• 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.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.125-139
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• 2021

Continuous subway line expansion over the years in Seoul metropolitan area has contributed to improved accessibility to public transport. Since public transport accessibility has a significant impact on housing decisions, quantitative analysis of correlation between housing prices and public transport accessibility is regarded as one of the most important factors for planning better housing policies. This study defines the reduction of traveling time resulted from the construction of new metro stations despite them not being the closest stations as 'Network Expansion Effect', and seeks to understand how the Network Expansion Effect impacts on housing prices. The study analyzes monthly rent data converted from upfront lump sum deposit, so called Jeonse in Korea, from 2012 to 2018, through 'A Modified Repeat Sales Model.' As a result, the effect of 'Network Expansion' on rental prices in Seoul has stronger during the period of 2017 to 2018 than the base period of 2012 to 2014, which suggests the 'Network Expansion' has a meaningful effect on rent. In addition, in comparison between the most and the least affected group of apartments by 'Network Expansion Effect', the most affected group has more price increase than the least affected group. These findings also indicate that different levels of 'Network Expansion Effect' have various influences on the value of residential real estate properties.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.5-20
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• 2018

This paper aims to analyze characteristic by the cities focused on the ratio of new apartment resale that is one of the apartment unit sale market, which has been increased recently. So, this study examined characteristics of population, apartment trade & sale, housing with 162 cities and counties and performed multiple regression analysis with dependent variable, ratio of new apartment resale. As a result. the factors affecting the ratio of new apartment resale are 7variables, apartment sales rate, transfer of ownership, apartment turnover rate, sale volume, regional apartment rate, population increasing rate, housing average apartment sale price rate. In terms of the increase in apartment sales prices, the rate of sales price increase was relatively low in areas where the transaction rate for apartment sales is high, and the number of apartment sales right transactions increased as the number of other ownership transfers rose. As a result, the data will be based on the improvement of the government's policies and systems to stimulate the transaction focused on the real estate agents in the apartment market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6315-6324
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• 2015

This study analyses the effects of household finances on rental price of apartment in Seoul which play a major role in real estate policy. We estimate VAR models using time series data. Economy variables such as sales price of apartment in Seoul, consumer price index, hiring rate, real GNI and loan amount of housing mortgage, which relate to household finances and influence the rental price of apartment, are used for estimation. The main findings are as follows. In the short term, the rental price of apartment is impacted by economy variables. Specifically, Relative contributions of variation in rental price of apartment through structural shock of economy variables are most influenced by their own. However, in the long term, household variables are more influential to the rental price of apartment. These results are expected to contribute to establish housing price stabilization policies through understanding the relationship between economy variables and rental price of apartment.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.207-208
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• 2019

Currently, as the public interest for environmental issues has grown rapidly, the needs for G-SEED have also increased. However, as investment according to eco-friendly elements is inevitable to receive G-SEED certification, it is necessary to find out whether or not the sales price of apartments have increased compared to investment costs. Therefore, the objective of this study is to analyze the sales price of apartments according to G-SEED by using T-test. To achieve the objective, First, variables affecting on the sales price of apartments are selected. Second, the data are collected by using GIS(Geographic Information System). Third, after testing the normality, a comparison analysis is conducted on the sales price between G-SEED certified and non-certified apartments by using T-test. As a result, it is concluded that G-SEED certified apartments are more expensive than non-certified apartments. In the future, these findings can be utilized to develop of apartments price calculation model based on the G-SEED.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.116-124
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• 2016

In the past, once apartments were built by housing construction companies, their presale went smoothly. Therefore, the developer and construction companies in Korea were extremely competitive in the housing market. However, when the 1997 foreign exchange crisis and 2008 global financial crisis occurred, the quantity of unsold new housing stocks rapidly increased, which caused construction companies to experience a serious liquidity crisis. This paper aims at analyzing the influence of Liquidity on the Housing Market before and after Macroeconomic Fluctuations using VECM. The periods from September 2001 to September 2008 and from October 2008 to October 2015, which were before and after the Subprime financial crisis, were set as Models 1 and 2, respectively. The results are as follows. First, it is important to develop a long-term policy for the housing transaction market to improve household incomes. Second, due to the shortage in the supply of jeonse housing, structural changes in the housing market have appeared. Thus, it is necessary to seek political measures to minimize the impact of transitional changes on the market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.635-646
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• 2016

This paper proposes a housing business model, where the presale and Chonsei housing are supplied under a presale system at the same time based on the characteristic correlation between the housing presale market and Chonsei market in Korea. Markowitz portfolio theory was used to review the risk diversification effects from the changes in the ratio between the presale housing supply and the Chonsei housing supply. The housing sale price indicator was used as a proxy variable to determine the presale housing supply. The housing Chonsei price indicator was used as a proxy variable to determine the Chonsei housing supply. The proposed housing business model was applied to major areas in Korea to examine the risk diversification effect. Comparisons of the regional portfolio analyses showed that the flexibility of the proposed housing business model can be quite effective because each regional housing market exhibits different characteristics. Market participants, such as developers, construction companies, consumers, and government, can expect various effects through the proposed housing business model. Nevertheless, policy support is necessary for practical applications of the proposed housing business model. In particular, public funds from the government need to be introduced.

• Land and Housing Review
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• v.14 no.3
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• pp.55-75
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• 2023

Based on prior studies on real estate policy, tax policy, and financial policy, this study examined how tax policy and financial policy affected real estate prices using monthly data from January 2014 to December 2021. We performed a VAR model using unit root tests, cointegration tests, as well as conducted impulse response analysis and variance decomposition analysis. The results are as follows. First, the tax regulation index and the financial regulation index had no discernible impact on housing prices. Specifically, a one-sided stabilizing regulatory policy was ineffective and, instead, led to unintended side effects, such as price increases resulting from reduced transaction volume. Secondly, mortgage rates had a negative impact on the housing sale price index. In other words, an increase in interest rates might led to a decrease in housing prices. Thirdly, an increase in the transfer difference, which involves capital gains tax, has a positive effect on housing prices. This led to rising housing prices because the transfer taxes were shifted to buyers, causing them to hesitate to make purchases due to the increased tax burden. Fourthly, both acquisition taxes and mortgage loans had relatively little impact on housing prices.

• Smart Media Journal
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• v.11 no.3
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• pp.62-73
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• 2022

Researches on the prediction of domestic apartment sales price have been continuously conducted, but it is not easy to accurately predict apartment prices because various characteristics are compounded. Prior to predicting apartment sales price, the analysis of major factors, influencing on sale prices, is of paramount importance to improve the accuracy of sales price. Therefore, this study aims to analyze what are the factors that affect the apartment sales price in Gwangju, which is currently showing a steady increase rate. With 6 years of Gwangju apartment transaction price and various social factor data, several maching learning techniques such as multiple regression analysis, random forest, and deep artificial neural network algorithms are applied to identify major factors in each model. The performances of each model are compared with RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and R² (coefficient of determination). The experiment shows that several factors such as 'contract year', 'applicable area', 'certificate of deposit', 'mortgage rate', 'leading index', 'producer price index', 'coincident composite index' are analyzed as main factors, affecting the sales price.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3260-3269
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• 2014

The construction industry has strong ties with other industries, and so construction company insolvency also has a strong influence on other industries. Prediction models addressing the insolvency of construction company have been well studied. Although factors contributing to insolvency must precede those of predictions of insolvency, studies on these contributing factors are limited. The purpose of this study is to analyze the influence of changes in the housing market on construction company insolvency by using the Vector Error Correction Model. Construction companies were divided into two groups, and the expected default frequency(EDF), which indicates insolvency of each company was measured through the KMV model. The results verified that 10 largest construction companies were in a better financial condition compared to relatively smaller construction companies. As a result of conducting impulse response analysis, the EDF of large companies was found to be more sensitive to housing market change than that of small- and medium-sized construction companies.