Acknowledgement
This work was supported by INHA UNIVERSITY Research Grant.
International conference on construction engineering and project management (국제학술발표논문집)
- 2015.10a
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- Pages.44-48
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- 2015
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- 2508-9048(eISSN)
Forecasting Housing Demand with Big Data
- Kim, Han Been (Civil Engineering, College of Engineering, INHA UNIVERSITY) ;
- Kim, Seong Do (Civil Engineering, College of Engineering, INHA UNIVERSITY) ;
- Song, Su Jin (Civil Engineering, College of Engineering, INHA UNIVERSITY) ;
- Shin, Do Hyoung (Civil Engineering, College of Engineering, INHA UNIVERSITY)
- Published : 2015.10.11
Abstract
Housing price is a key indicator of housing demand. Actual Transaction Price Index of Apartment (ATPIA) released by Korea Appraisal Board is useful to understand the current level of housing price, but it does not forecast future prices. Big data such as the frequency of internet search queries is more accessible and faster than ever. Forecasting future housing demand through big data will be very helpful in housing market. The objective of this study is to develop a forecasting model of ATPIA as a part of forecasting housing demand. For forecasting, a concept of time shift was applied in the model. As a result, the forecasting model with the time shift of 5 months shows the highest coefficient of determination, thus selected as the optimal model. The mean error rate is 2.95% which is a quite promising result.