Economic Statistics Issues under Big Data——Taking China as an Example
- 10.2991/978-94-6463-056-5_8How to use a DOI?
- house prices; population; multicollinearity; regression; population structure
China’s economy has increased dramatically over the past decades. Along with the development of economy, the house prices also increased sharply. Thus, it is necessary to investigate factors contributing to the high house prices. Existing literature demonstrates that GDP, salary, total retail sales of consumer goods and population strongly correlated with increasing house prices. However, there is a FU study that investigated the relationship between house prices and those factors in China. We contacted the study to fill the gap.
This study used a quantitative method to analyze the relationships. Specifically, descriptive statistics and regression models Boo request other house prices. The data was selected from 10 large cities in China from 2011 to 2019. This paper finds that there is a strong relationship between house prices and GDP, house prices and total retail sales of consumer goods and house prices and average yearly salary. However, contrasting with past research, the result shows a negative relationship between house prices and the total number of populations. The limitation of the study includes small sample size end problems of multicollinearity. Further research can be conducted using other factors such as fertility rate as the explanatory variable.
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Cite this article
TY - CONF AU - Yining Yang PY - 2022 DA - 2022/12/29 TI - Economic Statistics Issues under Big Data——Taking China as an Example BT - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022) PB - Atlantis Press SP - 40 EP - 52 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-056-5_8 DO - 10.2991/978-94-6463-056-5_8 ID - Yang2022 ER -