• 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.

• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.51-61
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• 2023

Purpose - Since COVID-19, the government's expansion of liquidity to stimulate the economy has resulted in an increase in private debt and an increase in asset prices of such as real estate and stocks. The recent sharp rise of the US Federal fund rate and tapering by the Fed have led to a fast rise in domestic interest rates, putting a heavy burden on the Korean economy, where the level of household debt is very high. Excessive household debt might have negative effects on the economy, such as shrinking consumption, economic recession, and deepening economic inequality. Therefore, now more than ever, it is necessary to identify the causes of the increase in household debt. Design/methodology/approach - Main methodology is regression analysis. Dependent variable is household loans from depository institutions. Independent variables are consumer price index, unemployment rate, household loan interest rate, housing sales price index, and composite stock price index. The sample periods are from 2017 to May 2022, comprising 72 months of data. The comparative analysis period before and after COVID-19 is from January 2017 to December 2019 for the pre-COVID-19 period, and from Jan 2020 to December 2022 for the post-COVID-19 period. Findings - Looking at the results of the regression analysis for the entire period, it was found that increases in the consumer price index, unemployment rate, and household loan interest rates decrease household loans, while increases in the housing sales price index increase household loans. Research implications or Originality - Household loans of depository institutions are mainly made up of high-credit and high-income borrowers with good repayment ability, so the risk of the financial system is low. As household loans are closely linked to the real estate market, the risk of household loan defaults may increase if real estate prices fall sharply.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.55-62
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• 2019

This study analyzed the relationship between housing price, purchase price, Gini coefficient, interest rate, and the employment, considering that the change in housing price was an important factor influencing macroeconomic variables and income inequality. The panel VAR model was constructed considering the panel data, and the Granger causality, Impulse response and Variance dispersion analysis were performed. As a result, when compared to before and after the global financial crisis, it was shown that the rent price had an effect on income inequality, but in the following period, both the rent price and the selling price affected the income inequality. And that it has a large impact on inequality. In addition, the causality between income inequality and employment rate, interest rate, and tax rate was confirmed. Therefore, it is expected that it will be a desirable policy to mitigate income inequality considering the influence of policy variables for economic activation including government real estate policy.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.469-481
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• 2012

The demand for small-sized housing has been increasing due to the recession of real-estate price and the increase of small-sized households. Especially, the demand for affordable housing has been increasing since the style of housing and the location fits the lifestyle of small-sized household. In addition, many investors have been buying it because it has advertised as an investment property holding high-return ratio. However, an empirical analysis about the selling price and the return ratio has not been done yet. Therefore, the purpose of the research is having the empirical analysis based on the selling price and return ration by examining the affordable housing in Seoul. The urban-life housing more than 50 generations of the Seoul was irradiated for the analysis. And the linear regression analysis and PLS(Partial Least Square Regression) analysis was used for the empirical analysis. The result of analysis, based on the linear regression analysis, showed that factors including neighboring housing price and subway catchment area have a significant effect to the determinant factors of housing price. The analysis for return ratio showed neighboring housing price, subway catchment area and amenities affects the ratio. Especially, the fault of using small sample was covered by using the partial least square regression in this research.

• Korean Journal of Construction Engineering and Management
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• v.13 no.5
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• pp.25-34
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• 2012

It has been five years since the Post-Construction Sale System of Housing was introduced. The purpose of this study is to identify objectives and effects of the Post-Construction Sale System of Housing and analyze change of profitability at different sales time from a supplier's point of view. Apartment buildings construction projects performed in Seoul are used for the case study. The present value of sales revenues, sensitivity and the present value of expected sales prices are analyzed. According to the findings, first, profits made from a Pre-construction sales system was 5.1%~6.2% higher than those from a Post-construction sales system. Among four plans of a Pre-construction sales system (A, B, C and D plan), sales revenue from the A plan, which takes a deposit at the time of starting construction, was the greatest. Second, increase of the rate of discount and decrease of sales revenues are in direct proportion. The bigger rate of discount leads actual reduction of sales revenues. Third, for the present value of sales revenues reflecting change in basic model construction cost, a Pre-construction sales system showed a little higher than that of a Post-construction sales system by approximately 2%. It should be known that this study suggests profitability of Pre-and Post-construction sales system by clearly measuring them in the supplier's point of view and calculates sales revenues, considering change of a sale price following change of sales time.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.23-29
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• 2015

In this study, the spatial relevance between the regional housing price data and the spatial distribution of the location-based social media data is explored. The spatial analysis with rasterization was applied to this study, because the both data have a different form to analyze. The geo-tagged Twitter data had been collected for a month and the regional housing price index about sales and lease were used. The spatial range of both data includes Seoul and the some parts of the metropolitan area. 2,000m grid was constructed to consider the different spatial measure between two data, and they were combined into the constructed grids. The Hotspot Analysis was operated using the combined dataset to see the comparison of spatial distribution, and the bivariate spatial correlation coefficients between two data were measured for the quantitative analysis. The result of this study shows that Seocho-gu area is detected as a common hotspot of tweet and housing sales price index data. though the spatial relevance is not detected between tweet and housing lease price index data.

• Journal of the Korea Institute of Building Construction
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• v.19 no.5
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• pp.439-448
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• 2019

There are various factors affecting the success and failure of an apartment building project. However, after the unit sale price has been determined and the sale has started, the most important factor affecting on the project is the initial sales ratio for one month after the sale. Generally, developers predict an initial sales ratio by various data such as economic situation, the trend of the housing market, and the house price near the business place. However, it is very difficult for these factors to be calculated quantitatively in connection with the initial sales ratio. Therefore, the purpose of this study is to develop a regression model for forecasting the initial sales ratio of apartment building projects. For this study, pre-sales data collection, correlation analysis between influencing factors, and regression model development are performed sequentially. The results of this study are used as basic data for predicting the initial sales ratio in the feasibility analysis of apartment building projects and are used as key data for the development of the risk management 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 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 the Economic Geographical Society of Korea
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• v.17 no.1
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• pp.147-159
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• 2014

The present study was aimed to estimate the rational bubble by using the state space model and Kalman filter, of the national, capital, non-capital, Gangnam, and Gangbuk regions housing sales price from November 2003 to August 2013, for the whole period, and before and after the global financial crisis. For the whole period, Gangnam marked the highest rational bubble of 25.4%, followed by Gangbuk 21.3%, capital region 20.1%, whole country 18.9%, and non-capital region 14.3%. Prior to the global financial crisis, Gangnam showed 26.7% of bubble, which is approximately 7.4% higher than Gangbuk with 19.3%. On the other hand, after the global financial crisis, the bubble has collapsed a lot with Gangnam 13.2% and Gangbuk 10.7%; however, the non-capital region showed rather an increase of about 15% from 4.2% before the crisis to 9.0% after the crisis. The main cause of this is that the trading price has declined but the rents have risen in the capital region including Gangnam and Gangbuk, while the transaction price has gone up in non-capital region due to various positive signs like the moving of public institutions.