• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.121-125
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• 2009

The Public Noticed Land Price system was developed in order to establish a practical and consistent land price information system, where land prices are appraised and publicly noticed according to the Law Governing the Public Notice of Real Estate Prices and Appraisals.The appraisal process for evaluating and appraising the public noticed land price in conducted in phases, where the nation's land is divided into human and spatial portions according to administrative districts. Depending on the subjective judgement of the appraiser, it is therefore possible for discrepancies to occur in land prices for lots near the boundaries of administrative districts. There is the computerized support system to maintain the public noticed land prices balance between the boundaries of cities, counties, and districts (the units in which evaluation and appraisal are conducted to determine public noticed land prices). But, due to that system is divided into attribute and spatial data information, it is possible for discrepancies to occur in land prices for lots near the boundaries of administrative districts. The purpose of this study to suggest the reasonable methods on discrepancies in public noticed land prices through spatial analysis using GIS.

• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.53-61
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• 2012

This study makes an attempt to appraise how much successful both (Ham-Pyung) Butterfly Festival and (Mu-Ju) Firefly Festival are in the view point of ecology-friendly effects, and based on this appraisal, searches for desirable and developmental directions of other eco-festivals. The empirical analysis of the study shows that even if the Butterfly Festival turns out to be more famous than the Firefly Festival, the latter is confirmed to be more eco-friendly than the former. The fact that the latter is more eco-friendly seems to be due to the fact that the subject matter, that is the theme, of the festival is the fireflies which are not only natural monuments but also peculiar in-site-resources specific to Mu-Ju. This fact suggests that the other eco-festivals, newly emerging ones in particular, need to find and designate in-site-resources inherent in their regions as the theme, as far as possible.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.207-224
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• 2015

The purpose of this study is to review the various valuation techniques of intangible assets. The value of intangible asset by the income approach can be measured as the present value of the economic benefit over the intangible asset's remaining useful life. The typical methods used in intangible asset economic income projections include extrapolation method, life cycle analyses, sensitivity analyses, simulation analyses, judgment method, and tabula rasa method. There are several methods available for estimating capitalization rates and discount rates for intangible asset, in which we have discussed market extraction method, capital asset pricing model, built-up method, discounted cash flow model, and weighted average cost of capital method. As the capitalization methods for intangible asset, relief-from-royalty method, excess earnings capitalization method, profit split method, residual from business enterprise method, postulated loss of income method and so on have been reviewed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3747-3756
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• 2012

This paper examines the effect of price ceiling regulation on the new apartment price. The analysis procedure of the study is divided into two parts, which stand for a case study on the effect of price control on the new apartment price and the survey of real estate experts on price ceiling regulation. The empirical results of our case study show that the selling price under price ceiling regulation is generally lower than that in the situation of price deregulation, in terms of the land development expense and construction cost. With regard to the survey results, more than half of respondents have opinions that price ceiling regulation has an impact on the new apartment price and lowers the price. They are equally divided pro and con regarding the problem of keeping or discarding the regulation.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.31-41
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• 2015

The telecommunication industry has been considered as a national fundamental infrastructure. However, due to the rapid evolution of technology and the change of industry market conditions, the telecommunication infrastructure needs no more huge space for facility and it leads its use to the mixed-use development based on private investment. This study intends to examine the financial feasibility of the development project for the optimal alternative use of telecommunication facility purpose site as a case study based on two types: contributed acceptance and multi-level designation. The NPA and IRR has been analyzed by the simulation of stochastic variables including rent price and its variation rate, vacancy rate, construction cost, capitalization rate and discount rate. The research finding indicates that the two types of development are satisfied with the financial feasibility and it is noteworthy that the rent price turns out to be the most critical factor for the project. Accordingly, it is expected that these research finding can be applied for providing the solid cases of financial feasibility analysis for the development project in limited use of telecommunication facility purpose site.

• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.74-81
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• 2015

As one of urban infrastructure system, the electricity substation is critical for urban life and industrial activity as the electricity demands get higher than ever. However the substation is generally regarded as unpleasant or dangerous facility, which finally results in the continuous opposition movement by resident due to the belief of unidentified negative effect in apartment prices. Accordingly, as the scientifically objective and quantitative analysis is required to solve the social conflict, this study intends to examine the variation affected by urban infrastructure system, expecially for substation. After the independent variable defining the price of apartment and the dependent variable, which is apartment price, are identified and their spatial data has been filed, the forecasting model has been developed through the hedonic price function as well as artificial neural networks system. The research finding indicated that the spatial range affected by substation is not notable and the range of some case was applicable for less than 600m. It is expected that these research findings can be applied for establishing the one of solid cases for the analysis of economical effect to local housing market by the urban infrastructure system.

• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.499-507
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• 2023

This study conducted an empirical analysis on a land purchase requisition policy in Bukhansan National Park to draw the efficacy, limitations and implications of this policy. A logistic regression analysis was conducted to identify factors that affected the landowners' decision on applying for land purchase requisition using the government's records on acquisition of private lands in the park since 2006 when this policy began to be implemented. Results illustrate that the probability that a landowner applied for purchase requisition increased if the land was classified as forest, if a large proportion of the land was designated as the nature conservation district, if it was located farther from park boundary, and if it had higher appraised value per square meter. These results indicate that as the landowners had less chance to utilize their lands, they more likely apply for purchase requisition. These results also imply that the government can achieve a high conservation performance level if private lands are acquire by the land acquisition requisition policy. The logistic regression model also predict that 401m² of the private lands in Bukhansan National Park will likely be purchase-requested in future. Despites its usefulness in mitigating landowners' complaints in national parks, the land purchase requisition policy has not been widely utilized. Based on these empirical results, this study provides policy implications to facilitate the ulitization of this policy.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• The Korean Journal of Financial Studies
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• v.3 no.1
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• pp.233-265
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• 1996

We examine the impact of public and private information on price movements using the thirty DJIA stocks and twenty-one NASDAQ stocks. We find that the standard deviation of daily returns on information days (dividend announcement, earnings announcement, insider purchase, or insider sale) is much higher than on no-information days. Both public information matters at the NYSE, probably due to masked identification of insiders. Earnings announcement has the greatest impact for both DJIA and NASDAQ stocks, and there is some evidence of positive impact of insider asle on return volatility of NASDAQ stocks. There has been considerable debate, e.g., French and Roll (1986), over whether market volatility is due to public information or private information-the latter gathered through costly search and only revealed through trading. Public information is composed of (1) marketwide public information such as regularly scheduled federal economic announcements (e.g., employment, GNP, leading indicators) and (2) company-specific public information such as dividend and earnings announcements. Policy makers and corporate insiders have a better access to marketwide private information (e.g., a new monetary policy decision made in the Federal Reserve Board meeting) and company-specific private information, respectively, compated to the general public. Ederington and Lee (1993) show that marketwide public information accounts for most of the observed volatility patterns in interest rate and foreign exchange futures markets. Company-specific public information is explored by Patell and Wolfson (1984) and Jennings and Starks (1985). They show that dividend and earnings announcements induce higher than normal volatility in equity prices. Kyle (1985), Admati and Pfleiderer (1988), Barclay, Litzenberger and Warner (1990), Foster and Viswanathan (1990), Back (1992), and Barclay and Warner (1993) show that the private information help by informed traders and revealed through trading influences market volatility. Cornell and Sirri (1992)' and Meulbroek (1992) investigate the actual insider trading activities in a tender offer case and the prosecuted illegal trading cased, respectively. This paper examines the aggregate and individual impact of marketwide information, company-specific public information, and company-specific private information on equity prices. Specifically, we use the thirty common stocks in the Dow Jones Industrial Average (DJIA) and twenty one National Association of Securities Dealers Automated Quotations (NASDAQ) common stocks to examine how their prices react to information. Marketwide information (public and private) is estimated by the movement in the Standard and Poors (S & P) 500 Index price for the DJIA stocks and the movement in the NASDAQ Composite Index price for the NASDAQ stocks. Divedend and earnings announcements are used as a subset of company-specific public information. The trading activity of corporate insiders (major corporate officers, members of the board of directors, and owners of at least 10 percent of any equity class) with an access to private information can be cannot legally trade on private information. Therefore, most insider transactions are not necessarily based on private information. Nevertheless, we hypothesize that market participants observe how insiders trade in order to infer any information that they cannot possess because insiders tend to buy (sell) when they have good (bad) information about their company. For example, Damodaran and Liu (1993) show that insiders of real estate investment trusts buy (sell) after they receive favorable (unfavorable) appraisal news before the information in these appraisals is released to the public. Price discovery in a competitive multiple-dealership market (NASDAQ) would be different from that in a monopolistic specialist system (NYSE). Consequently, we hypothesize that NASDAQ stocks are affected more by private information (or more precisely, insider trading) than the DJIA stocks. In the next section, we describe our choices of the fifty-one stocks and the public and private information set. We also discuss institutional differences between the NYSE and the NASDAQ market. In Section II, we examine the implications of public and private information for the volatility of daily returns of each stock. In Section III, we turn to the question of the relative importance of individual elements of our information set. Further analysis of the five DJIA stocks and the four NASDAQ stocks that are most sensitive to earnings announcements is given in Section IV, and our results are summarized in Section V.