Earticle

초록

영어

eal estate using the text mining technique. As a result of analyzing the first stage–regression model for the target period of five years, road width and the walking distance to the subway station were significant among the location factors. For the second step, text mining analysis was conducted. Through the analysis, it was found that the distribution and importance of major keywords were different before and after COVID-19. In the last three-stage model, Big Data by period, which is the result of the two-stage text mining analysis, was used as an independent variable to analyze the effect on the price of income-producing real estate traded for each period. As a result of the analysis, it was found that unstructured information such as online searches, reviews, and comments shows consumption trends and affects prices of income-producing real estate as well as industries. The forementioned findings could provide implications for effective investment strategies in the future reflecting on the factors affecting the price of income-producing real estate in the major trade area.

목차

ABSTRACT
Ⅰ. 서론
1. 연구의 배경 및 목적
2. 연구의 범위 및 방법
Ⅱ. 이론적배경과 선행연구
1. 상권의 개념과 상권분석기법
2. 빅데이터와 텍스트마이닝의 활용
3. 선행연구와 본 연구의 차별성
Ⅲ. 강남역 상권과 수익형부동산 거래현황
Ⅳ. 실증분석
1. 1단계회귀모형분석
2. 텍스트마이닝분석
3. 기간빅데이터활용 회귀분석
Ⅴ. 결론
<참고문헌>

키워드

저자

• 최정환 [ Choi, Jung Hwan | 정회원, 단국대학교 부동산학 박사 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  토지와건물 [THE JOURNAL OF REAL ESTATE]

• 간기

  연간

• pISSN

  2383-8256

• 수록기간

  1985~2025

• 십진분류

  KDC 321 DDC 333