• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.203-216
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• 2015

This study focuses on the relationship between housing sale prices and Jeonse prices, amid a recent surge of Jeonse price and Jeonse-to-housing sale price ratio. There are many studies about the relationship between house prices and Jeonse, but they couldn't fully explain what makes them spike up. In addition to this relationship, this paper deals with the difference of Jeonse system on regions and price levels. Using Granger causality and Spearman's Correlation Coefficient, the outcome is drawn. As the result, the expected rate for housing sale prices effects on the Jeonse-to-housing sale price ratio. The higher on sale price, the lower the Jeonse-to-housing sale price ratio regarding the region difference.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.523-531
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• 2008

This study tried to find differences between housing lotting prices and sale prices owing to new multi-family housing price regulation. As the results of this study, they are as follows; First, this study shows housing market in Busan has a preferences of new housing which has a new housing form differing from the existing housing form. For example, the mixed-use apartment with higher stories shows steeper incline than the apartments with the existing forms. Second, the new housing prices are affected by the information that affect the price of the old existing housing. They are rates of green area of an apartment complex, the number of household, accessibility to downtown Busan and etc.. They are also confirmed factors that affect a rise of used-housing price in other studies. Third, brand value of apartments affects new housing prices. For example, if the major construction companies build the new apartment, it shows a rising trend than any other housing. Therefore, the local construction companies are expected to be put on a disadvantage places than major construction companies. Fourth, the lotting prices are the most important cause that lead to rise the new housing prices. Accordingly, the present lotting prices are expected that upward tendency the purchasing prices of the new housing will not continue, because the lotting prices have risen since the government removed lotting price regulations and exceeded the level of used-housing prices. And it denote that importance of housing sub-market which indicates rates of old existing housing market rising, frist preference Gu, second preference Gu, rate of multi-family housing.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.607-612
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• 2021

This study estimated the impact of the shock of housing price increase on the total fertility rate and the contribution of each variable to changes in the TFR. This study is differentiated by estimating the contribution rate of each variable to the fertility rate through the Shapley decomposition and the panel VAR's forecast error variance decomposition, which previous studies have not attempted. The main results of this study are as follows. First, the decline in the TFR in Korea has been strongly influenced by the recent decline in the total fertility rate, and this influence is expected to continue in the future. In the case of housing costs, in the past, housing sales prices had a relatively small contribution to changes in the total fertility rate compared to the jeonse prices, but their influence is expected to increase in the long term in the future. It has been demonstrated that private education expenses other than housing sale price and Jeonse price also acted as a major cause of the decline in the total fertility rate.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.19 no.2
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• pp.96-106
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• 2018

The government adopted activation policy of real estate to overcome low economic growth rate. Real estate activation plan adopted by the government raised credit limit by lowering the regulation, and reduced real estate investment cost by reducing the base rate. Also, delayed transfer tax on multi-house owner to activate real estate investment and resolved purchase right resale. Relief of real estate regulate caused increase of housing sales and price increase, and the real estate market changed to overheating aspect such as premium upon completion of lot sale in a short time. Such market atmosphere greatly increased household debs as owners own houses based on 'financial debt' instead of their income. Since 2017, real estate policy was reinforced to reduce household debts and lending rate was raised due to rise of base rate, accordingly, burden of household debt is expected to increase. This research suggested a plan for the Financial Supervisory Service to efficiently manage the financial world by analyzing the cause and problem of household debs.