• Management & Information Systems Review
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• v.20
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• pp.17-32
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• 2007

We try to study the plan to deliver the message of the hope and common peoples are diligent and we can buy the real estate in work if we make efforts hard that relieve an real estate price and analyze a timex situation. If prepared the countermeasure in the government with many real estate policy with due to a short though countermeasure which is seen at one's face. The error to the people of the policy which does not do the staring gaze to tie. This paper to pursue the stability of an real estate price and analyze the price according to an real estate policy and lead an real estate policy for a residing stability of the common people. There are we even though we grope the method to actualize and protect a lease security according to a house lease law of protection.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.187-190
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• 2006

We proposed a new real estate price appra isal model that can appreciate the efficiencies of each criteria that would affect the price. The proposed Real Estate Price Appraisal Model is developed by the DEA-AR model which enhances the DEA-CCR model. We used the unit-cost per criteria method to set the assurance region of each weights of the DEA-AR model. In order to estimate the unit cost of major criteria effecting the price of real estate, we used the Goal Programming so that the price of real estate reaches the actual price being traded in. We expect that this approach could be helpful to make an objective real estate price appraisal.

• 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.

• International Area Studies Review
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• v.15 no.1
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• pp.219-240
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• 2011

The main purpose of this study is to analyze the effects of stock price and real estate price on the money demand. We investigated the demand for money for 25 money units of 10 countries. To estimate the money demand functions, Johansen's cointegration and ARDL-bounds test were employed. Additionally, Stock and Watson's DOLS method was applied to estimate long-run cointegration vectors. According to the results of cointegration test, stock price and real estate price are crucial in the long-run equilibrium relationship. There were no cointegration relationships among money demand, real income, interest rate, and exchange rate in 12 money unit models. However, by including stock price and real estate price on the tested models, we could find strong cointegration relationships, using ARDL-bounds test. The results of DOLS confirm that stock price and real estate price are effective factors influencing on money demands. Especially, the coefficient of real estate price is statistically significant in the 19 out of 20 money unit models. However, the direction and magnitude of coefficients of asset prices are different across countries and money units.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.199-205
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• 2020

As many government policies have been announced today regarding real estate, especially housing, interest in prices in the housing market has increased significantly. In this study, I would like to present the direction of government policies by analyzing the relationship among income instability, the psychological condition of real estate price changes and willingness to adjust real estate holding ratio. First, major variables were extracted through the prior study review, and using a survey, data were collected and path analysis was conducted. According to the analysis, the current income instability had a negative impact on the psychological condition of real estate price changes, and a positive influence on the willingness to adjust real estate holding ratio, but the psychological condition of real estate price changes did not have a statistically significant impact on the willingness to adjust real estate holding ratio. Thus, the difference analysis was conducted between groups by dividing the ages and the number of dependents respectively. According to the analysis, the impact of income instability and psychological condition of real estate price changes on willingness to adjust real estate holding ratio differed between groups divided by ages and number of dependents. The results of this analysis will help the government to establish real estate policies and help each household to use the analysis as basic data when they make a decision about real estate. On the other hand, this study has limitations that have only been conducted cross-sectional analysis and analyzing time series changes and differences in perception between regions are going to be conducted in a future study.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.27-43
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• 2020

Purpose - This study explores the impact of the real estate industry on related industries for the perspective of Chinese steel companies. Design/methodology/approach - The impact of housing prices on the 41 listed steel companies' performance was analyzed by using the panel data model. We used two kinds of housing price indexes that are set in the panel data models to estimate the range of the real estate market, driving the performance growth of steel listed companies. Moreover, the net profit of steel companies is used as the dependent variable. To test the stability of the model, ROA used as a dependent variable for the robustness test. Also, to avoid the time trend of housing prices, this paper selects the growth rate of housing prices as the primary research variable. After Fisher-type testings, there is no unit root problem in both independent and dependent variables. Findings - The results indicated that the rise in the housing price has a positive influence on the steel company performance. When the housing price increases by 1%, the net profit of steel enterprises will increase by 5 to 20 million yuan. Research implications or Originality - In this paper, empirical data at the micro-level and panel model are used to quantify China's real estate industry's driving effect on the iron and steel industry, providing evidence from the microdata level. It helps us to understand further the status and role of China's real estate industry in the economic structure.

• 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 Asian Finance, Economics and Business
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• v.7 no.6
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• pp.517-524
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• 2020

The study examines factors affecting apartment prices in the real estate market of Ho Chi Minh City, Vietnam. The study uses primary data based on surveys of customers who have traded successfully, and collects transaction data from real estate trading companies that are the top investors in Ho Chi Minh City real estate market. The collected data include 384 observations in a total of 24 districts, detailing that each district surveyed on a minimum of four projects, each project carried out a survey on a minimum of four apartments. The survey collected 339 valid questionnaires for analysis and model testing. This study employs multivariate regression with the data of 339 observations. The research results reveal that five significant factors affect positively the price of apartments in Ho Chi Minh City - apartment area, toilet and bedroom, apartment floor, reference price, and apartment interior. Besides, there are three significant factors affecting negatively the price of apartments - next price trend, distance to city center, and potential building. From the results, the research proposes solutions in the pricing of apartments in the real estate market in Ho Chi Minh City - better information system, a real estate transaction index, and stricter management of small brokerage activities.

• 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 Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.