• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.55-63
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• 2015

The need for estimating the real transaction price of land is increasing in order to build foundation for transparent land transaction and fair taxation. This study looked into the applicability of cokriging combining real transaction price of land, altitude and gradient for effective price estimation on the points where the real transaction does not take place in the course of using the real transaction price of land. The real transaction price of land have been estimated using the real transaction materials of Yeongcheon, Gyeongsangbuk-do from January 2012 to June 2014, and the results have been compared with the estimation results of ordinary kriging. As a result of analyzing the mean error and root mean square error (RMSE) of the estimated price and 2,575 verification points, it was found that compared to ordinary kriging, cokriging results were more effective in terms of the real transaction price estimation and actualization. The reason that cokriging is more effective in the real transaction price estimation is because it takes account of altitude and gradient which are the forces influencing the land value.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.217-228
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• 2018

Since the real estate reporting system was first introduced, about 2 million real estate transaction per year have been reported over the last 10 years with an increasing demand for real estate price estimates. This study looks at the applicability and superiority of the regression-kriging method to derive effective real transaction prices estimation on the location where information about real transaction is unavailable. Several issues on predicting the real estate price are discussed and illustrated using the real transaction reports of Jinju, Gyeongsangnam-do. Results have been compared with a simple regression model in terms of the mean absolute error and root square error. It turns out that the regression-kriging model provides a more effective estimation of land price compared to the simple regression model. The regression-kriging method adequately reflects the spatial structure of the term that is not explained by other characteristic variables.

• Korea Real Estate Review
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• v.28 no.4
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• pp.63-73
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• 2018

The purpose of this study is to identify the key factors affecting official land values and the appropriateness of the assessed land price, to find out what determines the real estate price and to assess the appropriateness of the valuation. This study explored whether actual transaction prices of forest land located in six Gu districts in southern and northern parts of Han river are appropriate using independent sample t-analysis and logistic regression analysis. Results showed that regional differences and shape were adequate for development restriction areas, whether biotope was designated, whether or not to be preserved, differences in pitch, and differences in use, and differences in bearing and approach. Thorough analysis of unique factors that determine forest land prices must be carried out in advance and the findings should be applied to the examination and assessment of official land values. The forest land appraisal system is closely related to the public's economic activity, thus it is necessary to apply forest land value determinants considered to be significant by market participants to the forestland appraisal system. I look forward to seeing variables related to the appropriateness of forest land transactions drawn from this study being used as indices for settlement of forest land transaction orders and market stabilization.

• Korean Journal of Construction Engineering and Management
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• v.20 no.5
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• pp.115-124
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• 2019

The purpose of this study is to analyze the housing price variation within the redevelopment project district, affected by the characteristics of project and implementation stage. This study implemented the hedonic price model employing the actual transaction price with 24 dependent variables from 2006 to 2016 inside 19 redevelopment districts in Seoul. Research finding indicates that the larger ratio of the number of tenants and general distribution, the smaller ratio of rented households and the more positive effect of housing price. It is noteworthy that this study demonstrated the actual transaction price of houses located within the project districts by implementation stage. This study is expected to help the policy makers, the developers and the investors make more reliable decisions on the feasibility study related to the redevelopment project.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.63-70
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• 2014

This study is to analyze impact of geography and timing on the real transactions prices of apartment complexes in Seoul using data provided by the Ministry of Land, Infrastructure and Transport. The average real transactions and location data of apartment complex was combined into the GIS data. First, the pattern of apartment real transaction price change by period and by area was analyzed by kriging, the one of the spatial interpolation technique. Second, to analyze the pattern of apartment market price change by administrative district(administrative 'Dong' unit), the average of market price per unit area was calculated and converted to Moran I value, which was used to analyze the clustering level of the real transaction price. Through the analysis, spatial-temporal distribution pattern can be found and the type of change can be forecasted. Therefore, this study can be referred as of the base data research for the housing or local policies. Also, the regional unbalanced apartment price can be presented by analyzing the vertical pattern of the change in the time series and the horizontal pattern of the change based on GIS.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• Land and Housing Review
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• v.11 no.2
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• pp.33-46
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• 2020

As the fastest growing office transaction volume in Korea, there's been a need for development of indicators to accurately diagnose the office capital market. The purpose of this paper is experimentally calculate to the office price index for effective benchmark indices in Seoul. The quantitative methodology used a Case-Shiller Repeat Sales Model (1991), based on actual multiple office transaction dataset with over minimum 1,653 ㎡ from Q3 1999 to 4Q 2019 in the case of 1,536 buildings within Seoul Metropolitan. In addition, the collected historical data and spatial statistical analysis tools were treated with the SAS 9.4 and ArcGIS 10.7 programs. The main empirical results of research are briefly summarized as follows; First, Seoul office price index was estimated to be 344.3 point (2001.1Q=100.0P) at the end of 2019, and has more than tripled over the past two decades. it means that the sales price of office per 3.3 ㎡ has consistently risen more than 12% every year since 2000, which is far above the indices for apartment housing index, announced by the MOLIT (2009). Second, between quarterly and annual office price index for the two-step estimation of the MIT Real Estate Research Center (MIT/CRE), T, L, AL variables have statistically significant coefficient (Beta) all of the mode l (p<0.01). Third, it was possible to produce a more stable office price index against the basic index by using the Moore-Penrose's pseoudo inverse technique at low transaction frequency. Fourth, as an lagging indicators, the office price index is closely related to key macroeconomic indicators, such as GDP(+), KOSPI(+), interest rates (5-year KTB, -). This facts indicate that long-term office investment tends to outperform other financial assets owing to high return and low risk pattern. In conclusion, these findings are practically meaningful to presenting an new office price index that increases accuracy and then attempting to preliminary applications for the case of Seoul. Moreover, it can provide sincerely useful benchmark about investing an office and predicting changes of the sales price among market participants (e.g. policy maker, investor, landlord, tenant, user) in the future.

• Spatial Information Research
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• v.21 no.6
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• pp.69-80
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• 2013

Our country has made every efforts to develop Real Estate Transaction culture with emphasis on Licensed Realtors by introducing Real Estate Transaction Law in 1983. Also, MOLIT(Ministry of Land, Infrastructure and Transport) designated several organizations including KAR(Korea Association of Realtors) as Real Estate Transaction Information Network Licensees for data credibility enhancement and transaction transparency. Nevertheless, the level of law abiding spirit and transaction culture are still similar to those of the old 'Bokdeokbang' era. The under-developed transaction behaviors prevent the social capital of people's credibility on Licensed Realtors from advancing, and results in the outcomes of unnecessary social cost. That is, very low credibility on the data on Sales Items in the market and the fear of speculative real estate price uprise and market distortions are continuing on. In this context, the purpose of this study is to propose the model of GIS-based Modernized Real Estate Transaction System and its execution policies to support credible Real Estate Information to the general public for efficient transactions in the market. Accordingly, the study aims at contributing to the modernization of Real Estate Transactions, fostering competitiveness of Realtors in the Real Estate Market.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.133-153
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• 2020

Officially assessed land price has been the index of South Korea since 1989 throughout different sectors of tax and welfare. Officially assessed land price is used as a tax valuation for the tax on property holdings, and the equity of such is the most important factor in the fair taxation for the people of South Korea. On this wise, this research analyzed and verified the horizontal and vertical inequity of officially assessed land price in Seoul by using the real transaction data between 2016 and 2018. In fact, Seoul's assessment ratio for the entire three-year period was 60.64% and it showed to increase each year. Horizontal equity was found to be most favorable in 2017, and the horizontal equity of each borough of Seoul appeared to improve each year. Vertical inequity was found to have reverse inequality in most boroughs of Seoul, however, some parts of Gangnam districts such as Gangnam-gu, Seocho-gu, and Gangdong-gu presented progressive inequality. Such example showed the need for improvement in terms of balance by each borough. The use of quantile regression demonstrated reverse inequality in most quantile, but, the differences in the value of the coefficient by each quantile showed the need for improvement of officially assessed land price with the equity of each quantile. Through the equity verification of officially assessed land price, it was analyzed that the lack of equity was found by year, by borough, and by use district. In order to redeem the lack of equity, the government must systematically supplement the real-estate disclosure system by initiating ratio studies to verify horizontal and vertical equity.

• 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.