Car Prices Research Articles

Analysis of linear regression on used car sales in Indonesia

There are few things that drive the business of selling used cars in Indonesia continues to increase, namely new car prices are higher. So the prospective buyers the ability to purchase power not too strong, buying a used car is one option. Linear regression is a statistical analysis of several variables relationship model according to the form of the relationship of linear equations explicitly. The purpose of this research is to apply the method of Linear Regression on selling used car Toyota Innova, and Honda CRV in Indonesia, especially in the cities of Jakarta, Bandung, Surabaya, and Semarang. Used car sales prices are influenced by the age factor car, car mileage, color car, car transmission, and car type. The results showed, an analysis of age factor car sharing and car has a mileage rating of 62.6% level of confidence. By adding some other variable that is the color of the car, the transmission and the cities of car sales has a value of more than 75% accuracy.

Value Model of Used Equipment: Market Trap for Gain

There is a belief that the acquisition of new industrial equipment is less profitable than the purchase of used equipment. The ground for this effect may be found in rather significant decline of the price of industrial equipment after the first years of acquisition. Well known that the car price falls down by 20% - 30% during the first year of ownership. For industrial equipment, the price does not fall so fast, but this effect is observed too. The article considers the profitability of purchasing of used equipment in the framework of Preserved Value Approach. The findings suggest that used equipment is indeed usually priced below its value. This effect provides the buyer of used equipment with an additional benefit, the value of which depends on the actual interest rate of the buyer. Moreover, the buyer may choose a strategy for extending the period of value-added use beyond the limits of economic life. In this case, the purchase of used equipment becomes especially profitable. The reason for this effect seems to be the existence of some “market punishment” for the owner, who acquired industrial equipment but is not able to use it effectively.

ОПРЕДЕЛЕНИЕ СРЕДНЕЙ ЦЕНЫ НОВЫХ АВТОМОБИЛЕЙ В РОССИИ С ПРИМЕНЕНИЕМ ДИНАМИЧЕСКОЙ МОДЕЛИ

The article deals with the issues of sales in the passenger car market and the factors that influence these sales. The main trends of the automotive market of new cars are studied. The reasons that contribute to the growth of prices for new cars are determined. On the basis of statistical data on car prices for the last four years, an econometric model of the time series is constructed, taking into account seasonal changes in sales. The obtained model and its application possibilities for obtaining a forecast of the average price of a middle-class passenger car in the short term are considered. Based on this model, a forecast of the average price for four periods is made, which is confirmed by the actual values of prices for middle-class cars in 2019.

Вопросы качества экологического состояния транспортной среды

The article considers the main factors that violate the ecological balance of pollutants from the functioning of road transport, in order to ensure the requirements for the design of a positive impact on the environmental safety of the road environment. The most important invention of mankind is transport, which made it possible to combine all spheres of human labor into a single system. At the same time, at the present stage, one of the causes of environmental damage is road transport.The relevance of this topic is due to the fact that every year the number of vehicles is only growing. Over 13 years, the growth of the fieet in Russia was 61% from 27 million in 2006 to 43.5 million. units in 2019 The development of transport networks, the growth of the human population, the decline in car prices and the General improvement in the material well-being of the population inexorably leads to an increase in the number of vehicles.

The Regression and Correlation Analysis of Carried Persons by Means of Public Passenger Transport of the Slovak Republic

The increase of environmental awareness of the population, technical innovations in transport, and the improvement of transport services quality moderately increase the growth of public passenger transport also in the Slovak Republic. On the other hand, the growth of the living standard, the employment rate and the price of personal cars fulfils/exceeds the capacity of the transport infrastructure. In order to determine an effective strategy of the development of passenger transport there are needed regression and correlation analyses of transport performances in public passenger transport in the Slovak Republic. The article presents results of analyses of transport performances in public passenger transport in the Slovak Republic since 1995 until 2017. Based on the realised analysis of carried persons in the Slovak Republic there are introduced regression and correlation analyses of selected factors where their impact on the transport performance itself is anticipated.

Heteroscedastic BART via Multiplicative Regression Trees

Bayesian additive regression trees (BART) has become increasingly popular as a flexible and scalable nonparametric regression approach for modern applied statistics problems. For the practitioner dealing with large and complex nonlinear response surfaces, its advantages include a matrix-free formulation and the lack of a requirement to prespecify a confining regression basis. Although flexible in fitting the mean, BART has been limited by its reliance on a constant variance error model. Alleviating this limitation, we propose HBART, a nonparametric heteroscedastic elaboration of BART. In BART, the mean function is modeled with a sum of trees, each of which determines an additive contribution to the mean. In HBART, the variance function is further modeled with a product of trees, each of which determines a multiplicative contribution to the variance. Like the mean model, this flexible, multidimensional variance model is entirely nonparametric with no need for the prespecification of a confining basis. Moreover, with this enhancement, HBART can provide insights into the potential relationships of the predictors with both the mean and the variance. Practical implementations of HBART with revealing new diagnostic plots are demonstrated with simulated and real data on used car prices and song year of release. Supplementary materials for this article are available online.

Forecasting Automobile Sales in Turkey with Artificial Neural Networks

This study aims to reveal significant factors which affect automobile sales and estimate the automobile sales in Turkey by using Artificial Neural Network (ANN), ARIMA, and time series decomposition techniques. The forecasting model includes automobile sales, automobile price, Euro and Dollar exchange rate, employment rate, consumer confidence index, oil prices and industrial production confidence index, the probability of buying an automobile, female employment rate, general economic situation, the expectation of general economic situation, financial status of households, expectation of financial status of households. According to the regression results, changes in Dollar exchange rate, the expectation of financial status of households, seasonally adjusted industrial production index, logarithmic form of automobile sales before-one-month which have a significant effect on automobile sales, are found to be the significant variables. The results show that ANN has a better estimation performance with MAPE=1.18% and RMSE=782 values than ARIMA and time series decomposition techniques.

PUBLIC PERCEPTION OF THE VEHICLE OWNERSHIP TRANSFER FEES (BBNKB) INCREMENT POLICY IN BANDAR LAMPUNG

<p><em>The aim of the study is to estimate the number of vehicles with </em><em>Lampung outside p</em><em>olice numbers</em><em> </em><em>operating in Lampung and to find</em><em> </em><em>out the public perceptions</em><em> </em><em>of</em><em> </em><em>the Lampung</em><em> </em><em>Provincial</em><em> </em><em>Government's policy through </em><em>Local Regulations</em><em> </em><em>Number</em><em> 2 of 2011</em><em>, </em><em>regarding the increase in v</em><em>ehicle ownership transfer fees</em><em> (BBNKB) from 10 percent to 12.5 percent, along with willingness to pay from the policy. This research applies survey method in Bandar Lampung</em><em> </em><em>city with the population of the people who are the owner of new motor</em><em> </em><em>vehicles including the four-wheeled and two-</em><em>w</em><em>heeled which were</em><em> </em><em>purchased starting</em><em> </em><em>in</em><em> </em><em>20</em><em>1</em><em>2 outside</em><em> </em><em>the province</em><em> o</em><em>f Lampung</em><em>. </em><em>The sampling method used is cluster sampling, meaning that the population is divided into several clusters. As the population of vehicles which have no BE police number operating in Lampung is unknown, then the number of the sample is set to 100 respondents.</em><strong><em></em></strong></p><p><em>Based on the result of the survey, it is unravelled that there are 18 percent of cars which have non-BE plate number operating in Lampung, and for motorcycles, there are only 4 percent. Another finding shows the fact that respondents buy vehicles outside the province of Lampung because the price difference is quite significant and they do not do second BNKB in Lampung Province in accordance with its operational area. From the willingnes to pay, all respondents approve that if the rate of BBNKB decreases back to 10 percent, most of them will return to buy a new vehicle in Lampung due to the fact that there will be smaller price difference between the price of a new car purchased in Lampung Province and new car purchased outside Lampung Province. BBNKB tariff reduction will provide opportunities for trade of vehicles in Lampung Province to be more competitive, thus, they can develop better. Moreover, there will be more exciting investments and more job fields in the automotive sector.</em><em></em></p><p><strong><em>JEL Classification: </em></strong><em>H20,<strong> </strong>H21, H24</em></p><p><strong><em>Keywords: </em></strong><em>BBNKB Tariff, Public Perception, Willingness to Pay</em></p>

How are fuel efficient cars priced? Evidence from eight EU countries

Do consumers value the fuel economy of cars? We examine this question taking advantage of the recent introduction of hybrid and battery-electric vehicles, which promise to reduce the use of fossil fuels in road transport, and hence carbon emissions and dependence on oil imports. The price of these vehicles may however create hurdles to their widespread adoption. Automakers claim that these technologies come with higher production costs. Consumers should be willing to pay a price differential for these cars just equal to the savings in fuel costs—unless their utility depends on considerations beyond car quality, performance and fuel economy.We specifically ask three related questions. First, do consumers value fuel economy, even among all-electric and hybrids? Second, do the prices of electric vehicles reflect the so-called “range anxiety”? Third, is there evidence of an all-electric or hybrid “premium” above and beyond the savings in fuel costs made possible by these cars over their conventional counterparts? We answer these questions using detailed data on new cars sold in eight European Union countries from January 2011 to September 2017, combined with gasoline, diesel and electricity prices and taxation information. Using hedonic pricing regressions and careful sample design, we find that, all else the same, more fuel-efficient variants of the same cars do cost more, but either the fuel economy is undercapitalized, or consumers and automakers are assuming a payback period of 2.2–4.6 years. We also find evidence of large all-electric and plug-in hybrid premiums above and beyond the savings in fuel costs, while regular hybrids cars carry a modest premium—one that is comparable to that of diesel cars or other types of technologies. Individuals who purchase all-electric or plug-in hybrids may be motivated by other considerations in addition to fuel costs, or automakers may be setting prices to take advantage of government incentives. We find no evidence that the prices of all-electric cars reflect their battery range.

From Import Substitution to Integration into Global Production Networks: The Case of the Indian Automobile Industry

This paper examines the growth trajectory and the current state of the Indian automobile industry, paying attention to factors that underpinned its transition from import substitution to integration into global production networks. Market-conforming policies implemented by the government of India over the past 2 decades, which marked a clear departure from protectionist policies in the past, have been instrumental in transforming the Indian automobile industry in line with ongoing structural changes in the world automobile industry. India has emerged as a significant producer of compact cars within global automobile production networks. Compact cars exported from India have become competitive in the international market because of the economies of scale of producing for a large domestic market and product adaptation to suit domestic market conditions. Interestingly, there are no significant differences in prices of compact cars sold in domestic and foreign markets. This suggests that the hypothesis of “import protection as export promotion” does not hold for Indian automobile exports.

İkinci El Araba Fiyatlarının Tahmini

In today's economic conditions, interest in second hand products has increased. Especially second-hand car or vehicles have a wide customer base. In the sector which has a workshop market, it is very important to make fast sales, to make the right pricing and to calculate the ideal prices of the cars in order to exchange at the right price. With linear regression analysis second-hand in such cases first determination of variables with effect on price, then it is possible to calculate the price by establising estimating model. In this study, the model was established by determining 23 of 78 variables affecting the price such as price, brands and model years of 5041 second-hand cars. The Determination Rate (R 2 ) of these 23 variables was found to be 89.1%. Then, by using this regression model, second hand prices of the cars were estimated via machine learning algorithm. The data set is divided into two as training and test data (70-30% and 80-20%). As a result of the study, it was determined the affinities between the real values and the estimated values. The proximity rate (±%) calculated in result of study shows affinity intensity of the estimation results to the true results. Via the prediction model established as a result of machine learning, the predictive accuracy rate was found to be 81.15% according to the 10% proximity of the correct results (upper limit; 110%, lower limit; 90%). According to the results, it is thought that machine learning technique could be second-hand to estimate second hand car prices. However, it is possible to reach a better estimation rate with a data set with more units and different variables.

The Fast and the Usurious: Putting the Brakes on Auto Lending Abuses

The car loan market is rife with consumer abuses: inflated pricing, discriminatory lending, and a variety of deceptions and scams. These abuses all stem from the dealer-centric nature of the auto finance market that ties the vehicle purchase to the vehicle financing. The overwhelming majority of consumers finance their purchases through the car dealer, but consumers cannot learn dealer financing terms in advance. They learn the offered financing terms only after spending substantial time and energy negotiating a car price, a trade-in price, warranties, insurance, and vehicle add-ons. At this point, because most consumers lack alternative financing options, they face a take-it-or-leave-it choice that leaves them especially vulnerable to dealer abuses. Not only does the lack of alternative financing options deprive consumers of the protection of competition in auto loans, but competition in the dealer-based lending market also actually works against consumers. Dealers auction off loans to financial institutions based largely on which institution allows the dealer the greatest compensation in the form of a markup on the loan. These ultimate lenders compete for the dealers’ business, not the consumers’, which results in consumers paying supracompetitive rates because of the dealer markup. The discretionary nature of the markups also enables discriminatory lending, with minorities often charged more for car loans, as well as a number of outright frauds and scams that cannot occur with third-party financing. This Article proposes to fix these auto lending abuses by requiring a three-business-day waiting period before delivery of the vehicle for con- sumers who do not have a bona fide third-party financing offer, as well as a prominently disclosed, penalty-free prepayment right for the loan during this period, and a system of mandatory data collection on auto loans to enable regulatory oversight. A penalty default waiting period would incentivize consumers to shop for financing separately from the vehicle purchase transaction, which will create positive competitive forces lowering dealers’ supracompetitive markups of financing, reduce opportunities for discriminatory lending, and protect consumers from other deceptive practices.

Decision Support System for Purchase of Used Cars using Electre Method

- This study aims to build a decision support system that can help facilitate the selection of the right used car for potential buyers. To be able to buy a used car that suits the needs and funds owned by consumers, buyers must consider the many criteria and factors of each used car which consists of various brands that exist today. This study uses the ELECTRE (Elimination and Choice Translation Reality) method. The criteria in the comparison used in this decision support system are documents, cylinder volume, year of production and car price. The ELECTRE method is used in conditions where alternatives that do not meet the criteria are eliminated, and suitable alternatives can be generated. Of the 8 samples of used cars studied, the best recommendation was Ayla (A4) with a total aggregate of 4 and an alternative that was not recommended was Agya (A3) with a total aggregate of 0.

Ordered quantile normalization: a semiparametric transformation built for the cross-validation era

ABSTRACT Normalization transformations have recently experienced a resurgence in popularity in the era of machine learning, particularly in data preprocessing. However, the classical methods that can be adapted to cross-validation are not always effective. We introduce Ordered Quantile (ORQ) normalization, a one-to-one transformation that is designed to consistently and effectively transform a vector of arbitrary distribution into a vector that follows a normal (Gaussian) distribution. In the absence of ties, ORQ normalization is guaranteed to produce normally distributed transformed data. Once trained, an ORQ transformation can be readily and effectively applied to new data. We compare the effectiveness of the ORQ technique with other popular normalization methods in a simulation study where the true data generating distributions are known. We find that ORQ normalization is the only method that works consistently and effectively, regardless of the underlying distribution. We also explore the use of repeated cross-validation to identify the best normalizing transformation when the true underlying distribution is unknown. We apply our technique and other normalization methods via the bestNormalize R package on a car pricing data set. We built bestNormalize to evaluate the normalization efficacy of many candidate transformations; the package is freely available via the Comprehensive R Archive Network.

Spatial differentiation and network externality in pricing mechanism of online car hailing platform

In order to eliminate the information asymmetry of supply and demand in transport service market, online car hailing platforms appear recent years. Almost all the transport service markets have the property of spatial differentiation, i.e, passengers' demand and vehicle supply are subject to different distributions in different regions at different time. Under the framework of two-sided market theory, this paper develops an intermediary pricing model for transport service market to evaluate the impact of spatial differentiation and network externality on online car hailing platform's pricing mechanism, based on the analysis of transport services' supply-demand matching. The analytic solution shows that the optimal price of the online car hailing platform is decided by the degree of heterogeneity in customer valuation on demand side and driver cost on supply side. Given optimal price on demand side, supply price in a region directly determines the supply of transport service in the same region, has cross effect on the supply in other region, and has indirect effect on demand price through the aggregate supply. Based on the trip record data of New York City, the simulation shows that online car hailing platform try to customize higher prices to attract more drivers and alleviate the passengers' demand pressure in morning rush hour. Furthermore, the simulation also shows that the disparity of the flow density in adjacent areas may affect the price adjustment mechanism of the online car hailing platform, and if the regional disparity further expands, the optimal choice of the platform is to increase the price of supply side, rather than the demand side.

Car Price Prediction using Machine Learning Techniques

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 87.38% was obtained.

Women’s gainful employment in ‘gher’ farming system (prawn–carp–rice integrated culture) in Bangladesh: trends and determinants

The paper examines trends in women’s gainful employment in ‘prawn-carp’ and ‘high yield variety (HYV) rice’ enterprises of the ‘gher’ farming system and jointly identifies the determinants of employing female and male labor in these enterprises using a farm-level panel data of 90 producers covering a 14-year period (2002–2015) from southwest Bangladesh by applying a multivariate Tobit approach. Results revealed that women’s gainful employment and real wage increased significantly with a substantial reduction in wage gap between female and male labor. The decision to employ female and male labor was found to be positively correlated thereby confirming jointness in decision-making and validity of our chosen approach. Prices and socio-economic factors exerted varied influences on female and male labor demand. Output prices, cultivated area, education, and experience positively increased women’s gainful employment whereas larger family size reduced it. Policy implications include investments in education targeted at the ‘gher’ farming households and land reform policies to consolidate farm size and facilitate smooth functioning of the land market to increase women’s gainful employment.

COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS AND REGRESSIONS FOR CAR PRICE PREDICTION

The purpose of the research described in this article is a comparative analysis of the predictive qualities of some models of machine learning and regression. The factors for models are the consumer characteristics of a used car: brand, transmission type, drive type, engine type, mileage, body type, year of manufacture, seller's region in Ukraine, condition of the car, information about accident, average price for analogue in Ukraine, engine volume, quantity of doors, availability of extra equipment, quantity of passenger’s seats, the first registration of a car, car was driven from abroad or not. Qualitative variables has been encoded as binary variables or by mean target encoding. The information about more than 200 thousand cars have been used for modeling. All models have been evaluated in the Python Software using Sklearn, Catboost, StatModels and Keras libraries. The following regression models and machine learning models were considered in the course of the study: linear regression; polynomial regression; decision tree; neural network; models based on "k-nearest neighbors", "random forest", "gradient boosting" algorithms; ensemble of models. The article presents the best in terms of quality (according to the criteria R2, MAE, MAD, MAPE) options from each class of models. It has been found that the best way to predict the price of a passenger car is through non-linear models. The results of the modeling show that the dependence between the price of a car and its characteristics is best described by the ensemble of models, which includes a neural network, models using "random forest" and "gradient boosting" algorithms. The ensemble of models showed an average relative approximation error of 11.2% and an average relative forecast error of 14.34%. All nonlinear models for car price have approximately the same predictive qualities (the difference between the MAPE within 2%) in this research.

A Hedonic Approach to Estimate the Price of Reliability, Energy Efficiency and Safety for New Cars in Switzerland

When purchasing a car in Switzerland, consumers can consult three quality labels. First, a reliability label provides the probability that a given car will have a breakdown in the next year. Second, an energy efficiency label, based on fuel consumption and carbon dioxide emissions, ranks cars from A to G. Third, a safety label, based on crash tests, ranks cars from five to one stars. The aim of this paper is to establish whether these labels have an impact on the price of new cars in Switzerland. Using a hedonic regression model, we show that the reliability label influences the price. More precisely, a decrease by one per thousand of the breakdown’s probability increases, on average, the car price by 639 Swiss francs. For the energy efficiency and the safety labels, the hedonic model attains its limits due to collinearity issues and the question cannot be answered in a satisfactory way.

Equilibrium Trade in Automobile Markets

We introduce a computationally tractable dynamic equilibrium model of the automobile market where new and used cars of multiple types (e.g. makes/models) are traded by heterogeneous consumers. Prices and quantities are determined endogenously to equate supply and demand for all car types and vintages, along with the ages at which cars are scrapped. The model allows for transactions costs, taxes, flexible specifications of car characteristics, consumer preferences, and heterogeneity. We apply the model to two examples: a revenue-neutral replacement of the new vehicle registration tax with a higher fuel tax and a hypothetical “merger to monopoly” in an oligopolistic new car market. We show substantial gains in consumer welfare from the tax policy change, as well as important effects on government revenues, automobile prices, driving, fuel consumption and CO 2 emissions, while the merger leads to substantial welfare losses.