Exploration of intelligent housing price forecasting based on the anchoring effect

The investigation of how to accurately predict the sale price of houses is the main objective of our work. Accurate secondhand housing price appraisal is critical in secondhand housing deals, mortgages, and risk assessment. Due to the complex composition of real estate prices, the difficulty of obtaining data and the lack of effective algorithms, the accurate appraisal of housing prices is still a challenge. Based on the hedonic model, the anchoring effect is added to the structure and location characteristics in this work. The 2SFCA algorithm is introduced into the location feature index to filter the influence of the accessibility index. Our model was trained using a variety of machine learning models, such as linear regression and random forest, and the results were evaluated to determine a suitable algorithm for building a secondhand housing transaction price forecasting model. The results showed that the prediction accuracy of the price prediction model could be improved by adding the facility accessibility index, and when the anchoring effect is added to the price prediction model, the prediction accuracy of the model could increase to 0.89. In comparing the results of various machine learning algorithms, we found that the ETR, RFR, and GBR models had better prediction results, and the accuracy rate could reach 0.9. In the end, a case study in Shenzhen was utilized to show that our proposed framework for predicting the price of secondhand houses, which integrated behavioral economics, hedonic price theory, and machine learning algorithms, was practical and efficient and can effectively improve the efficiency and accuracy of the evaluation.