Empirical Study on Real Estate Mass Appraisal Based on Dynamic Neural Networks

Real estate mass appraisal is increasingly gaining popularity as a critical issue, reflecting its growing importance and widespread adoption in economic spheres. And data-driven machine learning methods have made new contributions to enhancing the accuracy and intelligence level of mass appraisal. T...

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Bibliographic Details
Published in:Buildings (Basel) 2024-07, Vol.14 (7), p.2199
Main Authors: Chen, Chao, Ma, Xinsheng, Zhang, Xiaojia
Format: Article
Language:English
Subjects:
Accuracy
adaptive reasoning
Algorithms
Artificial intelligence
Data collection
Data processing
Datasets
dynamic neural network
hedonic price
Machine learning
mass appraisal
Network reliability
Neural networks
Parameter identification
Property taxes
Real estate
Real estate appraisal
Real property
Regression analysis
Research methodology
Valuation
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Summary:Real estate mass appraisal is increasingly gaining popularity as a critical issue, reflecting its growing importance and widespread adoption in economic spheres. And data-driven machine learning methods have made new contributions to enhancing the accuracy and intelligence level of mass appraisal. This study employs python web scraping technology to collect raw data on second-hand house transactions spanning from January 2015 to June 2023 in China. Through a series of data processing procedures, including feature indicator acquisition, the removal of irrelevant sample cases, feature indicator quantification, the handling of missing and outlier values, and normalization, a dataset suitable for direct use by mass appraisal models is constructed. A dynamic neural network model composed of three cascaded sub-models is designed, and the optimal parameter combination for model training is identified using grid searching. The appraisal results demonstrate the reliability of the dynamic neural network model proposed in this study, which is applicable to real estate mass appraisal. A comparison with the common methods indicates that the proposed model exhibits a superior performance in real estate mass appraisal.
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings14072199