• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.585-594
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• 2020

After the launch of Jeju Special Self-Governing Province in 2006, a number of real estate development projects were carried out; the real estate industry has relatively largely grown as compared with other regions since 2011. This growth has slowed down in 2017, along with the increase of unsold houses and the short of its incoming population, causing its real estate market recession. This study analyzed the source of real estate industry growth in Jeju: either affected by national growth power or its regional competitiveness. This study applied Shift-Share Analysis and Growth Differential Analysis, by dividing the recovery period ('06~'10) and expansion period ('11~'16). According to the result, sales amount and the number of employees in the real estate industry in Jeju had grown in the recovery period based on the national growth power. Its regional competitiveness and stable industrial structure grew in the growth period. Development and subdividing of real estate contributed to the growth of the real estate industry in Jeju. On the other hand, management of real estate weakened its market.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Korean Journal of Construction Engineering and Management
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• v.11 no.1
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• pp.101-112
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• 2010

The Korean real estate market currently is experiencing a slowdown due to the global economic crisis which has resulted from subprime mortgage lending practices. In response, the Korean government has enforced various policies, based on intend to deregulate real estate speculation, such as increasing the Loan to value ratio (LTV) in order to stimulate housing supply, demand and accompanying housing transactions. However, these policies have appeared to result in deep confusion in the Korean housing market. Furthermore, analyses for housing market forecasting particularly those which examine the impact of the international financial crisis on the Korean real estate market have been partial and fragmentary. Therefore, a comprehensive and systematical approach is required to analyze the real estate financial market and the causal nexus between market determining factors. Thus, with an integrated perspective and applying a system dynamics methodology, this paper proposes Korean Real Estate and Mortgage Market dynamics models based on the fundamental principles of housing markets, which are determined by supply and demand. As well, the potential effects of the Korean government's deregulation policies are considered by focusing on the main factor of these policies: the mortgage loan.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.119-124
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• 2023

This study analyzed the impact of the real estate changes on the unemployment rate in Korea. Using monthly data from January 2013 to February 2023, the study employed a multiple regression analysis model. The key findings are as follows: First, there was a significant causal relationship between the real estate changes and the unemployment rate. Specifically, an increase in the real estate market led to a significant decrease in the unemployment rate, while a decrease in the real estate market resulted in a significant increase in the unemployment rate. Second, an increase in the loan interest rate was found to significantly reduce the unemployment rate, while a rise in interest rates had positive effects on the employment. Furthermore, an increase in inflation was associated with a significant rise in the unemployment rate. Moreover, an increase in the number of permits issued for housing construction significantly reduced the unemployment rate. Lastly, conducting robustness tests by substituting variables did not significantly alter the analysis results, indicating the robustness of the impact of the real estate changes on the unemployment rate. Based on the above analysis, it can be inferred that the fluctuations in real estate prices in South Korea are linked to fluctuations in the unemployment rate, and stable management of the real estate market may contribute to the stability of the unemployment rate.

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.162-170
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• 2006

Neural network analysis is expected to enhance the forecasting ability for the real estate market. This paper reviews definition, structure, strengths and weaknesses of neural network analysis, and verifies the applicability of neural network analysis for the real estate market. Neural network analysis is compared with regression analysis using the same sample data. The analyses model the macroeconomic parameters that influence the sales price of apartments. The results show that neural network analysis provides better forecasting accuracy than regression analysis does, what confirms the applicability of neural network analysis for the real estate market.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.59-70
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• 2013

As people invest most of their asset in real estate, there is high interest in changing in housing and real estate prices in the future for a digital economy. Various variables are affecting the housing and real estate market. Among them, four variables : households, productive population, interest rate and index price are chosen and analyzed representatively. This study is aimed to build decision model of apartment prices in Seoul empirically. From the analysis result the stock index is the only variable which is significant statistically to apartments in Seoul. From this study, the households and productive population show the same direction as shown in the previous studies before but not significant statistically. Among the independent variables, the stock index is chosen as a major variable of determinant of Seoul apartment price. From the result of the research, prediction of stock market should be preceded to forecast the movement of housing and real estate market in the future.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.159-164
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• 2021

The COVID-19 pandemic has widely spread and has become a global problem. The pandemic has had a negative impact on most countries and on the global economic growth. In the real estate and housing market, the impact of the pandemic has directly disrupted the supply of raw materials and human resources. In case of Vietnam, the real estate and housing markets are increasingly becoming important contributors to Vietnam's economy, with a combined contribution of approximately 6% to the GDP of the country. Also, the pandemic has negatively affected the real estate in Vietnam. Using a sample data of 220 home, apartment and real estate buyers in the period of April 2020 to Apr 2021 in Nam Tu Liem and Cau Giay districts, Hanoi, the research results demonstrate that the area of the house, the number of beds, and the location of the land show a positive influence on the real estate price. Meanwhile, the distance from the land to the center of the district has a negative effect on the price, which means that the further away a land is from the center, lower is its price.

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.246-255
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• 2008

The purpose of this study is to suggest the way of effective management of real estate brokerage business by establishing the Job Model in real estate brokerage business in Korea. The AHP method, designed for professionals in a field, is used to establish the Job Model. The main result and implication of the study are as follows. The study found that real estate brokers recognize the importance of getting knowledge and information about the patterns and changes of the real estate market and economy, for the effective management of real estate brokerage business. The study therefore suggest that it is necessary to provide the brokers with education chance for the knowledge about the real estate market and economy.

• Asian Journal of Business Environment
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• v.12 no.1
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• pp.25-33
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• 2022

Purpose: The purpose of this paper is to determine the effects of the Real Estate Transactions Tax (RETT) on the economic cycles of Saudi Arabia. A secondary purpose is to determine the effects of RETT on the construction and real estate sectors of Saudi Arabia. Research design, data and methodology: The data used is retrieved from the General Authority of Statistics, Saudi Central Bank and the World Bank Open Data. Econometric models of multiple linear regression with dummy variables have been conducted to achieve the objectives and to quantitatively verify the hypotheses. Results: With the VAT exemption in real estate transactions and its substitution with RETT, a positive effect on the economy and the real estate sector has been observed. However, this tax reform has not produced any significant effects in the construction sector. Conclusions: The main conclusion of the present research is that the real estate market has a major influence on economic cycles. After the tax reform, a reduction in the contribution of taxes on real estate transactions to GDP was detected. For the construction sector, after the tax reform, it is estimated that there will be an insignificant reduction in the contribution of the real estate price index, and of the taxes on real estate transactions, to GDP.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.499-506
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• 2017

Consideration of influential factors through division of capital market sector and interest rate sector to find and resolve the problems in current housing market and leasing market will become an important index to prepare measures for stabilization of housing sales market and housing lease market. Furthermore, a guideline will be provide you with preliminary data using Big Data to prepare for sudden price fluctuation because expected economic crisis, stock market situation, and uncertain future financial crisis can be predicted which may help anticipate real estate price index such as housing sales price index and housing lease price index.