딥 러닝을 이용한 주택가격 예측에 관한 연구

• 영문명: A Study on Prediction of Housing Price Using Deep Learning
• 발행기관: 한국주거환경학회
• 저자명: 전해정(Chun, Hae Jung) 양혜선()
• 간행물 정보: 『주거환경(한국주거환경학회논문집)』住居環境 제17권 제2호 (통권 제44호), 37~49쪽, 전체 13쪽
• 주제분류: 사회과학 > 지역개발
• 파일형태: PDF
• 발행일자: 2019.07.30

영문 초록

The purpose of this study is to estimate housing prices using deep running. The simple RNN, LSTM, and GRU models, which are evaluated to be suitable for time series forecasting, are based on the time series data of apartment real price index, interest rate, household loan, building permit area and consumer price index. As a result of the empirical analysis, it is confirmed that the prediction power of the GRU model is superior to that of the learning data by evaluating the performance of forecasting power on apartment real price index based on the RMSE value. On the other hand, in the verification data, it is confirmed that the prediction power of the RNN model is excellent. Also, if the performance of the deep running model is evaluated with accuracy, the accuracy of the RNN model and the GRU model is the highest. As a result of this study, the government needs to build and develop a system that can predict and diagnose the housing market by using the deep learning technique that combines artificial neural network and big data to advance the housing market.

목차

Ⅰ. 서 론

Ⅱ. 선행연구 고찰

Ⅲ. 분석모형

Ⅳ. 실증 분석

Ⅴ. 결 론

키워드

주택매매가격 비선형 예측 딥 러닝 순환신경망(RNN) Housing Price Nonlinear Prediction Deep Learning Recurrent Neural Network