• Journal of Digital Contents Society
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• v.4 no.2
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• pp.147-155
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• 2003

The problem of the objective adequacy on land estimation has been seriously discussed with the Issue of paradigm conversion on land policy as the economy situation has been worse enough to be confused due to the sale price which could not be predicted in Korea. Especially as the data of the government land value is not recognized as the official one, the separate alternation has been adopted to calculate the value of land compensation or development allotment. Rather it has raised a question in argument that the system of the declared value does not accomplish its basic function properly on the contrary of the original purpose to unify the computation of the government land value. To reconsider the adequacy of government land value, the most crucial factor is to select the items of land estimation reasonably. In addition, it is urgently required to develop the system of digital contents to provide the data of land evaluation which most of people could trust, who are facing of the flood of information through internet.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.717-729
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• 2020

As the environmental pollution problem in modern society is rapidly changing with industrialization, the environmental pollution problem has a direct or indirect effect on various fields. In particular, heavy industry pollutants can be a significant variable in site selection and realestate value. Therefore, this study is based on transaction data of 13apartment complexes in Maanshan City, a representative steel city in China, and uses the Hedonic Price Model to study the effect on real estate prices, mainly on heavy industry pollution during environmental pollution. The conclusion shows that the farther away from the source of pollution, the higher values are.

• Knowledge Management Research
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• v.22 no.1
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• pp.105-120
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• 2021

Recently, there has been a dramatic change in real estate markets with the development of information technology. The word, Proptech, is defined as the real estate transaction innovation motivated by various types of information technology including artificial intelligence, sensing technology and big data. The objective of this study is to provide a value-creation framework for Proptech business based on the understanding of how and what types of values are created and shared, which gives organization to develop strategies and business models. And a new classification scheme of Proptech business is also suggested based on the recognition of created values along the development of Proptech business. Then, the proposed matrix is applied to derive the business value such as intangibility value, relational value and enhancement value with the case analysis on the each components of Proptech business.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1244-1247
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• 2015

본 논문에서는 소상공인의 창업 성공을 지원하는 점포 평가 분석 사례를 소개하여 기업의 빅데이터 도입 및 활용을 촉진하고자 한다. 본 사례에서는 카드사 거래 정보, 가맹점 정보, 부동산 가격 정보, 부동산 통계 정보, 감정평가 정보, 조사업무관련 정보 및 인허가 개폐업 정보를 활용해 36만개의 GIS 블록과 GEO 컨텐츠를 생산하여 빅데이터 분석을 실시하였다. 체계적인 분석을 위해 상권 평가 지수, 업종 평가 지수, 입지 평가 지수, 임대료 추정, 매출 추정, 적정면적 추정 등의 상권, 업종, 입지에 대한 지표를 개발하였다. 이를 통해 상가와 상권에 대한 분석 자료를 제공하여 과밀창업의 예방과 신중한 창업의 유도를 통해 창업실패로 유발 될 수 있는 경제적 비용의 감소 효과를 이룰 것으로 판단된다.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.5-20
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• 2018

This paper aims to analyze characteristic by the cities focused on the ratio of new apartment resale that is one of the apartment unit sale market, which has been increased recently. So, this study examined characteristics of population, apartment trade & sale, housing with 162 cities and counties and performed multiple regression analysis with dependent variable, ratio of new apartment resale. As a result. the factors affecting the ratio of new apartment resale are 7variables, apartment sales rate, transfer of ownership, apartment turnover rate, sale volume, regional apartment rate, population increasing rate, housing average apartment sale price rate. In terms of the increase in apartment sales prices, the rate of sales price increase was relatively low in areas where the transaction rate for apartment sales is high, and the number of apartment sales right transactions increased as the number of other ownership transfers rose. As a result, the data will be based on the improvement of the government's policies and systems to stimulate the transaction focused on the real estate agents in the apartment market.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.327-334
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• 2020

Recently, mobile and smart devices are rapidly spreading. As a result, real estate services, which were formerly face-to-face, have now been replaced by mobile environments. This study focuses on the real estate app service and 261 samples were used for the empirical analysis. The results of the hypotheses test using structural equation model are summarized as follows. First, performance expectancy, facilitating conditions, security and aesthetic perceived by users of real estate services have positively influence on positive attitude, but effort expectancy and social influence do not. Second, positive attitude of real estate services have positive effects on service Acceptance. Third, involvement moderated the relationship between positive attitude and service Acceptance. Based on the results of the analysis, it provided meaningful implications for practitioners and researchers in related fields.

• Korea Real Estate Review
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• v.27 no.1
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• pp.17-32
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• 2017

The openness of residential space directly affecting lighting, view, and ventilation leads to the variation of open floor plan type in apartment construction project. This study intends to substantiate the effect to the apartment price by design property of open floor plan based on actual design information of apartment and price. The open floor plan type and associated design property, and actual transaction price of apartment have been considered as variables for analysis by the hedonic price function model and artificial neural networks model. Research findings indicate that the openness affects the price of apartment positively and the three sides open plan is the most preferred with the highest price. This study aims to provide the implication to the developer in planning and design stage of apartment and the purchaser seeking the suitable price by floor plan design.

• 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.

• Korea Real Estate Review
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• v.28 no.2
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• pp.7-20
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• 2018

This paper has statistically analyzed the determining factors that affect office building sale prices by focusing on strata property sales through the hedonic price function. In this study, 1,171 office building transaction cases were analyzed in Seoul from 2000 to 2017. To determine the influence of various factors on office building sale prices, independent variables included factors that represented macroeconomic characteristics, locational characteristics, physical characteristics, and deal characteristics. The analysis of the strata property sales, which is a major concern in this study, showed that strata property sales enjoyed a discount of about 1.56 million won per pyeong out of the entire sales. In terms of the discount rate, strata property sales were at a 12.6% discount compared to entire property sales, so it was found that strata property sales significantly influenced office building selling price. This is due to the fact that the owner of the strata property encounters more difficulties in distributing cost than the sole proprietor in terms of property rights and the exercise of management rights. The results of this study are expected to contribute in securing transparency in transactions and risk management strategies in the future.