Abstract
The current standardization and legalization of commercial housing price assessment is low. In addition, computer technology is less used in this field. Therefore, the evaluation of commercial housing prices can no longer meet the requirements of today’s economy and society. Traditional real estate assessment methods such as market comparison method, cost estimation method, income reduction method, etc., pay attention to market information, but tend to emphasize the total value of real estate in evaluation practice, and they ignore the impact of various constraints on real estate prices, so it is impossible to scientifically understand the influencing factors of real estate prices and their mechanisms of action, resulting in a lack of scientific basis for decision-making by governments, developers or property users. Therefore, this paper introduces the improved algorithm of BP neural network to establish the housing price evaluation model, which reduces the subjectivity and randomness in the evaluation process. This has certain reference for the development of China’s real estate assessment method. The evaluation results show that the real estate evaluation price of the network output is close to the actual price, with maximum error of 3.04%. This shows that the application of improved BP neural network model in real estate price evaluation is not only technically feasible, but also credible.
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Zhou, X. The usage of artificial intelligence in the commodity house price evaluation model. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-019-01616-4
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DOI: https://doi.org/10.1007/s12652-019-01616-4