Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Development of Forecasting Model for the Initial Sale of Apartment Using Data Mining: The Case of Unsold Apartment Complex in Wirye New Town

데이터 마이닝을 이용한 아파트 초기계약 예측모형 개발: 위례 신도시 미분양 아파트 단지를 사례로

  • Received : 2018.10.23
  • Accepted : 2018.12.20
  • Published : 2018.12.28
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Abstract

This paper aims at applying the data mining such as decision tree, neural network, and logistic regression to an unsold apartment complex in Wirye new town and developing the model forecasting the result of initial sale contract by house unit. Raw data are divided into training data and test data. The order of predictability in training data is neural network, decision tree, and logistic regression. On the contrary, the results of test data show that logistic regression is the best model. This means that logistic regression has more data adaptability than neural network which is developed as the model optimized for training data. Determinants of initial sale are the location of floor, direction, the location of unit, the proximity of electricity and generator room, subscriber's residential region and the type of subscription. This suggests that using two models together is more effective in exploring determinants of initial sales. This paper contributes to the development of convergence field by expanding the scope of data mining.

이 연구에서는 미분양 아파트 단지의 세대별 계약 자료에 데이터 마이닝 기법인 의사결정나무, 신경망, 로지스틱 모형을 적용하여 세대별 초기계약을 예측하는 모형을 개발한다. 모형 개발에는 위례신도시 미분양 아파트 단지의 계약 자료가 이용되며, 이 자료는 훈련용 자료와 검정용 자료로 분할되어 분석에 투입된다. 훈련용 자료에서는 신경망, 의사결정나무, 로지스틱 모형 순으로 예측력이 뛰어났지만 검정용 자료에서는 로지스틱 모형이 가장 우수하게 나타났다. 이 같은 결과는 신경망이 훈련용 자료에 최적화된 모형으로 구축되면서 검정용 자료에 대한 적응성이 떨어져 나타난 결과로 판단된다. 의사결정나무와 로지스틱 모형을 병행 적용한 결과, 층수, 향, 세대 위치, 전기 및 발전기실의 소음, 청약자 거주지, 청약 종류가 초기계약에 영향을 주는 것으로 나타났다. 이는 두 가지 모형을 같이 사용하는 것이 초기계약 결정요인 발굴에 더 효과적이라는 것을 의미한다. 이 연구는 데이터 마이닝의 적용 범위를 주택 분양 예측까지 확장함으로써 융복합 분야 발전에 기여하고 있다.

Keywords

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Fig. 1. SAS E-Miner flow chart

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Fig. 2. Results of decision tree analysis

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Fig. 3. Structure of multi-layered neural network

Table 1. Composition of subscription

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Table 2. Contract rate of initial sale

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Table 3. Variables for data mining

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Table 4. Statistics of decision tree analysis

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Table 5. Classification rate of decision tree analysis

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Table 6. RMSE by the number of hidden nodes

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Table 7. Statistics of neural network analysis

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Table 8. Classification rate of Neural network analysis

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Table 11. Results of logistic regression analysis

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Table 9. Statistics of logistic regression analysis

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Table 10. Classification rate of logistic regression

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Table 12. Comparison of final models

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Table 13. Comparison of determinants by model

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