Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Analysis of Real Estate Market Trend Using Text Mining and Big Data

빅데이터와 텍스트마이닝을 이용한 부동산시장 동향분석

  • Chun, Hae-Jung (Department of Global Real Estate, Sangmyung University)
  • 전해정 (상명대학교 경영대학원 글로벌부동산학과)
  • Received : 2019.03.04
  • Accepted : 2019.04.20
  • Published : 2019.04.28
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Abstract

This study is on the trend of real estate market using text mining and big data. The data were collected through internet news posted on Naver from August 2016 to August 2017. As a result of TF-IDF analysis, the frequency was high in the order of housing, sale, household, real estate market, and region. Many words related to policies such as loan, government, countermeasures, and regulations were extracted, and the region - related words appeared the most frequently in Seoul. The combination of the words related to the region showed that the frequencies of 'Seoul - Gangnam', 'Seoul - Metropolitan area', 'Gangnam - reconstruction' and 'Seoul - reconstruction' appeared frequently. It can be seen that the people's interest and expectation about the reconstruction of Gangnam area is high.

본 연구는 빅데이터 분석방법인 텍스트마이닝을 이용한 부동산시장 동향분석에 관한 연구로 자료는 2016년 8월부터 2017년 8월까지의 포털사이트인 네이버에 게시된 인터넷 뉴스를 통해 수집하였다. TF-IDF 분석결과, 주택, 분양, 가구, 시장, 지역 순으로 빈도가 높게 나타났고 대출, 정부, 대책, 규제 등 정책과 관련된 단어들도 많이 추출되었으며 지역관련 단어는 서울의 출현빈도가 가장 많은 것으로 나타났다. 지역과 관련된 단어 조합은 '서울-강남', '서울-수도권', '강남-재건축', '서울-재건축'의 출현빈도가 많은 것으로 나타나 강남지역 재건축에 대한 사람들의 관심과 기대가 높은 것을 알 수 있다.

Keywords

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Fig. 1. Flow Chart of Analysis

DJTJBT_2019_v17n4_49_f0002.png 이미지

Fig. 2. Trend of Real Estate Articles

DJTJBT_2019_v17n4_49_f0003.png 이미지

Fig. 3. Analysis of Word Cloud

Table 1. Frequency of Articles by Media

DJTJBT_2019_v17n4_49_t0001.png 이미지

Table 2. TF-IDF Weights

DJTJBT_2019_v17n4_49_t0002.png 이미지

Table 3. Key Word N-gram

DJTJBT_2019_v17n4_49_t0003.png 이미지

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