• Journal of Environmental Policy
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• v.12 no.1
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• pp.3-35
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• 2013

Through Influence between the qualitative Environment-friendly Planning Elements (Independent variable) of eighty Apartment House on the four boundary areas in Metropolitan 2th Newtown as representative case of domestic Composition case of Eco-City and the quantitative deal price of Apartment House (dependent variable), it was analyzed empirically with use of the statistical measuring technique. This study has been meaning that it was groped the scheme of quantitative measuring analysis on environmental economics through the developmental extention for he classification system of the qualitative Environment-friendly Planning Elements on urban & architecture. And, As the result of empirical analysis, it was confirmed empirically that the economical induction effect was much more weight and worthy of notice through Environment-friendly Planning Elements in Metropolitan 2th Newtown influenced to the deal price of Apartment House, particularly it was found noticeable meaning that Environment-friendly Planning Elements of applicabled the green new techniqe like New & Renewable Energy were used already and it was influenced much to the deal price of Apartment House.

• 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 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.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.173-183
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• 2017

We investigate the factors affecting the price of apartments using the spatial and temporal data of private real estate prices. The factors affecting the price of apartment were analyzed using geographical and temporal weighted regression (GTWR) model which incorporates the temporal and spatial variation. In contrast to the OLS, a general approach used in previous studies, and GWR method which is most widely used for analyzing spatial data, GTWR considers both temporal and spatial characteristics of the house price, and leads to better description of the house price determination. Year of construction and floor area are selected as the significant factors from the analysis, and the house price are affected by them temporally and geographically.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.4
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• pp.567-572
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• 2011

The purpose of the study is aimed at estimating the reasonable price and forecasting the sales rate of the new apartment, using transaction data of the existing apartment that is close to perfectly competitive markets. In the present paper, therefore, attempts were made to determine the relationship between the existing apartment market and the new housing market. Also conducted an empirical analysis that complemented the problems of precedent studies.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.231-240
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• 2022

This study empirically analyzed the effect of complex commercial facilities on the price of nearby apartments in a Hedonic price model. The spatial range of this study was the walking area of H Department Store located in Pangyo among the second new towns suburb of Seoul, and the time range was 2020. The dependent variable was the real transaction price of the apartment, and independent variable were the characteristics of the housing, the characteristics of the complex, and the characteristics of the region. As a result of the analysis, the area of exclusive use space, the transaction floor, and the highway accessibility had a positive effect on the price of the apartment, and the elapsed year had a negative effect on the price of the apartment. However, the size of the apartment had little effect on apartment prices, and the distance from the complex commercial facilities was shown to be related to apartment prices, indicating that apartment prices declined as it moved away from the complex commercial facilities. Therefore, this is much more influential than the influence of distance from subway stations on apartment price. This confirms that the effect factors of apartment prices and the size of their influence appear differently in the new town area and the existing metropolitan area.

• 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.

• 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.

• 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.