• Smart Media Journal
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• v.11 no.3
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• pp.62-73
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• 2022

Researches on the prediction of domestic apartment sales price have been continuously conducted, but it is not easy to accurately predict apartment prices because various characteristics are compounded. Prior to predicting apartment sales price, the analysis of major factors, influencing on sale prices, is of paramount importance to improve the accuracy of sales price. Therefore, this study aims to analyze what are the factors that affect the apartment sales price in Gwangju, which is currently showing a steady increase rate. With 6 years of Gwangju apartment transaction price and various social factor data, several maching learning techniques such as multiple regression analysis, random forest, and deep artificial neural network algorithms are applied to identify major factors in each model. The performances of each model are compared with RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and R² (coefficient of determination). The experiment shows that several factors such as 'contract year', 'applicable area', 'certificate of deposit', 'mortgage rate', 'leading index', 'producer price index', 'coincident composite index' are analyzed as main factors, affecting the sales price.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.5-20
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• 2018

This paper aims to analyze characteristic by the cities focused on the ratio of new apartment resale that is one of the apartment unit sale market, which has been increased recently. So, this study examined characteristics of population, apartment trade & sale, housing with 162 cities and counties and performed multiple regression analysis with dependent variable, ratio of new apartment resale. As a result. the factors affecting the ratio of new apartment resale are 7variables, apartment sales rate, transfer of ownership, apartment turnover rate, sale volume, regional apartment rate, population increasing rate, housing average apartment sale price rate. In terms of the increase in apartment sales prices, the rate of sales price increase was relatively low in areas where the transaction rate for apartment sales is high, and the number of apartment sales right transactions increased as the number of other ownership transfers rose. As a result, the data will be based on the improvement of the government's policies and systems to stimulate the transaction focused on the real estate agents in the apartment market.

• Korea journal of population studies
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• v.8 no.1
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• pp.65-86
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• 1985

Korean population is ecpected to reach about 50 million by year 2000. And per capita GNP might attain the $5,000 level. This is bound to have profound impact on housing. For one thing, population and income growth will accelerate new household formation thus increasing new housing needs. On the other, changes in the housing preference function in association with income growth and new way of life would mean increasing demand for better dwelling environment. In addition, by year 2000, there will be many more elderly households necessitating new approaches to housing. The question is whether or not Korea could cope with new housing perspectives. If Korean housing has made in the past some progress in housing quality, it has not been able to tackle the mounting housing shortage. This is attributable to the concentration of effective housing demand in the hands of upper income groups in association with skewed income distribution and sustained dwelling price hike. Korea needs some basic changes in housing policy. The public sector should produce much more small dwellings either for sales or renting. Second, mortgage loans should be expanded so as to increase the access to housing. Third, every thing must be done to cut down the dwelling price through tax cut, relaxation of some requlations, cyclical stabilization of dwelling construction and loan subsidies.

• Korea Real Estate Review
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• v.24 no.1
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• pp.15-26
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• 2014

Using multivariate GARCH model, we estimate the relationship between the housing sale prices and lease prices in the Korean housing market. In the analysis of relationship between the rate of changes in sale and lease prices, the correlation coefficient of the apartment and detached house is higher than that of the townhouse. By housing type, the correlation coefficient between detached house and townhouse is higher than between apartment and detached house or apartment and townhouse. By housing size, there are no significant different results between the sales price and the rental price. The correlation coefficient between medium and small size is the highest in the apartment housing market, whereas the correlation coefficient between large and medium size is the highest in the detached housing market, resulting from the fact that people may be more interested in medium- and small-sized apartment and large- and medium-sized detached house. In the detached housing market, the correlation coefficient between large-medium size and medium-small size in the rental price is higher than that of sales price. This result implies that the process of the decision making between purchasing and leasing a house might be different.

• 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.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.193-201
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• 2019

The public's interest regarding multi-household houses, one of the small-scale housings used as profit earning property, has been increasing. Previous studies regarding price, such as the rent and sales price of multi-household houses', however, were difficult to find. Thus, this study set forth to find out what characteristics influence the sales price of multi-household houses so as to provide further suggestions to investors' decision makings and developers' strategy establishments. The data was retrieved from multi-household sales transacted in Changwon City. Through empirical analysis, this paper found that prices were high in Euichang-gu and Seongsan-gu, and meaningful variables in terms of locations were distance from major trade areas(-), distance from main streets(-), and Corner site(+). Meaningful variables related to household characteristics were total floor area(+), Studio type(+), Southern exposure(+), Building age(-), and Full-furnished(+).

• Journal of the Korean Home Economics Association
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• v.35 no.2
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• pp.95-110
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• 1997

The purpose of this study were 1) to identity housing fund by the type of housing mobility and 2) to analyze the variable contributing to housing fund by the type of housing mobility. For these purposes, the 1993 KHPSD data was used and the sample in this study consisted of 2,796 couple households. Statistics employed for the analysis were frequencies, means, univariate analysis and multiple regression analysis. As the results, the composition and the amount of housing fund according to the type of housing mobility, were different respectively. Housing fund was consisted of previous housing sales price, savings deposits, loans, inheritance, subsidy, and personal debts. Households who already own houses used housing finance for their housing fund easily while renters were at a disadvantage to use housing finance. Moreover, among the contributing factors, home ownership, number of family member, residence, average monthly income, average monthly expenditure, husband's education attainment, satisfaction with housing, husband's job, and the type fo housing were positively associated with the amounts of housing funds. However, duration fo residence tended to negatively related to the amounts of housing funds.

• Korean Journal of Construction Engineering and Management
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• v.13 no.2
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• pp.128-136
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• 2012

In this study, we investigated the macroeconomic variables that affect housing prices thus creating a large impact on people's lives as well as the real estate market. For the study, the macroeconomic variables able to influence the House Rental Price (housing price by lease or deposit) were used for an analysis as follows: housing sales price index, household loans rate, total household savings, the number of employees and a multiple regression analysis was performed using a time series for each macroeconomic variable. As a result of the analysis, the House Rental Price was affected by all of four macroeconomic variables. The House Rental Price increased as each variable enlarged. In conclusion, this study may be useful for finding a solution for stabilizing the House Rental Price as well as for the establishment of efficient and sustainable policies for the housing market.

• Spatial Information Research
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• v.10 no.2
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• pp.215-232
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• 2002

Variables representing neighborhood quality should be included in hedonic price models to control lfor the influences of negative or positive externalities from the quality of neighborhood on urban housing prices. This study proposes a GIS-based method to effectively measure the neighborhood quality variable when data on the neighborhood quality are aggregated by census sub area. This study also tests the superiority of the proposed neighborhood quality variable created by intensive use of GIS operations to a neighborhood variable not based on GIS operations in explaining the housing price variations by using Seoul＇s apartment sales data. The results from this study show that the neighborhood quality variable based on GIS-based operations shows better performance in explaining the urban housing price variations in Seoul＇s housing market. The implication from the results is that the potentials of GIS-based spatial operations in creating neighborhood quality variables should be well acknowledged by the researchers in the area of urban housing market study and GIS-based spatial operations should be more actively applied to generate better neighborhood quality variables for hedonic price models.

• 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.