The random forest model established in this paper has better application value to price prediction.

Research on Influencing Factors and Trend Prediction of New Energy Second-hand Car Prices

With the advancement of technology, due to their lower cost and high performance than traditional fuel cars, new energy vehicles have been gradually occupying an increasingly important position in the economy and society for years. However, in addition to the continuous expansion of the industry, lots of consumers and dealers have turned their attention to the post production issues of new energy vehicles - the second-hand market. Its widely believed by the scholars that the resale value of new energy vehicles is influenced by a great many factors, mainly including the using time, mileage, policies and so on. This article used the Tesla model 3 as an example and established interpretative structural modeling (ISM) and ordinary least squares (OLS) models, in order to do factor analysis and trend prediction on the resale value of new energy vehicles. After containing quantities of results, the research ultimately found out the specific negative correlation between two major elements and the second-hand price of the new energy cars, together with simple point predictions through OLS models. In this condition, it is feasible for the dealer and costumers of the new energy vehicles market to get a standard in second-hand prices, while the theoretical field could also be partly extended in the aspect of mathematical statistics and economic analysis.

Machine Learning-Based Models for Accurate Car Prices Prediction

The used car market is becoming more and more popular in various countries, but some car trading platforms have inaccuracies in predicting prices. So, it is necessary to select machine learning models with high accuracy to predict used car prices for both buyers and sellers. This study selected three factors that have the greatest impact on used car prices, which are: car name, car use year, and car use mileage. In the selection of dataset, this study chooses more than 2000 data from four brands of BMW, Volkswagen, Acura, and Tesla which are very popular in the market to predict. In the part of comparing the accuracy of machine learning models, this paper uses three machine learning models: linear regression, decision tree regressor and random forest regressor. Based on criteria such as root-mean-square error and R-variance, with K-Fold cross validation method to compare the advantages and disadvantages of every model. Through the analysis, all the test criteria consistently show that random forest regressor has the highest performance and can achieve an R-square value of 0.8562. After getting the suitable model, accurate price prediction will be realized in more car trading platforms to help buyers and sellers to understand the real price of used cars.

Machine Learning Powered Chatbot for Prediction of Used Car Price

Machine Learning Powered Chatbot for Prediction of Used Car Price

A Machine Learning Approach for Study of Emission Standards on Used Car Prices in India

A Machine Learning Approach for Study of Emission Standards on Used Car Prices in India

Prediction of Resale Value of Pre-Owned Luxury Cars in the Indian Market Employing Machine Learning Techniques

Abstract: The market for second-hand luxury cars in India is witnessing a significant surge, expected to grow at a rate of 16.30% from 2024 to 2032. This growth is fueled by increased car manufacturing, rising disposable incomes, and a shift in consumer preferences towards luxury brands. However, accurately determining the resale value of these vehicles presents a challenge due to various influencing factors. In this dynamic market, informed decision-making is crucial for luxury car buyers. Digital platforms have revolutionized access to real-time market data, helping both buyers and sellers stay updated on pricing trends. Our research explores the complexities of predicting prices for pre-owned luxury cars and introduces a predictive analytics framework using advanced machine learning algorithms. We collected and preprocessed a comprehensive dataset and conducted an in-depth exploratory data analysis. Various regression techniques, including Linear Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting, were employed to forecast prices. These models were evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to identify the most accurate predictive model. This study offers a systematic solution for price prediction, enhancing the buying process for stakeholders in the second-hand luxury car market

Assessing Public Opinions of and Interest in Bidirectional Electric Vehicle Charging Technologies: A U.S. Perspective

An increasing number of battery-electric vehicles (BEVs) have bidirectional charging technology that provides new benefits to motorists, homeowners, and power grid operators. A web-based survey investigates the willingness of over 300 Americans to pay for added bidirectional charging features, namely, vehicle-to-load (V2L), vehicle-to-home (V2H), and vehicle-to-grid (V2G) technologies, along with their expected use frequency. Summary statistics suggest that Americans are willing to pay (WTP) an average of $280 and $776 on top of the price of a new car for V2L and V2H, respectively. About 51.3% would let their power company discharge their vehicle via V2G during grid emergencies if compensated and guaranteed a minimum battery level. Interval regression and ordered probit equations explain how demographics, travel patterns, and attitudinal variables affect the response variables, including WTP for bidirectional charging features and expected reliance on technology. The statistically and practically significant relationships suggest that adults over 34 have lower WTP values for V2L and V2H, and those in households with more vehicles plan on more bi-directional charging, as expected. The findings have implications for policymakers, manufacturers, and stakeholders involved in the BEV ecosystem, informing their decision-making processes related to integrating and commercializing bidirectional charging technologies. These models may even help power grid planners understand who is likely to adopt V2G technology, enabling them to aggregate and shift BEV loads to help manage the grid in parallel and isolation.

Second-hand car price prediction based on multiple linear regression models

With the advancement of the automotive manufacturing industry and the convenience of public transportation, the second-hand car market continues to expand. To comprehensively evaluate the price of used cars considering various factors, there is an urgent need for a computational model based on big data and machine learning. This article aims to fulfill this requirement, this article introduces a multiple linear regression model based on machine learning technology, which is applied to predict the prices of second-hand cars. In this study, the author digitized categorical variables and removed factors irrelevant to numerical variables using a dataset containing nearly 1500 samples from the second-hand car market. By constructing a multiple linear regression model with independent variables such as insurance validity, fuel_type, seats, ownership, transmission, mileage (kmpl), engine (cc), kms_driven and registration year with price (unit: ten thousand) as dependent variable. The root mean square error (RMSE) was finally obtained as 13.939.

Spillover effects of online comments on high-involvement product sales: evidence from the Chinese automobile industry

PurposeOnline comments significantly impact consumer choice and product sales. Existing research focuses on the direct effects of online comments on product sales, whereas studies on the spillover effects of online comments are relatively limited, especially for high-involvement products. This study explores the impact of online comments of competing products on focal product sales in high-involvement products.Design/methodology/approachData mining techniques are used to collect 72,367 online comments from the Autohome platform, and sentiment analysis algorithms are used to quantify the textual information for subsequent analysis. Specifically, two panel two-way fixed-effects models are constructed to explore the impact of the average valence and quantity of online comments of competing cars on focal car sales, and analyse this impact in terms of heterogeneity across car price levels, while the moderating effect of online comments of competing cars is explored.FindingsThe results show that the average quantity of online comments of competing cars has a significant effect on the sales of the focal car in the overall sample, while the average valence of online comments of competing cars does not have a significant spillover effect. Moreover, the spillover effect varies by car price level. For high-priced cars, the average quantity of online comments of competing cars significantly and negatively affects focal car sales, and the average valence of online comments of competing cars significantly and negatively moderates the effect of the valence of focal car online comments on its sales. For lower-priced cars, online comments of competing cars don’t significantly affect focal car sales.Originality/valueThis study not only enriches the theory of online comments and high-involvement product sales, but also provides reference and guidance for exploring spillover effects of online comments for other products.

Comparative Analysis of BEST Buses and Electric Buses in India

An overwhelming trend that marks the beginning of the transportation sector’s energy transition is the electrification of public transportation. The public sector’s considerable influence and predominance of public ownership define the transportation industry. Consequently, tendering processes Are frequently used to determine which bus delivery options are the most efficient. Based on a comprehensive analysis of the Polish bus market and in-depth interviews with individual vendors, this paper compares, assesses, and delineates the distinctions between the criteria applied in tenders for battery electric buses and diesel buses. The article additionally endeavors to ascertain whether the proportion of the vehicle price in the life cycle cost or total cost of ownership aligns with the weight assigned to the “vehicle price” criterion. The outcomes show There isn't a noticeable distinction between diesel and battery electric buses' tender requirements. Institutions that had previously created diesel bus acquisition patterns applied those patterns, for the most part, to battery electric bus tenders. As a result, the weights assigned to the criteria in tenders do not account for the benefits and drawbacks of either technology. Procedures for tendering are modified to meet regional needs and operational specifications. On current routes and schedules, electric buses frequently take the place of conventionally powered vehicles. Operational requirements are therefore known. This makes it possible to calculate the required number of vehicles as well as the fundamental technical and operational parameters (such as choosing the best charging technique and battery capacity). The charging strategy will therefore have an impact on the overall cost of ownership Opportunity charging is preferred for longer mileages, and overnight charging is preferred for shorter assignments.The electrification of public transportation is a massive trend that represents the first stage of the energy transition of the transportation sector. The transportation industry is defined by the significant influence of the public sector and the prevalence of public ownership. As such, tendering proceduresare widely employed to identify the most effective bus delivery options. This paper compares, evaluates, and discusses the differences between the specifications used in tenders for battery electric buses and diesel buses. It is based on a thorough examination of the Polish bus market and indepth interviews with specific vendors. The piece also aims to determine whether the percentage of the car price in the life cycle cost or overall cost of ownership is consistent with the importance given to the "vehicle price" factor. The results reveal no appreciable.

PREDICTION OF SELLING PRICE OF VARIOUS CARS

Abstract – Predicting the selling price of cars is a complex problem that involves analyzing various factors, including the car’s make, model, year, mileage, condition, and other relevant attributes. This study aims to develop a predictive model that accurately estimates the selling prices of cars using machine learning techniques. By leveraging a dataset containing historical data on car sales, we employ multiple regression analysis and other advanced algorithms to identify patterns and key determinants that influence car prices. The proposed model is validated using a separate test dataset to ensure its accuracy and reliability. The results demonstrate that our model can effectively predict car prices, providing valuable insights for buyers, sellers, and automotive industry stakeholders. This predictive tool can assist in making informed decisions, enhancing market efficiency, and ultimately contributing to a more transparent automotive market. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Data Analysis and Prediction of Of Various app

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Exploratory Data Analysis and Prediction of Rating Of Various app on Google Play Store

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Disentangling policy and structural effects on car-ownership for car-owning and carless US households during and after the COVID-19 pandemic

This research analyzed car-ownership in the US during and after the COVID-19 pandemic, by utilizing a nationally representative household survey spanning from January 2020 to March 2022. Using a multilevel Hurdle model with month and state random effects, vehicle ownership trends are separately modeled for car-owning and carless households within the same modeling framework, while accounting for endogeneity and unobserved heterogeneity. An increase in the total amount of economic stimulus funding received increased the odds of vehicle ownership, while high car prices, high levels of housing expenditures, living in multi-family dwelling, and being from minority and low-skilled worker families negatively affected car-ownership. Greater household labor force participation, increases in household size and young persons in the household, living in states with high COVID caseloads and with moderately stringent Stay-at-Home social distancing mandates affected car-ownership propensities differently for car-owning and carless households. The significance of the research is that it disentangled pandemic-related and transportation policy variables from changes in household structure, living arrangements and employment-related characteristics. The analysis jointly considered how short-term pandemic-related influences as well as long-term demographic and occupational factors differently affect car-ownership for car-owning households and those without cars. Policy implications are drawn for consumer protection in the car ownership process, auto loan forbearance in future economic disruptions, strategies for public transportation which has continued to suffer from lower levels of use, and sustainability programs due to higher volumes of older used cars.