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A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

Jintao Fu1, Yong Zhou1,*, Qian Qiu2, Guangwei Xu3, Neng Wan3

1 School of Computer Science, Southwest Petroleum University, Chengdu, 610500, China
2 Key Laboratory on Aero-Engine Altitude Simulation Technology, Sichuan Gas Turbine Establishment, AECC, Mianyang, 621000, China
3 Aero Engine Academy of China, Aero Engine Corporation of China, Beijing, 101300, China

* Corresponding Author: Yong Zhou. Email: email

Journal of Quantum Computing 2022, 4(3), 147-163.


In the field of computer research, the increase of data in result of societal progress has been remarkable, and the management of this data and the analysis of linked businesses have grown in popularity. There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices. Using regression methods in machine learning to segment the data set, examine the major factors affecting it, and forecast home prices is the most popular method for examining pricing information. It is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price movement. In today’s forecasting studies, ensemble learning is a very prevalent and well-liked study methodology. The regression integration computation of large housing datasets can use a lot of computer resources as well as computation time, and ensemble learning uses more resources and calls for more machine support in integrating diverse models. The Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models, combining the best benefits of separate models. The Average Model has a strong applicability in the field of regression prediction and significantly increases computational efficiency. The technique is also easier to replicate and very effective in regression investigations. Before using regression processing techniques, this work creates an average of different regression models using the AM (Average Model) algorithm in a novel way. By evaluating essential models with 90% accuracy, this technique significantly increases the accuracy of house price predictions. The experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms, and the prediction accuracy is greatly improved compared with other algorithms, and has a good experimental effect in house price prediction.


Cite This Article

J. Fu, Y. Zhou, Q. Qiu, G. Xu and N. Wan, "A model average algorithm for housing price forecast with evaluation interpretation," Journal of Quantum Computing, vol. 4, no.3, pp. 147–163, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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