• 주택과사람들
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• s.199
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• pp.28-31
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• 2006

정부는 투기 수요를 억제하기 위해 8·31 대책으로 보유세를 강화하고 실거래가 과세를 정착시키는 등의 획기적인 주택세제 개편을 추진하고 있다. 하지만 부동산 시장 상황은 거래가 위축되고 가격은 더 치솟고 있다. 정부가 추진하고 있는 세제 개편의 현황과 해결 과제에 대해 살펴본다.

• Journal of the Korean Geographical Society
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• v.48 no.6
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• pp.879-896
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• 2013

The assessed land values and housing prices have been widely utilized as a basic information for the land and house trades and for evaluating governmental and local taxes. However, there exists a price difference in actual markets between the assessment level and assessed land values or housing prices. This paper emphasizes the spatial mismatch between the assessed land values and housing market prices and particularly addresses the following two aspects by focusing on spatial effects of the modifiable areal units, which would substantially affect the estimation of the assessed land values and housing prices. First, we examine the spatial distributions of the assessed land values and housing market prices, and the gap between those prices, on the basis of the aggregated spatial units(i.e., aggregation districts). Second, we explore the scale effect of the MAUP(modifiable areal unit problem) generally embedded in estimating the prices of the sampled standard lands and houses, and calibrating the correction index for the land values and housing prices for the individuals. For the application, we analysed the land values and housing prices in Seoul utilizing GIS and statistical software. As a result, some spatial clusters that the housing market prices are significantly higher than the assessed land values were identified at a finer geographic level. Also, it was empirically revealed that the statistical results from the regression of regional variables on the assessed land values for the individuals are significantly affected by the aggregation levels of the spatial units.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.31-34
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• 2023

주택 매매에 있어서 가격에 대한 예측은 매우 중요하지만, 실거래 발생 전까지는 정확한 가격을 알 수 없다. 그렇기에 주택가격을 예측하는 많은 연구가 진행되어왔다. 주택가격을 결정하는 영향요인은 크게 주택의 내부요인과 주택의 외부 요인으로 구분되는데, 내부적인 요인 (공급면적, 전용면적, 층, 방 개수 등)에 대한 연구가 많이 진행되었다. 하지만 외부적인 요인 (위치 요인, 금융요인 등)에 대한 연구는 미비하였다. 본 연구는 주택 매수자 관점에서 가격 예측 시 외부적인 요인 역시 중요하다고 판단하여 외부요인을 적용하고자 한다. 본 논문에서 제안하는 방법은 다양한 외부요인 중 주택의 위치 정보를 활용하여, 해당 정보 기반으로 도출 가능한 데이터를 추가한다. 또한 이용량에 따른 지하철역 데이터를 추가하여 관련된 여러 영향요인들을 분석 및 적용 후 머신러닝 기반 예측 모델을 생성한다. 생성된 모델들에 주택매매 실거래 데이터를 적용하여 예측 정확도를 비교 후 높은 정확성을 보이는 모델 결과에 주요하게 영향을 끼치는 요인에 관하여 기술한다.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.23-33
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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.

• 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 Digital Convergence
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• v.19 no.9
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• pp.9-18
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• 2021

Constructing the Metropolitan Railway Express (the GTX) may have an impact on consumer confidence and housing sales price located near the planned route. This study looked at how consumers' psychology and housing prices change as the large-scale transport infrastructure plane was planned. Also, it looked at the relationship between consumer sentiment and housing prices to analyze the impact of new transportation facilities inflows. Using a correlation analysis, the relationship between the consumer sentiment index and the actual transaction price of apartments was identified. The impact of GTX on the consumer sentiment index and the actual transaction price of apartments was looked at using the Difference-in-Differences methodology. Our finding shows that the construction plan of a large-scale transportation infrastructure in the metropolitan area affects the sentiment of housing consumption and actual transactions. In a situation where the government is speeding up the construction of a wide-area transportation network such as GTX with the goal of becoming a city where people can commute to downtown Seoul within 30 minutes, policies that can stabilize the housing market in transportation hubs should be suggested.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.245-256
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• 2024

Since 2020, quantitative easing and easy money policies have been implemented for the purpose of economic stimulus. As a result, real estate prices have skyrocketed. In this study, the relationship between sales and rental prices by housing type during the period of soaring real estate prices in Busan was analyzed spatio-temporally. Based on the actual transaction price data, housing type, transaction type, and monthly data of district units were constructed. Among the spatio-temporal analysis models, the SpVAR, which is used to understand the temporal and spatial effects of variables, and the GSTAR, which is used to understand the effects of each region on those variables, were used. As a result, the sales price of apartment had positive effect on the sale price of apartment, row house, and detached house in the surrounding area, including the target area. On the other hand, it was confirmed that demand was converted to apartment rental due to an increase in apartment sales prices, and the sale price fell again over time. The spatio-temporal spillover effect of apartments was positive, but the positive effect of row house and detached house were concentrated in the original downtown area.

• 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 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.