• 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.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.65-76
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• 2014

Housing prices are influenced by external shock factors such as real estate policy or economy. Thus, the intervention effect is important for the development of forecasting model for housing price index. In this paper, we examined the degree of effective power of external shock factors for forecasting housing price index and analyzed time series models for efficient forecasting of housing price index. It is shown that intervention models are better than other models in forecasting results using real data based on the accuracy criteria.

• 주택과사람들
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• s.156
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• pp.32-34
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• 2003

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.27-29
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• 2023

불안정한 부동산 가격은 지속적인 사회 문제로 거론되고 있는데 이는 부동산 매매 가격을 예측할 수 있는 정확한 지표가 체계적이고 구체적으로 확립되지 않았기 때문이다. 본 논문은 가격변동에 주요하게 영향을 미치는 특성을 파악하여 가격 예측 지표로 활용하기 위해 머신러닝 모델을 적용하여 특성 분석을 수행한다. 이를 위해 한국부동산원에서 제공하는 2021년 10월부터 2022년 9월까지 1년간의 역 주변 500M 이내 거래 데이터 약 30만 6천 개를 어떠한 과정으로 전처리하여 머신러닝 모델에 적용하였는지 기술한다.

• Korea Real Estate Review
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• v.27 no.3
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• pp.41-50
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• 2017

Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications. This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Korean Journal of Construction Engineering and Management
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• v.17 no.3
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• pp.13-22
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• 2016

Remodeling of multi-family house (MFH) has emerged as a major issue in the construction industry. Many decision-makers are struggling to determine whether to conduct a remodeling because of insufficient information including standard or method for projecting the price of their facilities after remodeling works. In this context, this research has conducted for analyzing how the price of the MFH is shifted by remodeling works. To achieve this research goal, (i) fourteen MFH remodeling cases were collected and (ii) price variation for the collected cases was analyzed in order to figure out how remodeling work affects the price of MFH cases. Finally, this research suggested the factors determining price of the MFH, wherein remodeling work has been conducted. From the results of this research, it is expected that the decision-makers can understand what is the crucial factors for determining the price of their MFH when they plans remodeling, and further this research can be a corner stone for developing a model for predicting the price of MFH if remodeling would be performed.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.607-612
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• 2021

This study estimated the impact of the shock of housing price increase on the total fertility rate and the contribution of each variable to changes in the TFR. This study is differentiated by estimating the contribution rate of each variable to the fertility rate through the Shapley decomposition and the panel VAR's forecast error variance decomposition, which previous studies have not attempted. The main results of this study are as follows. First, the decline in the TFR in Korea has been strongly influenced by the recent decline in the total fertility rate, and this influence is expected to continue in the future. In the case of housing costs, in the past, housing sales prices had a relatively small contribution to changes in the total fertility rate compared to the jeonse prices, but their influence is expected to increase in the long term in the future. It has been demonstrated that private education expenses other than housing sale price and Jeonse price also acted as a major cause of the decline in the total fertility rate.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1573-1583
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• 2016

It is necessary to check mutual correlations between related variables because housing prices are influenced by a lot of variables of the economy both internally and externally. In this paper, employing the Granger causality test, we have validated interrelated relationship between the variables. In addition, there is cointegration associations in the results of the cointegration test between the variables. Therefore, an analysis using a vector error correction model including an error correction term has been attempted. As a result of the empirical comparative analysis of the forecasting performance with ARIMA and VAR models, it is confirmed that the forecasting performance by vector error correction model is superior to those of the former two models.