| Title |
Trend Analysis of Apartments Demand based on Big Data |
| Authors |
Kim, Tae-Kyeong ; Kim, Han Soo |
| DOI |
http://dx.doi.org/10.6106/KJCEM.2017.18.6.013 |
| Keywords |
Big Data ; Text Mining ; Apartments Demand ; Trend |
| Abstract |
Apartments are a major type of residence and their number has continuously increased. Apartments have multiple meanings in that for public they are not only for residence purpose but for investment, a major commodity for construction firms and a critical policy measure of public well-fare for the government. Therefore, it is critical to understand and analyze trends in apartments demand for pro-active actions. The objective of the study is to analyze and identify key trends in apartments demand based on big data drawn from articles of major daily newspapers. The study identifies 17 major trends from seven themes including development, trade, sale in lots, location requirements, policy, residential environment, and investment and profit. The research methods in the study can be usefully applied to further studies for various issues in relation to the construciton industry. |