Car Prices Research Articles

Prediction of Selling Price of Various Cars

Abstract: – The manufacturer sets the price of a new car in the industry, with the government incurring some additional expenditures in the form of taxes. Customers purchasing a new car may thus be sure that their investment will be worthwhile. However, due to rising new car prices and buyers’ financial inability to purchase them, used car sales are increasing globally. As a result, a used car price prediction system that efficiently assesses the worthiness of the car utilizing a range of factors is required. The current system comprises a system in which a dealer decides on a price at random and the buyer has no knowledge of the car or its current worth. In reality, the seller has no clue what the car is worth or what price he should charge for it. To address this issue, we have devised a highly effective model. Regression algorithms are employed because they produce a continuous value rather than a classified value as an output. As a result, rather than predicting a car’s price range, it will be feasible to estimate its real price. A user interface has also been created that takes input from any user and shows the price of a car based on the inputs. IndexTerms – Used Car Price Prediction, Regression Algorithms, Machine Learning, Linear Regression, Ridge and Lasso Regression, Bayesian Ridge Regression, Decision Tree, Random Forest, XG Boost, Gradient Boosting.

Car Price Prediction Using Machine Learning

Abstract: The objective of the "Car Price Prediction Using Machine Learning" project is to anticipate car prices based on pertinent variables by utilizing predictive modelling and advanced data analytics. The rising need for precise and active pricing systems in the automobile sector is addressed by this initiative. The system will evaluate past vehicle data, including brand, model, manufacturing year, the mileage, type of fuel, and other details, by utilizing machine learning techniques. In order to produce specific predictions, the suggested model will be trained on an extensive dataset, noticing patterns and parallels within the data. In order to create a reliable prediction model, the research focuses on using regression techniques, such as ensemble approaches or linear regression. System of measurement like as absolute mean error and R-squared coefficient will be used to evaluate the predicted accuracy of the system in order to regulate its usefulness. If this resourcefulness is employed successfully, it will have a big impact on the automobile business for clients and venders alike by giving them a tool to evaluate fair market prices and helping them make decisions. This study enhances to the body of knowledge in price analysis using machine learning and lays the footing for future improvements in the prognostication of dynamic market trends in the automobile sector.

Research on factors affecting second-hand car market prices

This study uses a multiple linear regression model to analyze the key factors affecting second-hand car prices. Using data exploration and regression analysis, 945 samples spanning the years 2019-2024 were analyzed, and four variables were chosen for further examination. In terms of price, these factors include the impact of mileage, the year the vehicle was manufactured, the brand category, and the color. According to the findings, the most important factors that influence price are the number of miles driven and the year the vehicle was manufactured. Additionally, the category of the brand has a significant impact, while the color has a negligible effect on the price. In addition to assisting customers in making more informed choices, the findings of this study can also provide car dealers with valuable insights regarding pricing strategies and inventory management. The findings of this study also revealed that the market for used automobiles is characterized by a significant circulation and a multitude of factors that have an impact. To further refine the existing model, further research could be conducted to take into consideration additional potential influencing factors.

Study on Consumption Preference of New Energy Vehicles

The sales volume and sales volume of new energy vehicles in the market reflect the public's use of new energy vehicles. According to the feedback and demand of different people, the purchase proportion of new energy vehicles and new energy vehicles is different. The impact on economic development and the impact on the purchasing economy. The psychological impact of people using new energy vehicles. Consumers' pursuit of new energy vehicle performance (breakthroughs in speed and road use quality. Changes in conventional engine power output and motor output. The psychological impact on the use of new energy vehicles. Consumers' pursuit of new energy vehicle performance. After-sales quality assurance and service, the impact of different age groups on new energy vehicles. And the impact on the economic development of the population and the purchasing population, different age groups have different demands for new energy vehicles. After-sales service has also improved with market changes, and more and more people are pursuing the purchase of new energy vehicles. China's new energy vehicle policy will have an impact on the price of cars and buyers.

Study of Various Materials for Automobile Bumper: A Review

India leads the world in annual road fatalities (230,000), despite being a top-ten car market. This discrepancy highlights the urgent need for stricter vehicle safety regulations. While concerns regarding an 8-15% increase in car prices due to stricter standards exist, the potential for global car exports and significant lives saved outweighs these costs. This review analyses the critical role car bumpers play in mitigating accident severity. By absorbing impact energy and protecting occupants and vehicles, bumpers directly address a crucial gap in current Indian automotive safety. Implementing stringent safety regulations, including robust bumper testing programs, can significantly reduce road fatalities and injuries, aligning India's car safety standards with global best practices and fostering a safer transportation ecosystem.

Penerapan Model Regresi Linier dalam Prediksi Harga Mobil Bekas di India dan Visualisasi dengan Menggunakan Power BI

The growing message of the global automotive industry has led to an increase in used car sales, especially in unique consumer behavior markets like India. Pricing used cars presents challenges due to inconsistent factors such as mileage and age. This study aims to contribute to the understanding of the used car market in India and provide valuable information for business owners and industry players to make informed decisions. The results show that linear regression is accurate and forms a multiple linear regression model y = -1998.21+ 9.9 X1+ 1.22 X2+ 1.3 X3 -8.07 X4+ 1.83 5889267114422 and UMK 49.222702208411334. Apart from that, in the testing process the model built had an accuracy level of 71.09%. This research aims to predict used car prices by considering influencing factors, and the results will be presented visually to provide business owners with more interactive information.

Car Price Prediction Using Regression Techniques of Machine Learning

A car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars in Bosnia and Herzegovina, we applied three machine learning techniques (Artificial Neural Network, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble. The data used for the prediction was collected from the web portal autopijaca.ba using web scraper that was written in PHP programming language. Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into Java application. Furthermore, the model was evaluated using test data and the accuracy of 91% was obtained.

Revolutionizing the used car market: Predicting prices with XGBoost

In contemporary dynamic automobile industry, informed decision-making is crucial for success. Accurate sales predictions are essential for optimizing operations and resource allocation in the used car market. This article explores the application of machine learning, specifically the eXtreme Gradient Boosting(XGBoost) algorithm, in predicting used car prices in the UK. The dataset consists of over 110,000 data points from various brands, including features like model, year, mileage, and more. Data preprocessing, including outlier removal and feature scaling, is performed to prepare the dataset. XGBoost, a powerful machine learning algorithm, is selected for price prediction and compared with traditional regression models. The results show that eXtreme Gradient Boosting(XGBoost) outperforms other models in terms of R2 score, Mean Absolute Error, and Mean Square Error. Its accuracy and efficiency make it a valuable tool for both buyers and sellers in the used car market. However, challenges such as data quality, feature engineering complexity, and model interpretability remain. Future research directions include improving data quality, exploring advanced feature engineering techniques, and integrating external data sources. This study highlights the potential of machine learning in enhancing decision-making in the dynamic used car market.

Predicting prices for Pre-owned cars using ML Algorithms

Predicting prices for Pre-owned cars using ML Algorithms

Shifting towards luxury cars: The price and environmental effects of Beijing’s vehicle lottery system and an alternative policy

This paper investigates changes in the composition of vehicle fleets after Beijing implemented a vehicle lottery system (VLS) in 2011 that randomly allocates a quota of license plates to lottery participants. Using the car registration data, we examine the consumption response at the beginning of the restriction. Our analysis reveals that VLS effectively eliminated over one million additional car registrations in two years, however it also increased purchases of cars that are heavier, have larger engines, and are less fuel-efficient. We highlight that the change was associated with a greater reduction in the retail price of luxury cars compared with economy cars post-VLS implementation. Through a difference-in-differences (DID) analysis, we show that the average retail price in Beijing dropped by an additional 2 percent (about $0.4k) for economy cars and 5.8 percent (around $4.3k) for luxury cars, compared to cities without VLS. This price change increased the attractiveness of luxury cars relatively to economy cars. Furthermore, we build a structural model to estimate the consumer demand on vehicles as well as to simulate and compare other potential policies. Given the drawback of VLS that partially offsets the policy goal of emission elimination, we propose a market-based policy to save fuel consumption while achieving the same target of controlling vehicle growth as VLS.

Prediction And Investigation for U.S. Used Car Prices and Factors Related to Price

In contemporary society, as transportation infrastructure continues to advance, a growing number of families opt for car ownership as a means of commuting. However, constrained by budget considerations, many households opt for pre-owned vehicles. This underscores the significance of comprehending the dynamics of the used car market and delving into the factors that influence the pricing of these previously owned automobiles. The author found a data set of used cars in United States on Kaggle and used it for analysis. This dataset contains data about 762,091 used cars scraped from cars.com. The data was collected in Apr, 2023. The research question is how to predict the used car prices and what factors are related to the used car prices. The author uses Linear Regression and Random Forest Regression to predict used car prices. The author found the factors related to the price of used cars as well. The study found that the Random Forest Regression method is more accurate in predicting used car prices. A total of 17 used car factors correlated with used car price. Such as mileage, car age, mpg and so on.

Research on Prediction of Insurance Compensation Amount after Fire Disaster

Fire is one of the major disasters that seriously threaten public safety and social development, so it is particularly essential to predict the amount of insurance compensation after a fire. This paper predicts the number of fire occurrences from various aspects such as region, housing, and car fires based on the LSTM model. A model for predicting insurance compensation after a fire is established based on the LSTM-Poisson distribution model and factors such as average house prices. Taking the number of fire occurrences in four regions of the UK from 2001 to 2021 as a case, the fire insurance compensation model is used to predict based on the average house prices and car prices in the UK, providing a reference for insurance companies in terms of fire insurance compensation.

Veblen was Right: Why People Seek Unaffordable Cars

We study the connection between financialization and consumerism, and claim that greater availability of consumer loans affects consumerist culture and promotes the institution of conspicuous consumption. We extend the ideas of Thorstein Veblen who coined the term of “conspicuous consumption” to illustrate our thinking with the empirical evidence on car purchases financed through bank loans in Russia’s Rostov Region. We find that leveraged car buying has become embedded over the past 20 years. Social positioning drives people to seek more expensive cars, whereas getting a bank loan allows to buy an otherwise unaffordable car. We see a sign of conspicuousness in the fact that household income grows slower than the average price of a new car financed by a car loan, as well as the amount of loans. Our contribution is that a basic concept of institutional theory (conspicuous consumption) is combined with elements from other research approaches, namely the social significance of bank lending, functional differentiation of credit, and the effects of financialization.

A Latent Factor-Based Bayesian Neural Networks Model in Cloud Platform for Used Car Price Prediction

The selling price of a used car can be predicted based on its historical information. Accurate and reasonable used car price evaluation will be able to promote the healthy progress of the used car industry. Current used car price prediction models are troubled by data quality and the inability to provide estimates of uncertain information, and the prediction accuracy cannot meet the needs of real-world scenarios. In this work, we propose a price prediction model based on a Bayesian neural networks with latent factor (LFBNN) in a cloud platform, which is capable of performing latent factor extraction operations on structured data of used cars as a way to remove the effect of noise in the dataset on the model performance. Moreover, the weight parameters of the Bayesian neural networks (NNs) are represented as probability distributions, which is equivalent to introducing uncertainty and acting as a regularization effect compared to a NN with fixed weights, thus making it possible to alleviate the overfitting problem. The LFBNN model uses a cloud platform for data transmission and storage, enabling it to run faster. Compared with the current benchmark models, the model proposed in this work achieves excellent experimental results on two real datasets. The experimental results on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Car_{1}$</tex-math></inline-formula> dataset are 1.498 for mse, 1.224 for rmse, and 0.995 for MAE, and 1.519 for mse, 1.232 for rmse, and 1.002 for MAE on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Car_{2}$</tex-math></inline-formula> dataset.

The Influence of the Federal Reserve System Interest Rate Hike on Tesla: Empirical Research Based on ARIMA Model

On 23 March 2022, the Federal Reserve System announced interest rate hikes and increased 25 basis points. After that, a series of negative monetary policies were carried out. Tesla CEO stated that the price of cars must be reduced when interest rates rose sharply. This means that Tesla's stock price will continue to decrease. The 25 -basis points raised the rate of interest rates and the highest interest rate reached 5 % to respond to inflation. The rate hikes of The Federal Reserve System have a great impact on the world. This article selected all stock data from Tesla from the listing to August this year (including daily data, weekly data, and monthly data) and uses ARIMA model to model and analyses the data. It will compare with the actual value of the Federal Reserve System. According to the study, the trend of the decline in stock prices after the RECERAL Reserve System raised interest rates is the same as the trend of research prediction in this article. Its interest rate hikes will cause Tesla's stock price to fall.

Analisis Resiko Transformasi Mobil Berbahan Minyak ke Mobil Listrik Dengan Decision Tree

The development of the world of transportation has increased rapidly, this is marked by the emergence of electric cars. The changes experienced by society are so fast to switch from fuel cars to electric cars. Electric cars have various advantages of being environmentally friendly, not producing pollutant gas emissions, being faster, cost-effective and more comfortable. In addition to the advantages provided there are also several risks from the transformation of oil-fueled (bbm) cars into electric cars including expensive car prices, charging infrastructure, shorter mileage, electric car batteries have a limited lifespan. This study aims to analyze the risk mitigation of transforming oil-fueled cars into electric cars. In this case study, the researcher uses two kinds of attributes, namely the price of the car and the emissions it produces. The method used is to calculate each gain information from the attribute used by looking at the highest gain value, it will be known how big the risk is for the transformation of oil-fueled cars (bbm) to electric cars. and what factors (attributes) most determine the risk. . It is hoped that this research can help electric car developers and entrepreneurs to continue to improve the performance of existing electric cars.

Comparative analysis of data classification methods for prediction of trade-in auto prices

In the presented article, machine learning algorithms are used to predict the price of cars. Forecasting the price of cars is one of the most important issues in modern times, because the number of car users is increasing year by year worldwide. Therefore, it may be interesting for many car owners to know the approximate price of cars in advance. The vehicle’s build date, mileage, size and other parameters are essential data for the machine learning process. Based on these parameters, the data needed to predict the price of any car was classified and a training dataset was created. Based on this data set, some interesting predictions were made. The purpose of the article is to consider the pre-processing of data, to determine and analyze what achievements the direction of artificial intelligence has achieved on the basis of predicting the price of cars. Hybrid forecasting methods using statistical analysis and machine learning methods were used in the research.

Endowment Effect: From the Perspective of Employees’ Loyalty, Perceived Value and Used Car Pricing

The "endowment effect," a central cognitive bias in behavioral economics, represents individuals' tendency to assign a higher value to items they own compared to identical or similar items they don't possess. This research paper presents a comprehensive exploration of this effect and its diverse applications in three significant areas: bolstering employee loyalty, understanding the moderating role of emotional states in the perceived value of goods, and discerning pricing mechanisms in the secondhand car market. By leveraging the endowment effect, businesses can enhance employee loyalty and develop more effective pricing strategies in secondhand markets. Inherent in these applications is the opportunity to capitalize on inherent cognitive biases to optimize outcomes across different economic activities. Furthermore, understanding the interaction between emotional states, particularly positive emotions, and the endowment effect provides insights into consumer behavior, influencing their perceived value and purchasing intentions. The interplay between the endowment effect and emotional states offers a nuanced understanding of the multifaceted nature of consumer behavior. Thus, this research underscores the far-reaching implications of the endowment effect, highlighting its pervasive influence in our economic decision-making processes.

Comparative Analysis of Various Machine Learning and Deep Learning Approaches for Car Resale Price Prediction in the Turkish Market

With escalating environmental concerns worldwide, the shift towards second-hand car markets has emerged as an eco-friendly alternative to reduce the carbon footprint associated with manufacturing new vehicles. However, the lack of accurate and efficient price prediction mechanisms may impede the growth and efficiency of these markets. This study, focusing on the Turkish second-hand car market, contributes towards addressing this gap by introducing a unique, comprehensive dataset gathered from various online markets across Turkey, thereby offering a broad spectrum of data pertaining to different vehicle types, specifications, and resale conditions. The study employs both classical machine learning methods, specifically decision trees, and deep learning models to predict used car prices. This comparative analysis aims to assess the potential of these methods in improving the predictability and transparency of resale price determination. Despite the superior performance of decision tree models, the study found that deep learning techniques achieved comparable results, indicating their potential for further optimization and enhancement. The accurate prediction of resale prices could streamline the operations of second-hand car markets, increasing their appeal to potential buyers and sellers. This could, in turn, contribute to environmental sustainability by encouraging the usage of pre-owned vehicles, which can substantially reduce the demand for new cars and thereby lower the environmental impact associated with their production.

Car depreciation: How much is new car worth after purchase in Uzbekistan?

Depreciation of a car is a decrease in its value over time. World practice shows that depreciation depends on the car model, make, content and other factors, but depreciation of a new car can reach 20% (or even more) after purchase. In the local market of Uzbekistan due to shortage of supply budget cars, the demand is high. UzAuto Motors is only budget car manufacturer in the country. At the end of November 2023, in the secondary market offer price of new cars is less than one in the primary market. In this article this case was analyzed, calculated the real paying price (in USD) of UzAuto Motors cars (Onix PR2) considering waiting time. It is found that nowadays people cannot make profit from resale.