• The Journal of Society for e-Business Studies
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• v.14 no.3
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• pp.15-37
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• 2009

This study presents a new retrieval system developed to consider various preferential requirements for buyers in the real estate market. The paper analyses essential factors affecting the price of real estate and then a set of factors are classified by region-related factor and individual-related factor. After endowing the buyer's selected factors with weights in the retrieval system, the optimal solutions have been drawn by comparing with the others through an entropy measure of Multi-attribute Decision Making. This retrieval system is applied to the Busan real estate market to estimate the solutions of retrieval. Evaluation results indicate that the retrieval system can provide useful information to analyse the price determination factors of real estate, as well as to save the searching cost of the buyers.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• Journal of the Economic Geographical Society of Korea
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• v.10 no.3
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• pp.245-262
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• 2007

This paper aims at analyzing the characteristics of performance and asset management system of listed property trusts(LPT) in Australia, and elucidating the implications for Korean real estate indirect investment market. The main results of this paper are as follows. Firstly, LPT have a leading position among the real estate indirect investment systems in Australia, through the rapid growth of market capitalization. Secondly, LPT achieved superior risk adjustment performance than other financial products, and had valid portfolio diversification effect. Thirdly, many LPT have used stapled securities structure as a asset management system, and stapled LPT revealed superior risk adjustment performance than unit LPT. Finally, implications and policy measures such as using the stapled structure and activating the development activities were examined for the development of Korean real estate indirect investment market.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.1
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• pp.181-186
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• 2012

This study military and local authorities to set up military reserves has often caused friction. Therefore, military bases and military facilities around the Act's real estate management practices that can be was to prepare. As a result, the first government to develop a plan to train real estate professionals are actively involved with the Advisory Board shall be utilized to configure. Utilizing the second army of real estate assets for future urban development should be. The third section, research on military protection should be continued. Finally, border management and development plans to propose the measures that should be carried out.

• Journal of KIBIM
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• v.11 no.3
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• pp.12-21
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• 2021

Existing real estate markets are monopolized because they are capital intensive and have information asymmetry. However, with the advent of the Fourth Industrial Revolution, technology is converging in various industries based on information technology (IT), and the real estate market is also developing a new field called "PropTech". According to this trend, new PropTech technologies are emerging in various real estate services sectors in Korea, but the electronic contract system, which accounts for the largest portion of the real estate industry, is still cited as a complex identification process and long processing time. Therefore, in this paper, we propose an improvement plan for the current electronic contract system through the introduction of blockchain technology, which is drawing attention with the development of PropTech, and explore the possibility of introduction by producing an experimental model of blockchain-applied electronic contract system in a programming language.

• The Journal of Information Systems
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• v.7 no.1
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• pp.159-180
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• 1998

User-led IS development contributes to the response of actual user needs, the alternative solution for backlog, and including the enhanced communication and the reduced role conflict between users and DP department. However, there are too little research and practical concern about the user-led IS development. This case study on the Korea Real Estate Investment Trust addresses to the development background, IS development process, and the qualitative and quantitative benefit of user-led IS development. We believe that the case study contributes to the understanding for the benefit of user-led IS development approach.

• 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.

• International Area Studies Review
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• v.15 no.3
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• pp.263-283
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• 2011

As China has achieved continuous high growth, innovative housing system and rapid growth of housing finance for thirty years since its reforming and opening, Chinese real estate industry has developed rapidly, investment in real estate has increased remarkably and price of real estate has increased continuously. In particular, as China joined WTO in 2001, overseas companies have expanded investment in Chinese real estate market with chances of potential profit. Recently, management of foreign capital real estate companies has expanded from housing to office buildings and luxury shops. Besides, management has expanded from real estate development to various tasks such as management, brokerage service, lease management and financial investment. In order to measure relationship between FDI in Chinese real estate and Chinese housing price, this document utilizes not only various data such as housing price, FDI to real estate, domestic loan of real estate, interest rate and exchange rate in 35 major cities across China but also previous researches to set an empirical analysis model and analyze effects of foreigners' direct investment in Chinese real estate on Chinese housing price.

• KDI Journal of Economic Policy
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• v.26 no.1
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• pp.3-33
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• 2004

This paper investigates the effects of inflation on real estate prices, particularly the discrepancy between the sales and chonsei prices of housing, in an economy in which real interest rates are secularly declining due to the fall in capital productivity. When real interest rates fall, real estate prices rise relative to chonsei prices, and thus the well-known adverse effect of inflation, or the discrepancy between the value of financial assets (or chonsei principal) and the value of real assets (or real estate), is aggravated although the monetary authority maintains the same rate of inflation. This theoretical prediction can help explain the trend of the ratio of apartment sales prices to chonsei prices. That is, the stabilization of inflation relative to real interest rates appears to have contributed to the secular stabilization of this ratio in the 1990s, while the fall in real interest rates appears to have led to the rise of this ratio since 2001.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.256-263
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• 2021

This paper studies the problems and improvements of government real estate policies. Moon Jae-in government shifted toward regulation and pledge to curb the tax imposed by speculators. It strengthened regulations on reconstruction and bank loans rather than supply, and raised capital gains taxes. As the government implemented measures, emphasizing political logic rather than the economy, the market is unstable and the economy is in a recession. Land has increased the vicious cycle of problems due to population growth, industrialization, urbanization, and wealth growth. Mis-established land policies not only accelerate land prices, but also accelerate the use of disordered land and lead to disruptions in the trading order. In addition, real estate is so difficult to recover from the land problem that it is difficult to contain water that has been spilled once. This is called the irreversible nature of land. Once the land price rises, it is difficult to regain control and reckless development leads to the destruction of the ecosystem, making it difficult to return. This is why such a complex real estate issue should not be implemented as if it were a punishment in a short period of time with government policies. This paper aims to examine the problems of real estate policies and to examine ways to improve them.