Research And System Implementation Of Intelligent Automatic Evaluation Model For Real Estate Based On Machine Learning

As the core of real estate valuation,the automatic evaluation model should have the following characteristics:1)high data quality:the data determines the upper limit of the model,and the comparable case requirements for real estate evaluation are credible;2)the selection and quantification of features are reasonable:affecting the real estate value The selection and quantification of the characteristic factors need to meet the characteristics of China's real estate market data;3)The model accuracy is high:the price of the property is affected by many factors,but the price needs to be accurately evaluated.However,in the existing research,there is still a lack of research on the quality improvement of real estate data,the characteristic factor system that affects the price of the property is not perfect,and the model research is limited to the validity of the verification model.In view of the above problems,the specific research contents of this paper are as follows:1)The repeated record recognition model based on multi-similarity estimator:solves the problem of serious duplicate listings of property record information extracted from different houses;2)Based on house price characteristics Feature extraction and quantification methods of factor system:Solving the problem that the domestic housing price characteristic factor system is not perfect,the subjective factors of the appraisers affect the valuation accuracy;3)The automatic evaluation model based on the multi-level model fusion:solving the multiple factors due to the price of the house The impact of the real estate division is difficult to reasonably divide,resulting in complex model modeling and low precision.Based on the above research content,based on the existing reptile and address management capabilities,this paper implements a high-accuracy,land-based real estate batch evaluation system.The valuation accuracy index MAPE reaches 9.38%,including the following modules:1)Listing duplicate identification module:realized based on multiple similarities The estimator's listing duplicate record recognition model encapsulation and interface call;2)feature engineering module:realizes feature extraction and quantification based on the house price characteristic factor system,and can pre-process features according to configuration;3)automatic evaluation module:Encapsulation and interface calls for automated evaluation models based on multi-level model fusion.4)Model iteration module:Implement the model periodic iterative update mechanism to solve the problem that house prices are affected by time factors.