Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
권호 표지
DOI QR코드

DOI QR Code

아파트매매가격지수와 거시경제변수에 관한 시계열모형 연구

Time series models on trading price index of apartment and some macroeconomic variables

  • 이훈자 (평택대학교 데이터정보학과)
  • Lee, Hoonja (Department of Data Information, Pyeongtaek University)
  • 투고 : 2017.10.11
  • 심사 : 2017.11.20
  • 발행 : 2017.11.30
⟨ 이전 논문 다음 논문 ⟩

초록

아파트매매 가격지수의 변동은 국가의 경제뿐만 아니라 사회, 산업, 문화 등의 전 분야에 영향을 준다. 본 연구에서는 아파트매매 가격지수를 거시경제변수로 설명하는 시계열모형을 연구하고자 한다. 설명변수로 사용한 거시경제변수는 우리나라 주택담보 대출금리, 원유수입 물가지수, 소비자 물가지수, KOSPI 주가지수, 국내총생산 (GDP), 국민총소득 (GKI)의 6가지 변수를 사용하였다. 아파트매매 가격지수와 모든 경제변수는 2001년 9월부터 2017년 5월까지 약 16년간의 월별 자료를 사용하였다. 아파트매매 가격지수 자료의 설명을 위해 시계열 모형 중 자기회귀오차 (ARE) 모형을 사용하여 분석하였다. ARE 모형 분석 결과 아파트매매 가격지수는 1개월 전 아파트매매 가격지수, 주택담보 대출금리와 KOSPI 주가지수에 의해 영향을 받는 것으로 나타났다.

The variability of trade price index of apartment influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly trading price index of apartment data. About 16 years of the monthly data have been used from September 2001 to May 2017. In the ARE model, six macroeconomic variables are used as the explanatory variables for the rade price index of apartment. The six explanatory variables are mortgage rate, oil import price index, consumer price index, KOSPI stock index, GDP, and GNI. The result has shown that trading price index of apartment explained about 76% by the mortgage rate, and KOSPI stock index.

키워드

참고문헌

  1. Bae, Y. K. (2015). Predicting the probability of apartment price increase by using the logit & error correction model. Korea Real Estate Society, 33, 235-253.
  2. Chun, H. and Park, H. (2013). Trend/cycle decomposition in housing sales price. The Seoul Institute, 14, 77-86.
  3. Kim, J. (2015). Cluster analysis for Seoul apartment price using symbolic data. Journal of the Korean Data & Information Science Society, 26, 1239-1247. https://doi.org/10.7465/jkdi.2015.26.6.1239
  4. Kim, J. H. (2014). An anaysis of macroeconomic index influences on the trading and rental price of housing, Thesis of master's degree, department of rental estate, Pusan National University.
  5. Kim, K. and Ahn, J. (2010). Determinant factors for the apartment unit prices of large scale apartment complexes over 1,000 households in Seoul metropolitan area. The Korean Housing Association, 21, 81-90.
  6. Kim, W. and Yeo. J. (2017). An analysis of relationship between trading price index of apartment and other macroeconomic variables using multivariate time series analysis. Economic Study, 35, 1-17.
  7. Lee, H. (2014). Analysis of statistical models on temperature at the Seosan city in Korea. Journal of the Korean Data & Information Science Society, 25, 1293-1300. https://doi.org/10.7465/jkdi.2014.25.6.1293
  8. Lee, W. J. and Park, C. (2015). Prediction of apartment prices per unit in Daegu-Gyeongbuk areas by spatial regression models. Journal of the Korean Data & Information Science Society, 26, 561-568. https://doi.org/10.7465/jkdi.2015.26.3.561
  9. Lee Y. (2015). An analysis of influential factors towards national housing market segmentation and segmental housing price change due to volatility of the housing price in South Korea. Journal of the Korean Economics Studies, 33, 31-60.
  10. Lim, S. S. (2014). A study on the forecasting models using housing price index. Journal of the Korean Data & Information Science Society, 25, 65-76. https://doi.org/10.7465/jkdi.2014.25.1.65
  11. Park, D. W. and Lee, J. H. (2012). An analysis on the determinant of high-tech complex neighborhood rental and transaction price of apartment. Korea Real Estat Society, 30, 255-270.
  12. Park, H. and Kim, S. (2015). A study on interdependency of major group prices in Korea. Journal of Industrial Economics and Business, 28, 1353-1371.
  13. Park, Y. and Kim, K. (2016). Analysis of economic and Financial Data, Freedom Academy, Seoul.