Algorithm steps for house price prediction model.
<div><p>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)...
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2025
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| _version_ | 1852015126446604288 |
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| author | Xuan Wang (55634) |
| author2 | Xuan Li (137217) Haiyan Li (168109) |
| author2_role | author author |
| author_facet | Xuan Wang (55634) Xuan Li (137217) Haiyan Li (168109) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xuan Wang (55634) Xuan Li (137217) Haiyan Li (168109) |
| dc.date.none.fl_str_mv | 2025-11-05T18:29:04Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0335722.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Algorithm_steps_for_house_price_prediction_model_/30544252 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reducing training time mean absolute error enhancing regulatory compliance source geographic data convert unstructured data spatial feature extraction hpem achieves 98 poor generalization issues overall prediction accuracy optimized attention mechanism bat optimization algorithm attention mechanism structured data hpem ), feature stability xlink "> thereby minimizing research problem network integrates loop updates improve explainability impact features gathered properties existing methods evaluated using effectively utilized effective exploration dynamic features distribution shifts directly improves developed model computation inefficiency closer convergences balancing exploration |
| dc.title.none.fl_str_mv | Algorithm steps for house price prediction model. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_26b962e2b8167ce23b63c1e4c19fa804 |
| identifier_str_mv | 10.1371/journal.pone.0335722.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30544252 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Algorithm steps for house price prediction model.Xuan Wang (55634)Xuan Li (137217)Haiyan Li (168109)BiotechnologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedreducing training timemean absolute errorenhancing regulatory compliancesource geographic dataconvert unstructured dataspatial feature extractionhpem achieves 98poor generalization issuesoverall prediction accuracyoptimized attention mechanismbat optimization algorithmattention mechanismstructured datahpem ),feature stabilityxlink ">thereby minimizingresearch problemnetwork integratesloop updatesimprove explainabilityimpact featuresgathered propertiesexisting methodsevaluated usingeffectively utilizedeffective explorationdynamic featuresdistribution shiftsdirectly improvesdeveloped modelcomputation inefficiencycloser convergencesbalancing exploration<div><p>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.</p></div>2025-11-05T18:29:04ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0335722.t001https://figshare.com/articles/dataset/Algorithm_steps_for_house_price_prediction_model_/30544252CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305442522025-11-05T18:29:04Z |
| spellingShingle | Algorithm steps for house price prediction model. Xuan Wang (55634) Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reducing training time mean absolute error enhancing regulatory compliance source geographic data convert unstructured data spatial feature extraction hpem achieves 98 poor generalization issues overall prediction accuracy optimized attention mechanism bat optimization algorithm attention mechanism structured data hpem ), feature stability xlink "> thereby minimizing research problem network integrates loop updates improve explainability impact features gathered properties existing methods evaluated using effectively utilized effective exploration dynamic features distribution shifts directly improves developed model computation inefficiency closer convergences balancing exploration |
| status_str | publishedVersion |
| title | Algorithm steps for house price prediction model. |
| title_full | Algorithm steps for house price prediction model. |
| title_fullStr | Algorithm steps for house price prediction model. |
| title_full_unstemmed | Algorithm steps for house price prediction model. |
| title_short | Algorithm steps for house price prediction model. |
| title_sort | Algorithm steps for house price prediction model. |
| topic | Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reducing training time mean absolute error enhancing regulatory compliance source geographic data convert unstructured data spatial feature extraction hpem achieves 98 poor generalization issues overall prediction accuracy optimized attention mechanism bat optimization algorithm attention mechanism structured data hpem ), feature stability xlink "> thereby minimizing research problem network integrates loop updates improve explainability impact features gathered properties existing methods evaluated using effectively utilized effective exploration dynamic features distribution shifts directly improves developed model computation inefficiency closer convergences balancing exploration |