• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.102-110
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• 2018

The study analyzed the effects of the floor plan characteristics on the apartment price under the national housing size in 3 Gangnam districts for decades, the primary apartment markets in Korea. The analysis showed that the storage spaces such as kitchen, warehouses and dressage rooms have a positive effect on the price. Especially, the highly opened space with three-side open plan and the one with the unified type of livingroom, diningroom and kitchen have shown the strong effect on the price. For the kitchen spaces, the I-shaped kitchen tends to be more expensive while a centered living room has a positive effect on the price. These findings have an academic significance as the direct effects of plan characteristics on price has been examined unlike prior research focused on the analysis of trend, basic statistics, and satisfaction level. It is noteworthy that these research finding has identified the productive implication for the future floor plan design and pricing and also be implemented in the purchasing decision making by buyers in the housing market.

• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.47-69
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• 1990

Using an aggregator model, we look into the possibilities for substitution between Korea's exports, imports, domestic sales and domestic inputs (particularly labor), and substitution between disaggregated export and import components. Our approach heavily draws on an economy-wide GNP function that is similar to Samuelson's, modeling trade functions as derived from an integrated production system. Under the condition of homotheticity and weak separability, the GNP function would facilitate consistent aggregation that retains certain properties of the production structure. It would also be useful for a two-stage optimization process that enables us to obtain not only the net output price elasticities of the first-level aggregator functions, but also those of the second-level individual components of exports and imports. For the implementation of the model, we apply the Symmetric Generalized McFadden (SGM) function developed by Diewert and Wales to both stages of estimation. The first stage of the estimation procedure is to estimate the unit quantity equations of the second-level exports and imports that comprise four components each. The parameter estimates obtained in the first stage are utilized in the derivation of instrumental variables for the aggregate export and import prices being employed in the upper model. In the second stage, the net output supply equations derived from the GNP function are used in the estimation of the price elasticities of the first-level variables: exports, imports, domestic sales and labor. With these estimates in hand, we can come up with various elasticities of both the net output supply functions and the individual components of exports and imports. At the aggregate level (first-level), exports appear to be substitutable with domestic sales, while labor is complementary with imports. An increase in the price of exports would reduce the amount of the domestic sales supply, and a decrease in the wage rate would boost the demand for imports. On the other hand, labor and imports are complementary with exports and domestic sales in the input-output structure. At the disaggregate level (second-level), the price elasticities of the export and import components obtained indicate that both substitution and complement possibilities exist between them. Although these elasticities are interesting in their own right, they would be more usefully applied as inputs to the computational general equilibrium model.

• Information Systems Review
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• v.4 no.2
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• pp.293-307
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• 2002

In this study, industry-specific application systems operation cost estmation models are suggested. We reviewed operation cost models of previous researches, and developed a strong need for industry-specific operation outsourcing cost models. Security industry operation cost model and medical care industry outsourcing cost model are proposed, and tested with empirical data. We showed the validity of industry-specific application systems outsourcing cost models. Future research will be needed to develop outsourcing cost models for other industries and to refine cost models developed in this study.

• Convergence Security Journal
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• v.8 no.3
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• pp.83-92
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• 2008

In respect complication of group and period, the sales of retail trade is composed of various factors. This paper studies focus on estimating the determinants of the sales of retail trade. The volume of analysis consist of 7 groups. Analyzing period be formed over a 36 point(2005. 1$\sim$2007. 12). In this paper dependent variable setting up sales of retail trade, explanatory(independent) variables composed of composite stock price index, the number of the consumer's online buying behavior company, the coincident composite index, the index of trading price of APT, employment rate, an average of the rate of operation(the manufacturing industry), the consumer price index. The result of estimating the determinants of sales of retail trade provides empirical evidences of significance positive relationships between the coincident composite index, the index of trading price of APT, employment rate, an average of the rate of operation(the manufacturing industry). However this study provides empirical evidences of significance negative relationships between the consumer price index. The explanatory variables, that is, composite stock price and the number of the consumer's online buying behavior company, are non-significance variables. Implication of these findings are discussed for content research and practices.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.271-280
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• 1999

인공신경망을 시계열예측에 적용하는 경우에 고려되어야 할 문제중, 특히 모형에 적합한 입력변수의 생성이 중요시되고 있는데, 이러한 분야는 인공신경망의 모형생성과정에서 입력변수에 대한 전처리기법으로써 다양하게 제시되어 왔다. 가장 최근의 입력변수 전처리기법으로써 제시되고 있는 신호처리기법은 전통적 주기분할처리방법인 푸리에변환기법(Fourier transforms)을 비롯하여 이를 확장시킨 개념인 웨이블릿변환기법(wavelet transforms) 등으로 대별될 수 있다. 이는 기본적으로 시계열이 다수의 주기(cycle)들로 구성된 상이한 시계열들의 집합이라는 가정에서 출발하고 있다. 전통적으로 이러한 시계열은 전기 또는 전자공학에서 주파수영역분할, 즉 고주파 및 저주파수를 분할하기 위한 기법에 적용되어 왔다. 그러나, 최근에는 이러한 연구가 다양한 분야에 활발하게 응용되기 시작하였으며, 그 중의 대표적인 예가 바로 경영분야의 재무시계열에 대한 분석이다 전통적으로 재무시계열은 장, 단기의사결정을 가진 시장참여자들간의 거래특성이 시계열에 각기 달리 가격으로 반영되기 때문에 이러한 상이한 집단들의 고유한 거래움직임으로 말미암아 예를 들어, 주식시장이 프랙탈구조를 가지고 있다고 보기도 한다. 이처럼 재무시계열은 다양한 사회현상의 집합체라고 볼 수 있으며, 그만큼 예측모형을 구축하는데 어려움이 따른다. 본 연구는 이러한 시계열의 주기적 특성에 기반을 둔 신호처리분석으로서 기존의 시계열로부터 노이즈를 줄여 주면서 보다 의미 있는 정보로 변환시켜 줄 수 있는 웨이블릿분석 방법론을 새로운 필터링기법으로 사용하여 현재 많은 연구가 진행되고 있는 인공신경망과의 모형결합을 통해 기존연구와는 다른 새로운 통합예측방법론을 제시하고자 한다. 본 연구에서 제시하는 통합방법론은 크게 2단계 과정을 거쳐 예측모형으로 완성이 된다. 즉, 1차 모형단계에서 원시 재무시계열은 먼저 웨이블릿분석을 통해서 노이즈가 필터링 되는 동시에, 과거 재무시계열의 프랙탈 구조, 즉 비선형적인 움직임을 보다 잘 반영시켜 주는 다차원 주기요소를 가지는 시계열로 분해, 생성되며, 이렇게 주기에 따라 장단기로 분할된 시계열들은 2차 모형단계에서 신경망의 새로운 입력변수로서 사용되어 최종적인 인공 신경망모델을 구축하는 데 반영된다.

• Journal of Environmental Policy
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• v.14 no.4
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• pp.23-44
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• 2015

The policy aimed at introducing a Bonus-Malus system to reduce GHG and raise the market share of small cars is scheduled to go into effect in South Korea in 2020. Although the policy was originally planned to be enforced from 2015, the Ministry of Trade, Industry and Energy argued that the system brings low reduction effect and relative disadvantage to domestic small cars and brought arguments in 2014. As a result, the enforcement was pushed back. Related studies are mainly focused on offering statistical estimation of the policy's effect to support the arguments, and few theoretical studies were published given that there was not enough time until 2015 back then. The author approached the issue with mathematical modeling in order to give theoretical basis for sophisticated empirical studies. If car suppliers have market power and strategically set their prices, the impact of Bouns-Malus on car prices would be lower than what was originally intended. In case only a part of the car market loses its market power, the effect of the policy would be improved. Assume that the Bonus-Malus is currently at an optimal level and the car market structure is undergoing changes, then the direction of the new optimal level would depend on the elasticity of demand of each market and substitute elasticity. For example, if the car market becomes more monopolistic while the demand for big cars is elastic, demand for small cars is inelastic and substitution elasticity is low, then the new optimal level of Bonus-Malus should be higher.

• Environmental and Resource Economics Review
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• v.17 no.1
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• pp.165-196
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• 2008

This paper reports a quantitative meta analysis of the economic impacts of localized noxious and contaminated sites. Using either hedonic property value or stated preference methods, economists have studied the effects of contamination or noxious activities, or the benefits realized from their elimination, on real estate prices at more than 40 sites. In support of wise public and private investments in environmental quality, most of these studies aim to inform decision makers about the benefits of remediation and cleanup. Their results vary considerably, but there has been no previous systematic effort to analyze the differences and identify shared insights. This study uses established methods of meta analysis to identify points of agreement and differences in this body of literature. The studies are characterized by the type of site, modeling approach, geographic extent of impacts, data features, and other key factors that underlie their value estimates. The impact estimates are normalized as proportional effects on property values. This study attempts to discover whether the estimated economic impacts of contamination or noxious activity differ according to these characteristics of the studies, and whether anything general can be said about the economic consequences of site contamination and remediation. Bivariate, multivariate, and logit techniques are applied to the data. The results suggest that the property value is the most sensitive to water base contamination, published case studies result in systematically greater environmental value than those in unpublished reports, and real estate markets show responses to environmental condition changes.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.43-55
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• 2016

This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2001.11a
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• pp.169-186
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• 2001

The researches about the general flow of new product development process was achieved in various field. But there was little discussion about the methodologies and tools used in that process. So we suggest new DEA model as the methodology that determines sustainable price and quality attributes and this can substitute econometric hedonic methodology. To make smooth surface composed of quality attributes and price, we use QC-DEA model. Additionally we make AR-QC DEA model by introducing AR to reflect consumer perceptions on quality attributes. AR-QC DEA overcomes the limits of parametric methodology and represents product-specific shadow prices, so it is possible to supply the information about quality attributes and price combination in new product development process and to simulate easily whether new product can exist in the market. Finally by empirical research on notebook computer we can show that AR-QC DEA has the ability to explain market change.

• Journal of Service Research and Studies
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• v.6 no.4
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• pp.75-85
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• 2016

Coal can be divided into thermal coal and coking coal. The price of thermal coal is basically affected by demand and supply. However, many other factors with regard to economic condition such as exchange rate, economy growth rate also make an influence on the price. This study is targeted to develop a forecast model for thermal coal price by using System Dynamics Method. System dynamics provides results that better reflect the real world by employing an inter-dependent system of variables. This study found out that 8 factors have important influence on the thermal coal price. Most of the data of the variables were acquired from the Bloomberg Database. The period extends to 2 years and 4 months, from May of 2011 to August of 2013. The causal relations among the variables were acquired by regression analysis