• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_2
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• pp.315-322
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• 2022

Apartments are being established as a universal housing type because of the perception that they are excellent in preservation of asset values and convenience. In this study, through multiple regression analysis, it is a thesis that explores whether it affects the housing market in Gimhae, a small and medium-sized city in the province, and how the price flow in neighboring cities has an effect. It is possible to examine how macroeconomic variables such as the balloon effect and the lowest interest rate caused by the government's tweezers regulation bring about changes in the housing market of small and medium-sized cities in local regions.

• 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.

• Land and Housing Review
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• v.7 no.4
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• pp.207-215
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• 2016

The purpose of this paper is to explore ways in the construction of Apartment-type factories in the public sector. Recently the markets of Apartment-type factories are driven by the private sector as high quality, high price and supply of in the form of office, these construction trends are different from the original aim of introduce policy of Apartment-type factories which supply production spaces in the city to small capital enterprises. We analyzed the architectural characteristics of Apartment-type factories in Korea, the prospect of their future construction trends and the survey results targeted to small enterprises. In the results of this survey, we grope for the direction for construct Apartment-type factories in the public sector. The Apartment-type factories constructed by the public sector are appropriate supply for manufacturing plants and sale rather than lease form to complement the private market. To optimize economically and functionally for manufacturing activities, the design space in the internal and external spaces is required in order to supply more affordable. Route design, especially considering parking, unloading and warehousing of merchandise, and logistics should be differentiated from the Apartment-type Factories constructed by private sector.

• Land and Housing Review
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• v.7 no.4
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• pp.217-223
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• 2016

PSM is widely used pricing tool in field by the reason of data collection convenience and analytical intuitiveness. However, In high involvement environment, strategic respondent bias influence in reducing the price. By using 3 empirical cases of LH apartment for sale, We found that latent consumers' recognition of the range of acceptable and the range of optimal price are lower than real estate agent representative respondents'. This phenomenon is considered loss aversion effect of prospect theory to reduce loss by reducing price, and more influenced in high involvement situation than latent consumer respondents'. Also we found PSM result using real estate representative data is more useful in real market than latent consumers data distorted by loss aversion effects. The meaning of this study is finding some limitation in PSM using consumer data generally used. In further study, development of PSM measurement tool to minimize the effect of strategic bias are need to be studied. Also some new approaches in reinterpretation of the range of acceptable price and the range of optimal price are need to be followed.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.173-183
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• 2017

We investigate the factors affecting the price of apartments using the spatial and temporal data of private real estate prices. The factors affecting the price of apartment were analyzed using geographical and temporal weighted regression (GTWR) model which incorporates the temporal and spatial variation. In contrast to the OLS, a general approach used in previous studies, and GWR method which is most widely used for analyzing spatial data, GTWR considers both temporal and spatial characteristics of the house price, and leads to better description of the house price determination. Year of construction and floor area are selected as the significant factors from the analysis, and the house price are affected by them temporally and geographically.

• 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.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.23-34
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• 2011

Property auctions have become a new method for real estate investment because the property auction market grows in tandem with the growth of the real estate market. This study focused on the statistical model for predicting bid price rates which is the main index for participants in the real estate auction market. For estimating the monthly bid price rate, we proposed a new method to make up for the mean of regions and terms as well as to reduce the prediction error using a decision tree analysis. We also proposed a linear regression model to predict a bid price rate for individual auction property. We applied the proposed model to apartment auction property and tried to predict the bid price rate as well as categorize individual auction property into an auction grade.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.956-959
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• 2020

본 연구에서는 아파트를 대표할 수 있는 내·외적 데이터를 수집하고 인공지능 기술들을 활용하여 아파트 가격을 예측하는 시스템을 구축하고자 한다. 구체적으로 웹크롤링 기법을 통해 수집한 아파트 내·외적 데이터의 변수들에 대한 특성 선택(Feature Selection)을 수행하였고, 다양한 인공지능 기법을 활용하여 부동산 가격 예측 모형을 개발하였다. 아파트 가격 예측 모형 생성을 위해 Linear Regression, Ridge, Xgboost, Lightgbm, Catboost 등의 기계학습 알고리즘을 사용하였고, RMSE를 사용하여 각 예측 모형 간의 성능 비교를 수행하였다. 가장 성능이 좋은 예측 모형은 Xgboost기반 예측 모형이였으며, RMSE값이 약 0.0366으로 가장 낮았으며 테스트 데이터에 대한 정확도는 약 95.1%였다.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.433-442
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• 2021

This study analyzed the housing market before and after the New Stay movement which was introduced in 2015. In this study, the territories having a New Stay Project and non-involved territories were analyzed based on the apartment price changes according to supply for 12 months before and after the movement date. This study used the difference-in-differences statistical technique. A comparison was carried out in Michuhol-gu, Incheon between Dowha-dong where a New Stay Project was executed, and Sungeui-dong where no project was executed, based on the movement date. It was seen that the price level in the former territory was higher than the latter demonstrating that the introduction of the New Stay Project in Dowha-dong lowered the apartment prices nearby (Sungeui-dong). A comparison in Gwonseon-gu, Suwon between Omogcheon-dong where a New Stay Project was executed and Gosaek-dong where there was no such project, based on the movement date showed that the introduction of the New Stay Project in Omogcheon-dong seemed to lower or stabilize the apartment prices nearby (Gosaek-dong). These results imply that the apartment prices in nearby areas can be stabilized if the supply volume of company-type rental houses is increased.