• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.506-512
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• 2006

다양한 아파트 특성들을 이용하여 아파트 가격을 추정하고 예측하는 연구 또한 많이 존재하고 있는 실정이다. 그렇지만 이러한 연구들 대부분이 회귀모형에 지나치게 의존하고 있는 실정이다 그러나 회귀모형은 단점보다 장점이 많은 모형이다. 본 연구는 회귀모형을 부정하기보다는 새로운 모형을 도입하여, 회귀모형의 문제점들을 극복하고 회귀모형과 상호보완적인 모형을 도입할 필요성에 의해서 본 연구를 수행한 것이다. 다양한 아파트 특성들에 대하여 신경망모형을 이용하여 아파트 가격을 예측하고, 기존의 회귀모형과 비교하는 것이 본 연구의 주목적이다 또한 회귀모형과 신경망모형의 상호 보완적인 측면을 규명하는 것은 본 연구의 부차적인 목적이 된다 아파트 특성들은 주변에서 쉽게 이용 가능한 데이터를 위주로 하였다. 2004년 6월 기준으로 서울시 송파구와 도봉구의 아파트 매매가격들과 12개의 아파트 특성들을 수집하였다. 아파트 매매가격들 (즉, 매매 하한가, 일반 거래가, 매매 상한가) 을 새로운 측정방법을 이용하여 하나의 매매가격으로 추정하였으며, 대표성을 가지도록 하였다. 신경망모형을 도입하여 아파트 특성들을 이용하여 아파트 가격을 정밀하고 유효하게 예측하고, 기존의 회귀모형들과 비교하는 것은 아파트 가격에 대한 연구 분야에 큰 의미가 있다 하겠다. 그리고 주택에 관한 기존의 연구와 신규 연구에 신경망모형이 활용될 수 있으리라 판단된다.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.220-226
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• 2009

본 연구는 회귀모형을 부정하기보다는 새로운 모형을 도입하여, 회귀모형의 문제점을 극복하고 회귀모형과 상호보완적인 모형을 소개하고자 본 연구를 수행하였다. 현재까지 인공지능 분야에서 널리 이용되어 왔던 신경망모형(Neural Network Model)은 입력변수가 불완전하고 변동폭이 넓은 경우에도 해석이 가능하며, 데이터 수가 적거나 불규칙한 경우라도 사례의 반복학습을 통해 오차를 줄여나가기 때문에, 데이터 수에 민감한 영향을 받는 회귀모형보다 정밀한 산정이 가능하다(박우열, 차정환, 강경인, 2002). 이러한 신경망모형에 아파트 특성들을 도입하여 아파트 가격을 정밀하고 유효하게 예측하는 것은 아파트 가격에 대한 연구 분야에 큰 의미가 있다. 그리고 주택에 관한 기존 연구와 신규 연구에 신경망모형이 활용될 수 있으리라 판단된다.

• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.379-385
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• 2010

본 연구는 회귀모형을 부정하기보다는 새로운 모형을 도입하여, 회귀모형의 문제점을 극복하고 회귀모형과 상호보완적인 모형을 소개하고자 본 연구를 수행하였다. 현재까지 인공지능 분야에서 멀리 이용되어 왔던 신경망모형 (Neural Network Model)은 입력변수가 불완전하고 변동 폭이 넓은 경우에도 해석이 가능하며, 데이터 수가 적거나 불규칙한 경우라도 사례의 반복학습을 통해 오차를 줄여나가기 때문에, 데이터 수에 민감한 영향을 받는 회귀모형보다 정밀한 산정이 가능하다(박우열, 차정환, 강경인, 2002). 이러한 신경망모형에 아파트 특성들을 도입하여 아파트 가격을 정말하고 유효하게 예측하는 것은 아파트 가격에 대한 연구 분야에 큰 의미가 있다. 그리고 주택에 관한 기존 연구와 신규 연구에 신경망모형이 활용될 수 있으리라 판단된다.

• Korea Real Estate Review
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• v.27 no.1
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• pp.17-32
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• 2017

The openness of residential space directly affecting lighting, view, and ventilation leads to the variation of open floor plan type in apartment construction project. This study intends to substantiate the effect to the apartment price by design property of open floor plan based on actual design information of apartment and price. The open floor plan type and associated design property, and actual transaction price of apartment have been considered as variables for analysis by the hedonic price function model and artificial neural networks model. Research findings indicate that the openness affects the price of apartment positively and the three sides open plan is the most preferred with the highest price. This study aims to provide the implication to the developer in planning and design stage of apartment and the purchaser seeking the suitable price by floor plan design.

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.162-170
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• 2006

Neural network analysis is expected to enhance the forecasting ability for the real estate market. This paper reviews definition, structure, strengths and weaknesses of neural network analysis, and verifies the applicability of neural network analysis for the real estate market. Neural network analysis is compared with regression analysis using the same sample data. The analyses model the macroeconomic parameters that influence the sales price of apartments. The results show that neural network analysis provides better forecasting accuracy than regression analysis does, what confirms the applicability of neural network analysis for the real estate market.

• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.74-81
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• 2015

As one of urban infrastructure system, the electricity substation is critical for urban life and industrial activity as the electricity demands get higher than ever. However the substation is generally regarded as unpleasant or dangerous facility, which finally results in the continuous opposition movement by resident due to the belief of unidentified negative effect in apartment prices. Accordingly, as the scientifically objective and quantitative analysis is required to solve the social conflict, this study intends to examine the variation affected by urban infrastructure system, expecially for substation. After the independent variable defining the price of apartment and the dependent variable, which is apartment price, are identified and their spatial data has been filed, the forecasting model has been developed through the hedonic price function as well as artificial neural networks system. The research finding indicated that the spatial range affected by substation is not notable and the range of some case was applicable for less than 600m. It is expected that these research findings can be applied for establishing the one of solid cases for the analysis of economical effect to local housing market by the urban infrastructure system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.228-234
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• 2021

Real estate market participants need to have a sense of predicting real estate prices in decision-making. Commonly used methodologies, such as regression analysis, ARIMA, and VAR, have limitations in predicting the value of an asset, which fluctuates due to unknown variables. Therefore, to mitigate the limitations, an artificial neural was is used to predict the price trend of apartments in Seoul, the hottest real estate market in South Korea. For artificial neural network learning, the learning model is designed with 12 variables, which are divided into macro and micro factors. The study was conducted in three ways: (Ed note: What is the difference between case 1 and 2? Is case 1 micro factors?)CASE1 with macro factors, CASE2 with macro factors, and CASE3 with the combination of both factors. As a result, CASE1 and CASE2 show 87.5% predictive accuracy during the two-year experiment, and CASE3 shows 95.8%. This study defines various factors affecting apartment prices in macro and microscopic terms. The study also proposes an artificial network technique in predicting the price trend of apartments and analyzes its effectiveness. Therefore, it is expected that the recently developed learning technique can be applied to the real estate industry, enabling more efficient decision-making by market participants.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.171-181
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• 2022

The real estate price index plays key roles as quantitative data in real estate market analysis. International organizations including OECD publish the real estate price indexes by country, and the Korea Real Estate Board announces metropolitan-level and municipal-level indexes. However, when the index is set on the smaller spatial unit level than metropolitan and municipal-level, problems occur: missing values. As the spatial scope is narrowed down, there are cases where there are few or no transactions depending on the unit period, which lead index calculation difficult or even impossible. This study suggests a supervised learning-based machine learning model to compensate for missing values that may occur due to no transaction in a specific range and period. The models proposed in our research verify the accuracy of predicting the existing values and missing values.