Applying multiple linear regression and quadratic curve fitting to predict Beijing's housing prices

Peixi Cheng; Shenyu Li; Junhao Qi; Hongjin Song

doi:10.1117/12.2628106

22 April 2022 Applying multiple linear regression and quadratic curve fitting to predict Beijing's housing prices

Peixi Cheng, Shenyu Li, Junhao Qi, Hongjin Song

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 1216307 (2022) https://doi.org/10.1117/12.2628106
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

This paper constructs a statistical model that could predict housing prices in Beijing. With the fast development of China's society, the real estate market has shown an incredible growth rate. For Beijing, as the capital of China, the housing market would be the most specific example for research. Thus, we gathered about 20 years of housing data from the official site to construct a statistical model to predict housing prices. Furthermore, we use predicted data compared with real data. Since China has strong central control, the market is most affected by policies, and we cannot quantify it. However, we chose several factors that could be put into the model, including income, GDP, population, consumption level, and GDP from construction. These factors are considered the most important elements that will affect housing prices and are the factors we use in our prediction model. In the end, we make the predictions for values in 2019 and 2020, and the predicted price has little difference from the actual data, which could demonstrate our model's accuracy. However, our model is built ideally, and for real-word prediction it needs to consider more unpredictable factors, such as central policies, social change, and so on.

Citation Download Citation

Peixi Cheng, Shenyu Li, Junhao Qi, and Hongjin Song "Applying multiple linear regression and quadratic curve fitting to predict Beijing's housing prices", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 1216307 (22 April 2022); https://doi.org/10.1117/12.2628106

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