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Optimization of house price evaluation model based on multi-source geographic big data and deep neural network.

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Bibliographic Details
Title: Optimization of house price evaluation model based on multi-source geographic big data and deep neural network.
Authors: Wang X; School of Finance and Economics, Shenzhen University of Information Technology, Shenzhen, Guangdong, China., Li X; School of Finance and Economics, Shenzhen University of Information Technology, Shenzhen, Guangdong, China., Li H; School of Finance and Economics, Shenzhen University of Information Technology, Shenzhen, Guangdong, China.
Source: PloS one [PLoS One] 2025 Nov 05; Vol. 20 (11), pp. e0335722. Date of Electronic Publication: 2025 Nov 05 (Print Publication: 2025).
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science
MeSH Terms: Neural Networks, Computer* , Big Data* , Deep Learning* , Housing*/economics , Models, Economic* , Commerce*, Algorithms ; Humans
Abstract: Competing Interests: The authors have declared that no competing interests exist.
The real estate market requires effective and precise house price prediction, as conventional models often face difficulties in generalization, computational efficiency, and interpretability. The research problem is addressed by introducing the House Price Evaluation Model (HPEM), which utilizes a hybrid deep learning network for analyzing multi-source geographic data. The network integrates the attention mechanism with spatial feature extraction, and a bat optimization algorithm is used to improve explainability and accuracy. The gathered properties are processed using normalized techniques to convert unstructured data into structured data, which directly improves the overall prediction accuracy. The bat-optimized attention mechanism with spatial networks dynamically arranges high-impact features to effectively address unstable feature importances, computation inefficiency, and poor generalization issues. In addition, the echolocation-inspired approach explores optimal solutions by balancing exploration and exploitation, thereby minimizing the deviation between the outputs and reducing training time by 30% compared to existing methods. The efficiency of the system is then evaluated using the Housing Price Dataset information, where HPEM achieves 98.5% feature stability, 1.2 hours of human-in-loop updates, and a 4.2% mean absolute error (MAE) under distribution shifts. The effective exploration of dynamic features through bat optimization integration yields 15% closer convergences, enhancing regulatory compliance and accuracy. Therefore, the developed model is effectively utilized in real estate valuation schemes.
(Copyright: © 2025 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
References: PeerJ Comput Sci. 2021 Apr 19;7:e444. (PMID: 33977129)
Entry Date(s): Date Created: 20251105 Date Completed: 20251105 Latest Revision: 20251108
Update Code: 20251108
PubMed Central ID: PMC12588524
DOI: 10.1371/journal.pone.0335722
PMID: 41191568
Database: MEDLINE
Description
Abstract:Competing Interests: The authors have declared that no competing interests exist.<br />The real estate market requires effective and precise house price prediction, as conventional models often face difficulties in generalization, computational efficiency, and interpretability. The research problem is addressed by introducing the House Price Evaluation Model (HPEM), which utilizes a hybrid deep learning network for analyzing multi-source geographic data. The network integrates the attention mechanism with spatial feature extraction, and a bat optimization algorithm is used to improve explainability and accuracy. The gathered properties are processed using normalized techniques to convert unstructured data into structured data, which directly improves the overall prediction accuracy. The bat-optimized attention mechanism with spatial networks dynamically arranges high-impact features to effectively address unstable feature importances, computation inefficiency, and poor generalization issues. In addition, the echolocation-inspired approach explores optimal solutions by balancing exploration and exploitation, thereby minimizing the deviation between the outputs and reducing training time by 30% compared to existing methods. The efficiency of the system is then evaluated using the Housing Price Dataset information, where HPEM achieves 98.5% feature stability, 1.2 hours of human-in-loop updates, and a 4.2% mean absolute error (MAE) under distribution shifts. The effective exploration of dynamic features through bat optimization integration yields 15% closer convergences, enhancing regulatory compliance and accuracy. Therefore, the developed model is effectively utilized in real estate valuation schemes.<br /> (Copyright: © 2025 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
ISSN:1932-6203
DOI:10.1371/journal.pone.0335722