Determinants Of Prices Research Articles

ADAM SMITH’S THEORY OF VALUE AND THE FALLING RATE OF PROFIT: UNCOMMON CONCEPTIONS AND COMMON MISCONCEPTIONS

Smith’s theory of value and distribution, which emphasizes labor time as the determinant of prices, has been widely misunderstood. Ricardo misinterpreted it as relevant only to primitive societies, while Marx inaccurately aligned it with his own labor theory of value. In reality, Smith’s perspective oscillates between a labor-based and a labor-commanded approach to relative prices, intended for modern economies. Neoclassical economists further distorted Smith’s views by incorporating utility theory. Moreover, while Smith is often linked to the theory of a falling rate of profit due to competition, he actually attributed it to rising capital intensity. Contrary to the belief that Smith was a staunch advocate of free markets, he supported reasonable government intervention.

Urban air pollution and systemic risk of the real estate market in China

Air pollution is a major environmental risk to the economy nowadays. Given that urban air pollution is one of the determinants of urban housing price, and that both urban air pollution and housing price have significant spatial spillover effects, this paper explores the impact of inter-city air pollution spillover on systemic risk of the real estate market, using housing price data of 79 cities in 8 major urban agglomerations in China. This paper innovatively introduces the principal component analysis into the VAR model and appropriately conducts historical decomposition to construct a new systemic risk measurement index of the real estate market, which can identify both direction and magnitude of the impact. The results show that, on the time dimension, air pollution is found to contribute most time when systemic risk of the real estate market reaches a high level. On the spatial dimension, this paper identifies the urban agglomerations and node cities whose systemic risks of the corresponding real estate markets are more susceptible to air pollution spillover. On the spillover channels, this paper finds that the past cultivation of environmental awareness and the implementation of environmental protection policies in China have alleviated the negative impact of air pollution spillover on the systemic risk of the real estate market.

Answering new urban questions: Using eXplainable AI-driven analysis to identify determinants of Airbnb price in Dublin

This paper presents a novel approach for leveraging an eXplainable artificial intelligence approach to examine the price determinants of Airbnb in Dublin, Ireland, mainly focusing on urban characteristics. We employ an interpretable machine learning (ML) approach on publicly accessible Airbnb data to identify optimal Airbnb rental features, predict price trends, and understand Airbnb’s growth in Dublin. Our analysis also integrates features from three main categories: physical geography (e.g., elevation), social geography (e.g., Electoral Division), and infrastructure & economic geography based on housing/rental market theories (e.g., distance from the city centre). This integration of geographical attributes with ML algorithms provides localised insights into Airbnb pricing dynamics, addressing gaps in previous research that often overlooked the impact of geographical features on price prediction. By applying the TreeSHAP utility (tree-based attribute for SHapley Additive exPlanations), we establish the significant role of these geographical features in predicting Airbnb rental prices, thereby reinforcing theories like Alonso’s Bid-Rent Theory. We further validate the robustness of our models through testing procedures. Our study offers considerable value to urban planners, policymakers, urban researchers, and market stakeholders, deepening the understanding of Airbnb’s impact on urban landscapes and contributing to the broader discourse on the Sharing Economy.

Analysis of the market value of Premier League attacker

The main purpose of this study is to use the method of multiple linear regression to conduct a comprehensive discussion on "Factors affecting the market price of Premier League striker players". In the era of increasingly hot soccer, the transfer of stars is a big attraction in the transfer period every year, but there are still many clubs signing overpaid and underpaid players. The overall objective of this study is to find the determinants of players' price, so as to provide a reference for clubs to improve the utilization of funds in the transfer period. In this study, a dataset of player data for the 17-18 Premier League season was first downloaded via Kaggle. Then, the dataset obtained from Kaggle was used for empirical analysis to identify correlations that significantly affect the market price of players, and multiple linear regression analysis was performed after processing these data. Through the calculations, it was determined that match performance and goals scored had a significant positive impact on market value, and age and match possession had a non-significant negative impact on market value, which suggests that there is a need for the relevant team managers to optimize these aspects in order to promote a virtuous cycle of club development and team performance.

Analysis of the market value of Premier League attacker

The main purpose of this study is to use the method of multiple linear regression to conduct a comprehensive discussion on "Factors affecting the market price of Premier League striker players". In the era of increasingly hot soccer, the transfer of stars is a big attraction in the transfer period every year, but there are still many clubs signing overpaid and underpaid players. The overall objective of this study is to find the determinants of players' price, so as to provide a reference for clubs to improve the utilization of funds in the transfer period. In this study, a dataset of player data for the 17-18 Premier League season was first downloaded via Kaggle. Then, the dataset obtained from Kaggle was used for empirical analysis to identify correlations that significantly affect the market price of players, and multiple linear regression analysis was performed after processing these data. Through the calculations, it was determined that match performance and goals scored had a significant positive impact on market value, and age and match possession had a non-significant negative impact on market value, which suggests that there is a need for the relevant team managers to optimize these aspects in order to promote a virtuous cycle of club development and team performance.

Valuation of guaranteed lifelong withdrawal benefit with the long-term care option

In this paper, under the stochastic interest rate framework, we consider the valuation of a Guaranteed Lifelong Withdrawal Benefit (GLWB) annuity product by explicitly incorporating the health state of the policyholder through the long-term care (LTC) option. The product provides policyholders with protection against longevity risk and market downturns, as well as financial support when facing LTC needs. Within the context of dynamic withdrawals, the valuation of the GLWB annuity with the LTC option is characterized as a stochastic optimal control problem. We introduce a novel bang-bang analysis approach without the usual convexity assumption in literature and prove that the optimal withdrawal strategies for the policyholder are constrained to a finite set. Furthermore, we perform a sensitivity analysis on the price determinants of GLWB annuities with and without the LTC option, and provide economic interpretations. Lastly, we investigate the impact of gender on the optimal withdrawal strategy and the fair fee of the annuity with the LTC option.

Determinants of Virtual Property Prices in Decentraland an Empirical Analysis of Market Dynamics and Cryptocurrency Influence

Determinants of Virtual Property Prices in Decentraland an Empirical Analysis of Market Dynamics and Cryptocurrency Influence

Emission trading in a high dimensional context: to what extent are carbon markets integrated with the broader system?

The EU ETS represents the cornerstone of the EU climate policy framework. While most of the studies focus on the determinants of carbon price, this work will provide further insights into the influence of European Emission Allowance (EUA) prices on carbon dioxide trends and variables of the economic-financial-climate-environmental system considering a large set of time series. Results highlighted how CO2 appears to be more influenced by commodity prices, climate variables, and past industrial performances. Furthermore, a shock in carbon prices could potentially exert significant turbulence on the carbon dioxide series, fading in intensity as time goes by. Overall, there appears to be a net positive effect on the influence of carbon prices on the system. However, robustness checks identified how the impact of carbon price on CO2 and other variables of the model is still weak. This work sheds light on the EU ETS's influence on a set of multidimensional variables. Still, overlapping national policies appear to interfere with the EU ETS effectiveness in the EU.

Hedonic Pricing Models in Rural Tourism: Analyzing Factors Influencing Accommodation Pricing in Romania Using Geographically Weighted Regression

This study investigates the factors influencing pricing in Romanian rural tourism using a hedonic pricing model through a hybrid LASSO-OLS regression and geographically weighted regression (GWR). By analyzing data from 5028 unique accommodation units across 1170 local administrative units, we identify some key pricing determinants, including accommodation size, capacity, facilities, and environmental attributes. The results reveal that larger accommodations and those with higher guest capacities command higher prices. Luxurious facilities, such as massage services, pools, and fireplaces, significantly increase pricing, although the impact of such features varies by region, as do accommodation type and natural scenery, with agritouristic boarding houses and proximity to natural attractions like water bodies and forests being more valued in certain regions. These factors can aid rural entrepreneurs in optimizing pricing to enhance competitiveness and profitability.

Assessment of market price determinants of live goats in three district municipalities of the Eastern Cape Province, South Africa

Assessment of market price determinants of live goats in three district municipalities of the Eastern Cape Province, South Africa

What drives the price behavior of US sustainable stocks?

PurposeThis paper aims to identify the key drivers of US sustainable stock price movements in both the short and long term, deploying a rich collection of variables corresponding to green finance, investor attention and sentiment, market fear and uncertainty, macroeconomic variables, common market risk factors, commodity markets and the carbon emission market.Design/methodology/approachThe empirical analysis is based on two main methodologies. First, the elastic net penalized regression is utilized to select the factors most influential on the price formation of sustainable stocks. Second, short- and long-run dynamics of the chosen factors are examined using the dynamic simulations of the autoregressive distributed lag (DYNARDL) model.FindingsOf 32 candidate variables, the elastic net chooses US renewable energy, European sustainable stock market, EU ETS emission allowances, public attention to sustainable finance, gold and European renewable energy as the most contributing factors to the price behavior of sustainable stocks. The DYNARDL estimation results reveal that US renewable energy, European sustainable stock market and EU ETS emission allowances are important determinants in the short and long term, while public attention (European renewable energy) tends to affect sustainable stock prices only in the short (long) run.Practical implicationsThe corresponding short- and long-run effects of US renewable energy, EU ETS emission allowances and European sustainable stocks on US sustainable stock prices should induce policymakers to keep the price behavior of these factors under systematic review. The formulation of policy measures could serve to safeguard the sustainable stock market from the price vagaries in these influential markets.Originality/valueRelevant literature often focuses on the reaction of sustainable stocks to mainstream assets and risk proxies, limiting analysis to a few factors and providing an incomplete understanding of the drivers behind sustainable stock prices. More comprehensive research is needed due to the lack of studies on the determinants of sustainable stock prices and the growing global demand for these investments. This paper aims to address this gap by examining the potential explanatory power of 32 candidate factors representing key players in the global economic and financial landscape.

Housing price determinants in Ecuador: a spatial hedonic analysis

Purpose This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities. Design/methodology/approach This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal. Findings The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation. Research limitations/implications The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation. Practical implications This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study. Originality/value This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

Drivers of public procurement prices: Evidence from pharmaceutical markets

This paper examines the determinants of public procurement prices using comprehensive data on pharmaceutical purchases by the public sector in Chile. We first document sizable price differences between buyers for the same product and quantity purchased: the difference between the average prices paid by buyers at the 90th and 10th percentiles of the distribution is 16 percent. Our main results are related to the importance of market structure in explaining the dispersion in procurement prices. We find that market structure explains three times more dispersion than buyer effects. Moreover, we leverage exogenous variation in market structure due to patent expirations to estimate that the entry of an additional seller decreases average procurement prices by 11.7 percent, which is 72 percent of the price differences implied by the gap between the 90th and 10th percentiles of estimated buyer effects. These results suggest that supply-side factors are relevant determinants of public procurement prices and that their quantitative importance may exceed that of demand-side factors previously emphasized in the literature.

The determinants of transfer pricing: Evidence from Tunisian listed firms

The aim of this paper is to detect the determinants of transfer pricing in the Tunisian economic context. This study uses secondary data from financial statments of listed firms on the Tunis Stock Exchange (BVMT), as well as the notes published in the official bulletins and annual reports provided by the Tunisian Financial Market Council (CMF).The study period covered 10 years, from 2010 to 2019 with 310 observations. We use random effect model to verify financial and economic hypotheses in the Tunisian context. Robustness tests are also used to investigate the transfer pricing determinants’. We find that firm size, sector of activity, reinvestment of profits and tax loss’ imputation have a significant positive impact on the practice of transfer pricing. The originality of this study lies in the importance of the transfer pricing at the level of local companies with the presence of several tax rates in the same tax system, a range of tax incentives granted by the government and the imputation of perpetual loss over several years.These findings may serve the Tunisian government in its fight against transfer pricing.

지방 하위시장의 아파트 가격 결정요인 분석

This study aims to identify the characteristics of apartment price determinants between two local submarkets, and to ascertain the price differential between branded and non-branded apartments and corporate location. Multi-Regression analysis was conducted on the total number of apartments transacted in Geoje City over the past four years, using the actual transaction price of apartments as the dependent variable and structure, complex, brand, and corporate location characteristics as independent variables. The results indicate that in Suyang-dong(high prices), the variables of floor, floor area ratio, and brand have a positive effect, while the variables of rooms and corporate distance have a negative effect. In Okpo-dong(low prices), the number of car parking spaces and brand variables have a positive effect. Branded apartments are priced higher than non-branded apartments. Additionally, the value ratio of branded apartments is higher in the lower-priced area. Price discrepancies may exist within the same area, Consequently, branded apartments that cater to local characteristics can command high prices, even in price sub-areas.

Productivity Shocks of Dominant Companies and Local Housing Markets

We extend the literature on the influence of firm-level characteristics on housing markets by exploring the association between the labor productivity shocks of dominant firms and local housing prices. Using a sample of all U.S. firms from COMPUSTAT during 1980-2017, we find that the aggregate shocks of labor productivity of dominant firms at the metropolitan statistical area (MSA)-level explain for a significant portion of the local housing price changes in MSAs while controlling for other housing price determinants. About a year or more is required for the shocks to propagate through the local housing markets, which make them a viable predictor of future housing price. The productivity shock – housing price relation is stronger in areas that have more concentrated high-tech dominant firms or where dominant firms have closer links to their local non-dominant industry peers. Shocks are also more influential during economic expansion than economic contraction. Furthermore, the relation also exists at the zipcode level but the shocks propagate faster than at the MSA-level. The findings provide helpful insights for real estate practitioners and policymakers, especially in areas with a higher concentration of large companies.

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

Non-Market Price Determinants of Fossil Energy Raw Materials

Non-Market Price Determinants of Fossil Energy Raw Materials