• Journal of the Korean Geographical Society
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• v.48 no.2
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• pp.272-287
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• 2013

This paper describes the importance of selecting explanatory variables(e.g. land price determinants) in hedonic pricing models employed in predicting real estate price, and explores dynamics of the land price determinants over time. The City of Junju was chosen as the study area, and repeated measured price data of standard lots over 17 years were analyzed. We applied a three-level modeling approach to this data in consideration of its nested data structure and longitudinal characteristics. Main land price determinants we focused on are primarily based on items included in the standard comparison table of land price, which is an official hedonic pricing model used by Government to estimate land price for tax levy. Our result shows that the land price fluctuation over 17 years was not uniform over the whole study area with each neighborhood revealing different price trend, and as such warrants longitudinal model components. In addition, some of determinants previously recognized as important were proved insignificant. It was also found that significant determinants at a particular time point lost its power gradually over time and vice versa. It is expected that more accurate prediction of price would be possible when taken account for this dynamics of price determinants over time in applying hedonic pricing model method.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.1-16
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• 2006

This paper empirically analyzed consumer price search behavior using Web log data of a Korean web site for price comparison. Consumer click-stream data of the site was used to test the effects of price level, product category, third party certification, reputation of retailers on click behavior. According to the descriptive statistics, 67.4% of shopbot users clicked the offer which was the lowest price returned in a search. We found that third party certification and reputation of retailers were significant determinants of clicking the lowest priced offer from legit analysis. We also applied Tobit regression analysis to estimate the price premium of the two determinants, but only reputation of retailers was found to have price premium of 4.9%.

• Korea Real Estate Review
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• v.27 no.1
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• pp.45-57
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• 2017

In this study, the factors influencing logistics warehouse price were analyzed using Hedonic price model. All the actual transaction cases of the logistics centers in Gyeonggi province for 10 years from 2006 to 2015 were investigated. In this hedonic model, statistically significant variables includes building, economic, investment and time characteristics. The analysis permits a better insight of price determinants of warehouse price. First, the purchase price of large size logistics centers is relatively high. Second, the indirect investment shows higher price due to active investment tendency. Third, Foreign investors with various know-how on investment are leading the selling price.

• Culinary science and hospitality research
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• v.22 no.6
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• pp.151-159
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• 2016

With the targeted coffee consumers in Kwangju city, South Korea, this paper investigates a few determinants such as taste, aroma, mouth-feel, and satisfaction to influence coffee price, based upon self-evaluations by those who enjoy specialty coffee. Using both simple regression and standard multiple regression analyses, it turned out that tastes, smell, mouth-feel, and satisfaction of specialty coffee had effects on coffee price. This study implies that when coffee consumers decide coffee price, they consider multiple factors to influence their overall satisfaction in multiple aspects than a single facet like taste, aroma, and mouth-feel. Practical and theoretical discussion and implications are suggested for the following studies.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.93-104
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• 2024

China is playing more predominant role in the liquefied natural gas (LNG) market worldwide and LNG import price is subject to various factors both at home and abroad. Nevertheless, previous studies rarely heed a multiple of factors. A time-varying parameter factor augmented vector auto-regression (TVP-FAVAR) model is adopted to discover the determinants of China's LNG import price and their dynamic impacts from January 2012 to December 2021. According to the findings, market fundamentals have a greater impact on the import price of natural gas in China than overall economic demand, financial considerations, and world oil prices. The primary determinants include domestic gas consumption, consumer confidence and other demand-side information. Then, there are diverse and time-varying spillover effects of the four common determinants on the volatility of China's LNG import price at different intervals and time nodes. The price volatility is more sensitive and long-lasting to domestic natural gas pricing reform than other negative shocks such as the Sino-US trade war and the COVID-19 pandemic. The results in this study further proves the importance of domestic natural gas market liberalization. China ought to do more to support the further marketization of natural gas prices while working harder to guarantee natural gas supplies.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.211-220
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• 2021

The aim of the study is to implement a factor analysis of the determinants of pricing in a state-regulated competitive market using economic and mathematical modelling methods and to develop ways to improve the pricing environment of the market under study. The purpose of the work defines the main objectives: (i) to investigate the features of the competitive model of the Ukrainian flour market; (ii) to analyse the current price conjuncture of the flour market and the dynamics of the main determinants of pricing; (iii)to develop ways of improving the price situation on the flour market on the basis of the factor analysis on the results of economic and mathematical modelling. In order to ensure the reliability and validity of the research results, the following methods were applied: the logical-dialectical method of scientific knowledge in the study of the main theoretical aspects of flour market functioning, the method of logical generalisation and synthesis, comparison, factor analysis, correlation and regression analysis, the graphical method, etc. It has been shown that pricing in a state-regulated competitive market has its own characteristics. For example, in the flour market the price of goods cannot be influenced by producers (sellers) by any methods, therefore determinants of pricing by indirect influence have been taken into account. The five-factor power model of wheat flour price has been constructed. It was substantiated that the price of wheat flour in Ukraine is mostly influenced by consumer price index (0.92 %). The received complex model of wheat flour price may be used also for medium-term forecasting and working out the ways of price formation optimization in the flour market.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.488-493
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• 2004

This study discusses what determinants are important to enter the worldwide railroad market. Survey analysis is used to decide the determinants of export in the international railroad markets. The Study finds that with price factors, non-price factors such as technological innovation and technological transference are important, too. For manufacturing, market experience and know - how, financing and supports in the government level in the areas of strategical alliance and regulation are essential. For non-tariff factors, technical risk and characteristics of markets are considered.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• Management & Information Systems Review
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• v.28 no.3
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• pp.25-44
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• 2009

The purpose of this paper is to examine the determinants which cause a price differential between common and preferred stock. Prior studies have shown that variables like liquidity, size, the number of outstanding shares issued can explain the price differential between common and preferred stock price. Based on year 2006 through year 2008 data, we analyzed the determinants using regression model. Dummy variables representing large/small company and KSE/KOSDAQ respectively are added and analyzed as independent variables. The firm size, trade volume turnover, and the number of preferred shares to total outstanding shares were proved to make influence on the price differential under the 5% significance level. Especially, we have found the number of preferred shares to total outstanding shares provide the most strong relationship with the price differential. This means that a high ratio of preferred stock to total outstanding shares leads to relatively high value of common stock and causes a big price differential.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.347-357
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• 2022

The aim of the study is to model demand for full-time Bachelor's Degree Programs in Hospitality and Catering, taking into account the influence of the main determinants in the COVID-19 pandemic. The research used methods of algorithms, correlation and regression analysis, ANOVA, graphical method, deduction and induction, abstraction, etc. It was found that the demand for full-time Bachelor's Degree Programs in Hospitality and Catering is price elastic. It has been argued that it is useful to consider both price and non-price determinants when modelling demand for full-time Bachelor's Degree Programs in Hospitality and Catering. It is proved that the main determinants of demand for full-time Bachelor's Degree Programs in Hospitality and Catering are full-time tuition fee, maximum government order, license volume and Consolidated Ranking of a higher education institution (HEI). In this case, the applicant decides to enrol in a full-time Bachelor's Degree Program in Hospitality and Catering, guided by the optimal ratio of tuition fee and the prestige of the HEI.