• Land and Housing Review
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• v.15 no.2
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• pp.89-106
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• 2024

Apartments constitute 64% of the housing type composition, representing the highest proportion among housing types. This proportion has been increasing annually. Given this trend, apartment prices are likely to have a significant impact on the national economy and people's livelihoods. This study examines the impact of the recent development of Seodaegu Station on the surrounding apartment market, with a specific focus on the effects of the educational environment. To this end, we conduct empirical analysis employing a hedonic price model and spatial autocorrelation analysis, based on actual transaction price data from the Ministry of Land, Infrastructure, and Transport. The study revealed three key findings: first, the development of Seodaegu Station positively impacted apartment prices. Second, this positive effect increases with the proximity to Seodaegu Station. Third, the enhancement of the educational environment nearby the Seodaegu Station development also positively influenced apartment prices. This study aims to serve as baseline research output for the public management of future metropolitan transportation facility development projects and for predicting apartment price trends.

• 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.

• Journal of the Economic Geographical Society of Korea
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• v.20 no.3
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• pp.405-418
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• 2017

SHIFT is public rental housing policy introduced by Seoul Metropolitan in 2007, which works as Chonsei(korean unique deposit rental system). This paper examines the effect of SHIFT on Chonsei prices of neighborhood apartments. To estimate the change in prices of Chonsei after the provision of SHIFT, I collect data on Chonsei prices of apartments within a 5km radius from the SHIFT housings. Summary of main results are following. Chonsei prices of the apartments within a 2-3km radius decreased by 4.4% after the provision of SHIFT housings. In contrast, when it comes to apartments within a 1-2km radius, I can't find the stochastic relationship between the provision of SHIFT hosing and price changes. This results can be explained by "Offset effects" caused by real estate development. Provision of SHIFT can sequentially induce nearby area's development, which plays a factor in the effect of price increases. And this offset effects varies in each apartment complex depending on demand for Chonsei and supply of the SHIFT.

• 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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.249-250
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• 2016

Korean government has announced declared prices of apartment reflecting market condition each year. Therefore, on that basis, apartment owners have used as basic data trading apartments and the government has been used to calculate the tax. However, the sales prices and declared prices of apartment has occurred difference depending on the region and the brand. This study has analyzed and compared regional differences in sales price of apartments. The results of this study, we have known that sales price of apartments was a big difference depending on the region and the gross area. Especially, Seoul and Gyeonggi Province are the highest. And sales price of Southeast and urban area are the highest in Seoul. In the future, it is necessary that gap analysis between sales price and declared prices of apartment. And It is needed to develop apartment index considering the region and the gross area.

• Korean Journal of Construction Engineering and Management
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• v.25 no.4
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• pp.45-52
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• 2024

Estimating the sales price of a residential building development project is difficult because of it has many complex variables such as location, environment, and economic conditions. Many previous studies related to influence factors of the sales price is to identify by survey of experts and it is few studies by comparing with actual sales price. Accordingly, the purpose of this study is to identify the factors influenced on the projects by using correlation analysis from collected actual data in this study. For the purpose, first, the factors such as economy, location, housing, financial environmental factors were identified from previous studies. Second, data were collected on actual sale prices and selected factors. Finally, the actual sales price and factors were compared and analyzed by using correlation analysis. As a result, the R² values of economy, location, housing and financial environmental factors were over 0.5 respectively. Therefore, it was confirmed that these factors were significantly correlated with actual sales price. The results of this study are expected to be utilized as basic data for research and development of a new sale prices prediction model.

• Land and Housing Review
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• v.12 no.2
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• pp.33-45
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• 2021

Due to recent changes in government policy, officetels have received attention as alternative assets, along with the uplift of office and apartment prices in Seoul. However, the current officetel price indexes use small-size samples and, thus, there is a critique on their accuracy. They rely on valuation prices which lag the market trend and do not properly reflect the volatile nature of the property market, resulting in 'smoothing'. Therefore, the purpose of this paper is to create the officetel price index using transaction data. The data, provided by the Ministry of Land, Infrastructure and Transport from 2005 to 2020, includes sales prices and rental prices - Jeonsei and monthly rent (and their combinations). This study employed a repeat sales model for sales, jeonsei, and monthly rent indexes. It also contributes to improving conversion rates (between deposit and monthly rent) as a supplementary indicator. The main findings are as follows. First, the officetel price index and jeonsei index reached 132.5P and 163.9P, respectively, in Q4 2020 (1Q 2011=100.0P). However, the rent index was approximately below 100.0. Sales prices and jeonsei continued to rise due to high demand while monthly rent was largely unchanged due to vacancy risk. Second, the increase in the officetel sales price was lower than other housing types such as apartments and villas. Third, the employed approach has seen a potential to produce more reliable officetel price indexes reflecting high volatility compared to those indexes produced by other institutions, contributing to resolving 'smoothing'. As seen in the application in Seoul, this approach can enhance accuracy and, therefore, better assist market players to understand the market trend, which is much valuable under great uncertainties such as COVID-19 environments.

• Journal of the Korea Institute of Building Construction
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• v.22 no.1
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• pp.81-90
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• 2022

With apartment purchase prices rising, small and medium-sized cities have been highlighted as areas in which real estate speculation is overheated, and thus designated as target districts for adjustment. In addition, tax policy is constantly being adjusted in an attempt to stabilize real estate prices. The purpose of this study is to analyze the basic effect of tax policy on the purchase price of apartments in small and medium-sized cities. This study selected apartments in the Daejeon area that were constructed between 1990 and 2015. In addition, tax policy was divided into regulatory policy and easing policy based on tax increase and tax cut. This study analyzes the short-term difference of one year before and after the change in the purchase price of apartment houses. In addition, this study set the time when real estate policy was implemented and the actual transaction price of apartments in Daejeon as the analysis targets, and analyzed the correlation between tax policy and apartment sales prices through the NPV technique and T-test results. Through the study, it was found that most tax policies changed apartment purchase prices in the short term.

• 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.

• 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.