• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.43-51
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• 2012

The OLS(ordinary least squares) method is widely used in hedonic housing models. One of the assumptions of the OLS is an independent and uniform distribution of the disturbance term. This assumption can be violated when the spatial autocorrelation exists, which in turn leads to undesirable estimate results. An alterative to this, spatial econometric models have been introduced in housing price studies. This paper describes the comparisons between OLS and spatial econometric models using housing transaction prices of Busan, Korea. Owing to the approaches reflecting spatial autocorrelation, the spatial econometric models showed some superiority to the traditional OLS in terms of log likelihood and sigma square(${\sigma}^2$). Among the spatial models, the SAR(Spatial Autoregressive Models) seemed more appropriate than the SAC(General Spatial Models) and the SEM(Spatial Errors Models) for Busan housing markets. We can make sure the spatial effects on housing prices, and the reconstruction plans have strong impacts on the transaction prices. Selecting a suitable spatial model will play an important role in the housing policy of the government.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.61-93
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• 2019

This study is to apply Hedonic Price Method in analyzing residents' preferences for three types of urban green space (UGS, rivers, urban parks, and forests) near the apartment complexes in Seoul. Based on hedonic price function estimation results, residents in Seoul preferred for the urban amenity that was provided by the view and accessibility (in terms of both within 10 minutes and distance) of rivers and urban parks near the apartment complexes, but not forests. The annual benefits calculated using the shadow prices are about 550~600 thousand won for the urban park views and about 800 thousand won for the accessibility, which is 2-3 times higher than river views and accessibility. On the other hand, forest views and accessibility did not have significant effects on apartment prices, except the view of Bukhan mountain for the residents of Gangbuk area. Based on the empirical results, Seoul residents' preferences for urban parks would have important implications for the urban park sunset program that will be initiated from July 2020.

• Journal of the Korean Regional Science Association
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• v.36 no.1
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• pp.37-50
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• 2020

Estimating investment profits of real estate is critical to understand real estate markets and create relevant policy as real estate market and capital market combines closely. Thus, this study applied the concept of Tobin's Q to estimate investment profits for apartments as well as row-houses and multi-family homes in Seoul from 2010 to 2018. Investment profits were estimated by two approaches: subtracting the replacement cost from the transaction price and calculating ratio of the transaction price to the replacement cost, respectively. The spatio-temporal changes in investment profits were apparent in apartments compared with row-houses and multi-family homes. As a result of analyzing the spatial econometrics models, the investment profit was higher in the area with high density and new developments regardless of the housing types. The framework and key findings would be the effective reference to understand residential investment behavior, create relevant housing policy, introduce value capture of windfall, measure regional competitiveness, and estimate housing bubble.

• Korea Real Estate Review
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• v.24 no.1
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• pp.7-14
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• 2014

This study, as the temporal and spatial data for the real price apartment in Seoul from January 2006 to June 2013, empirically compared and analyzed the estimation result of apartment price using OLS by hedonic price model for the problem of space-time correlation, temporal autoregressive model (TAR) considering temporal effect, spatial autoregressive model (SAR) spatial effect and spatiotemporal autoregressive model (STAR) spatiotemporal effect. As a result, the adjusted R-square of STAR model was increased by 10% compared that of OLS model while the root mean squares error (RMSE) was decreased by 18%. Considering temporal and spatial effect, it is observed that the estimation of apartment price is more correct than the existing model. As the result of analyzing STAR model, the apartment price is affected as follows; area for apartment(-), years of apartment(-), dummy of low-rise(-), individual heating (-), city gas(-), dummy of reconstruction(+), stairs(+), size of complex(+). The results of other analysis method were the same. When estimating the price of real estate using STAR model, the government officials can improve policy efficiency and make reasonable investment based on the objective information by grasping trend of real estate market accurately.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7672-7676
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• 2015

This study examines the dynamic relations between housing price and trading volume in a set of apartment markets in Republic of Korea to explore the informational role of trading volume in predicting the price volatility. Using monthly index data, EGARCH model is utilized to test for volume effect. To estimate the EGARCH-based volatility, two different sets of region are applied for the monthly return. Strong evidence has been found towards housing turnover leading price volatility, this supports previous studies on financial sector(s). These findings also support that trading volume in the housing market contains information on investor sentiment which, in turn, has a valuation effect on the price.

• 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.

• Korea Real Estate Review
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• v.28 no.1
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• pp.91-104
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• 2018

In this study, the occurrence and degree of herding behavior as a market participant behavior in a housing market were analyzed. For the analysis method, the actual sales price was applied in the CSAD (Cross-sectional Absolute Deviation) model, which has been used the most of late for herding behavior analysis. For the analysis contents, these were subdivided into region, elapsed year, size, and market condition to analyze the regionality and the internal and external factors. For the study results, first, there was no herding behavior in the entire region of Seoul. By region, herding behavior occurred in the downtown, southeast, and northwest regions, which coincided with the results of the precedent study (Ngene et al., 2017). Second, in the market analysis by elapsed year, herding behavior was captured in dilapidated dwellings. By size, herding behavior was observed in small-scale ($60m^2$ or less) apartments and in $85m^2$ or higher and less than $102m^2$ national housing units. Third, during the time of the global financial crisis, herding behavior was not observed in all the regions, whereas when the market situations were in a boom cycle, it was observed in the northwest region. These results suggest that there is a difference from the stock market, where in a period of recession, herding behavior occurs intensively with the expanding fear of incurring losses. This study is significant in that it analyzed the market participant behaviors in the behavioral economic aspects to better understand the abnormal phenomenon in a housing market, and in that it additionally provides a psychological factor - market participant behavior - in market analysis.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.419-430
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• 2023

Currently, NFTs have no dominant application except for the proof of ownership for digital content, and it also have small liquidity problem, which makes their price difficult to predict. Real estate usually has very high barriers to investment due to its high pricing. Real estate can be converted into NFTs and also divided into small value fungible tokens (FTs), and it can increase the the volume of the investor community due to more liquidity and better accessibility. In this document, we implement and design a system that allows ordinary users can invest on high priced real estate utilizing Black Litterman (BL) model-based Portfolio investment interface. To this end, we target a set of real estates pegged as collateral and issue NFT for the collateral using blockchain. We use oracle to get the current real estate information and to monitor varying real estate prices. After tokenizing real estate into NFTs, we divide the NFTs into easily accessible price FTs, thereby, we can lower prices and provide large liquidity with price volatility limited. In addition, we also implemented BL based asset portfolio interface for effective portfolio composition for investing in multiple of real estates with small investments. Using BL model, investors can fix the asset portfolio. We implemented the whole system using Solidity smart contracts on Flask web framework with public data portals as oracle interfaces.