Browse > Article
http://dx.doi.org/10.7465/jkdi.2016.27.6.1573

Comparison of the forecasting models with real estate price index  

Lim, Seong Sik (Division of General Education, SeoKyeong University)
Publication Information
Journal of the Korean Data and Information Science Society / v.27, no.6, 2016 , pp. 1573-1583 More about this Journal
Abstract
It is necessary to check mutual correlations between related variables because housing prices are influenced by a lot of variables of the economy both internally and externally. In this paper, employing the Granger causality test, we have validated interrelated relationship between the variables. In addition, there is cointegration associations in the results of the cointegration test between the variables. Therefore, an analysis using a vector error correction model including an error correction term has been attempted. As a result of the empirical comparative analysis of the forecasting performance with ARIMA and VAR models, it is confirmed that the forecasting performance by vector error correction model is superior to those of the former two models.
Keywords
Housing price index; jeonse price index; vector AR model; vector error corrected model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Son, J. S., Kim, K. Y. and Kim, Y. S. (2003). A study on the forecasting model of real estate market : The case of Korea. Housing Studies Review, 11, 49-75.
2 Wei, W. W. S. (1990). Time series analysis, Addison-Wesley, Redwood City, California.
3 Yoon, J. H. and Kim, H. S. (2000). Short-term forecasting model for the housing market, Korea Research Institute for Human Settlements, Anyang.
4 Bang, K. S. (2011). Real estate terms dictionary, Buyonsa, Seoul.
5 Kim, D. W. and Cho, J. H. (2012). An analysis on determinants of apartment jeonse price and jeonse price ratio in Seoul. Housing Studies Review, 20, 183-204.
6 Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1994). Time series analysis forecasting and control, 3rd Ed., Prentice-Hall, Inc., New Jersey.
7 Cho. S. S. and Lee. J. H. (2014). SAS/ETS usage for economic time series analysis, Freeacademy, Seoul.
8 Cho, Y. J. and Kim, Y. H. (2008). Development of forecasting model in tax exemption oil if fisheries using seasonal ARIMA. Journal of the Korean Data & Information Science Society, 19, 1037-1046.
9 Chun, H. J. (2013). The dynamic correlation between chonsei price, house prices, and house lease price to house sale price ratio. Korea Real Estate Academy Review, 53, 189-200.
10 Han, K. S. (2011). The influence of real estate pricing factors on house sales price index in west area of Gangwon-do. Korean Academic Society of Business Administration, 66, 547-565.
11 Kim, K. Y. (1998). Model identification and test for forecasting house price. The Korea Spatial Planning Review, 197, 54-61.
12 Lee, H. W. and Lee, H. B. (2009). Comparative analysis for predictability of housing price index by model in Seoul. Korea Real Estate Academy Review, 38, 215-235.
13 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.   DOI
14 Noh, Y. H. and Kim, G. H. (2012). A study on the impact on real estate policy of housing prices. Korea Real Estate Academy Review, 50, 108-122.
15 Park, I. C., Kwon, O. J. and Kim, T. Y. (2009). KOSPI directivity forecasting by time series model. Journal of the Korean Data & Information Science Society, 20, 991-998.