기계학습 알고리즘을 활용한 지역 별 아파트 실거래가격지수 예측모델 비교: LIME 해석력 검증

Purpose: Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach: In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings: This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.