• 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.

• Korea Real Estate Review
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• v.27 no.4
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• pp.51-61
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• 2017

The purpose of this study is to find out the implications for the activation of the real estate industry by comparing the structure of production and distribution, as well as the industrial linkage, of Korean real estate industry with that of the US through an input-output analysis. This study employed the World Input-Output Database (WIOD) provided by the EU in 2016. The results are as follows. First, while the share of the Korean real estate industry in the national economy has been steadily decreasing since the year 2000, the real estate industry of the United States is increasing. Second, both Korean and US real estate businesses have higher value added rates than the industry average, but the intermediate demand rate is lower than the industry average. Furthermore, the intermediate input rate and intermediate demand rate of the Korean real estate industry were lower than that of the US. Third, the change in the final demand for the Korean real estate industry has a lower production and value added effect on the national economy than that of the United States. Fourth, the industrial linkage of the US real estate industry is larger and broader than that of Korea. Finally, it is suggested that a policy to increase the industrial linkage of real estate industry with high value-added industries is needed in order to revitalize Korea's real estate industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.15-20
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• 2020

Recently, the real estate is of high interest. This is because real estate, which was considered only a residential environment in the past, is recognized as a stable investment target due to the ever-growing demand on it. In particular, in the case of the domestic market, despite the decrease in the number of people, the number of single-person households and the influx of people to large cities are accelerating, and real estate prices are rising sharply around the metropolitan area. Therefore, accurately predicting the prospects of the future real estate market becomes a very important issue not only for individual asset management but also for government policy establishment. In this paper, we developed a program to predict future real estate market prices by learning past real estate sales data using machine learning techniques. The data on the market price of real estate provided by the Korea Appraisal Board and the Ministry of Land, Infrastructure and Transport were used, and the average sales price forecast for 2022 by region is presented. The developed program is publicly available so that it could be used in various forms.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.113-120
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• 2017

The purpose of this study is to analyze how the relative proportion of retention between real estate for business and investment real estate among the real estate held by corporations has been changed after and before the Financial Crisis as well as whether there has been any difference between KOSPI and KOSDAQ listed companies in terms of their share of the real estate. The increasing pattern of real estate owned by KOSDAQ were similar to the KOSPI companies except for investment properties during the Financial Crisis. The proportion of real estate owned by KOSPI had been lower than that of KOSDAQ companies in both investment and business real estate before the Financial Crisis. However, during the period of the Financial Crisis, the proportion of real estate for business held by KOSPI firms was higher than that of KOSDAQ firms. Furthermore, the portion of investment of real estate owned by KOSPI has remained higher than that of KOSDAQ after the Financial Crisis period and the recent period. Based on the results of this analysis, how the relevance of the change of portion between real estate for business and investment real estate affects management performance will be figured out in the future studies.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.535-545
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• 2019

If the real estate auction market indicators are relevant and meaningful, they can be meaningful information to the real estate market in connection with general real estate. The purpose of this study is to examine whether time-supply logic is applied in auction market by identifying time series correlations for the number of auctions, the auction rate, and the auction price rate, which are major indicators of real estate auction market. The real estate types were classified into three categories: residential real estate, land, and commercial real estate. The monthly time series of auctions in the metropolitan real estate were compiled for 96 months. Based on this data, the auction market model for each type was established and the mutual influences between the indicators were analyzed. As a result, the supply and demand indicators, the number of auctions and the auction rate, showed the nature of supply and demand according to the supply and demand logic of the market. However, the correlation was high for residential real estate and relatively low for commercial real estate. the auction rate has a long-term impact on price indicators, especially residential real estate, which is quantitatively explanatory and significant. The three auction-related indicators differ in degree, but there is a correlation, especially for residential real estate, which can be useful information for policy making.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.906-917
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• 2021

This study focused on real estate rental income, which is being interested as a means of preparing for old age in the age of low growth and aging. Rental income is seen to function as a safety net of society at a time when it is necessary to live a difficult old age due to the disconnection of income and the extension of the average life span. Therefore, this study conducted the following study on 1,025 households that own rental real estate nationwide. First, the relationship between the characteristics of the household of the rental real estate owner and the real estate rental income was analyzed, and second, it examined whether there is a difference in rental income between the group that engages in income activities other than rental income and the group that only has rental income without income activities. As a result of the analysis, among the demographic and sociological characteristics, gender and spouse were identified as significant variables in rental income. Among the economic characteristics, income and total debt were found to be significant variables. In the case of income activities, rental income was low, and rental income was high when the total debt was high. However, if interest rates rise and the economic recession is prolonged due to unpredictable causes, the owner may suffer from double-use. In preparation for this, it is necessary to review real estate policy alternatives such as easing the period of real estate holdings.

• Informatization Policy
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• v.26 no.1
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• pp.46-61
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• 2019

The Fourth Industrial Revolution has been revolutionizing industry and education. This paper proposes an education platform, Smart Space EduPlatform (SSEP), for the real estate industry, aimed at educating the basic real estate technology (RETech) for workers in the real estate industry so they can achieve the highest and best use of the real estate in the smart environment. The habitat of SSEP is driven by the donation system ensuring sustainability, various technical functions such as tools for content production and learning participation, and learning behavior frameworks each in form of a learner, a teacher, and a helper. Services of SSEP consist of 17 important RETech lectures under 6 categories-planning and design, decision-making, management, economics, construction, and equipment-and project-based learning (PBL) curriculums. The lectures are provided along with video contents, additional learning materials and learning management service, while teachers' workshops, learner invitation and registration management, curriculum operation services are offered for the PBL curriculums.

• The Journal of Information Systems
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• v.30 no.2
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• pp.147-163
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• 2021

Purpose The study aims to predict real estate prices by utilizing regional characteristics. Since real estate has the characteristic of immobility, the characteristics of a region have a great influence on the price of real estate. In addition, real estate prices are closely related to economic development and are a major concern for policy makers and investors. Accurate house price forecasting is necessary to prepare for the impact of house price fluctuations. To improve the performance of our predictive models, we applied LSTM, a widely used deep learning technique for predicting time series data. Design/methodology/approach This study used time series data on real estate prices provided by the Ministry of Land, Infrastructure and Transport. For time series data preprocessing, HP filters were applied to decompose trends and SOM was used to cluster regions with similar price directions. To build a real estate price prediction model, SVR and LSTM were applied, and the prices of regions classified into similar clusters by SOM were used as input variables. Findings The clustering results showed that the region of the same cluster was geographically close, and it was possible to confirm the characteristics of being classified as the same cluster even if there was a price level and a similar industry group. As a result of predicting real estate prices in 1, 2, and 3 months, LSTM showed better predictive performance than SVR, and LSTM showed better predictive performance in long-term forecasting 3 months later than in 1-month short-term forecasting.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.446-458
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• 2018

The media is not just a means of conveying social reality, but is also a political economic subject that restructures social reality. The perceptions and attitudes of the people who read the news can be influenced by the content and direction of the media. Therefore, it is very important to understand and discuss the characteristics of news coverage produced by media. In the case of issues closely related to economic benefits rather than general socio-economic issues, more objective arguments and confirmation of facts are required. In this study, we tried to understand how real estate policy, which is one of the major political and economic issues of S. Korean society, is covered in the media. After analyzing media coverage, we concluded that it was somewhat unreasonable to look at facts about real estate policy objectively and make realistic alternatives, because the framework and attitudes expressed in the articles varied by newspaper.

• Journal of Distribution Science
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• v.12 no.3
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• pp.65-73
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• 2014

Purpose - Aging and fewer economically active people have challenged the assumption of continuous population increases. A new real estate valuation methodology reflecting changes in population structure is thus needed. Research design, data, and methodology - The relationship between demographic change and changes in real estate prices is analyzed using ordinary least squares (OLS) to estimate the parameters, and a population structure change (PSC)-Binomial Option Model is developed to assess the volatility of the estimated parameters. Results based on Seoul and Shanghai data are compared. Results - Results of the DCF method indicate that investing in Seoul is better than investing in Shanghai, but the binomial option indicates the opposite. The PSC-binomial option model, reflecting changes in population structure, yields higher values (24.6 million won in Seoul and 43.3 million won in Shanghai) than those given by the binomial option model. Conclusions - This study indicates that applying changes in population structure to existing research, such as in the binomial option model, represents a more accurate real estate valuation method. Results demonstrate that the new model is more accurate than existing models such as the DCF or binomial option.