Feature configuration and fusion framework.
<div><p>The automated valuation model (AVM) has been widely used by real estate stakeholders to provide accurate property value estimations automatically. Traditional valuation models are subjective and inaccurate, and previous studies have shown that machine learning (ML) approaches per...
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2025
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| _version_ | 1852020268838420480 |
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| author | Lin Deng (127858) |
| author_facet | Lin Deng (127858) |
| author_role | author |
| dc.creator.none.fl_str_mv | Lin Deng (127858) |
| dc.date.none.fl_str_mv | 2025-05-19T17:36:05Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321951.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Feature_configuration_and_fusion_framework_/29101794 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Neuroscience Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified structured tabular data spatial distribution patterns source unstructured data shapley additive explanations real estate stakeholders multiple linear regression experimental results show real estate valuation automated valuation model lightgbm ), k approaches perform better formulate ml pipelines eight ml regressors source image fusion source image features image feature values remote sensing images nonlinear associations exist div >< p consider integrating multi traditional valuation models image fusion image features nonlinear associations valuation models mlp ), feature configuration better understanding enhanced ml urban planners street view significantly different reliable predictions random forest public authorities previous studies nearest neighbors machine learning incorporating multi housing prices findings contribute extra tree city centre case study |
| dc.title.none.fl_str_mv | Feature configuration and fusion framework. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>The automated valuation model (AVM) has been widely used by real estate stakeholders to provide accurate property value estimations automatically. Traditional valuation models are subjective and inaccurate, and previous studies have shown that machine learning (ML) approaches perform better in real estate valuation. These valuation models are based on structured tabular data, and few consider integrating multi-source unstructured data such as images. Most previous studies use fixed feature space for model training without considering the model performance variation brought by various feature configuration parameters. To fill these gaps, this study uses Hong Kong as a case study and proposes an enhanced ML-based real estate valuation framework with feature configuration and multi-source image data fusion, including exterior housing photos, street view and remote sensing images. <sup></sup>Eight ML regressors, namely, Random Forest, Extra Tree, XGBoost, Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear Regression (MLR) are used to formulate ML pipelines for training. The SHapley Additive exPlanations (SHAP) method is used to examine the effects of images on housing prices. The experimental results show that the model performances using different feature configuration parameters are significantly different, indicating the necessity of feature configuration to obtain more accurate and reliable predictions. Extra Tree performs significantly better than other models. Half of the top 10 significant features are image features, and incorporating multi-source image features can improve property valuation accuracy. Nonlinear associations exist between image features and housing prices, and the spatial distribution patterns of image feature values and corresponding SHAP main effects vary significantly from the city centre to the suburbs. These findings contribute to a better understanding of AVM development with image fusion and the nonlinear associations between image features and housing prices for public authorities, urban planners, and real estate developers.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_2eb7d0ffac733dbd6370da45d73c7c39 |
| identifier_str_mv | 10.1371/journal.pone.0321951.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29101794 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Feature configuration and fusion framework.Lin Deng (127858)MedicineNeuroscienceSpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedstructured tabular dataspatial distribution patternssource unstructured datashapley additive explanationsreal estate stakeholdersmultiple linear regressionexperimental results showreal estate valuationautomated valuation modellightgbm ), kapproaches perform betterformulate ml pipelineseight ml regressorssource image fusionsource image featuresimage feature valuesremote sensing imagesnonlinear associations existdiv >< pconsider integrating multitraditional valuation modelsimage fusionimage featuresnonlinear associationsvaluation modelsmlp ),feature configurationbetter understandingenhanced mlurban plannersstreet viewsignificantly differentreliable predictionsrandom forestpublic authoritiesprevious studiesnearest neighborsmachine learningincorporating multihousing pricesfindings contributeextra treecity centrecase study<div><p>The automated valuation model (AVM) has been widely used by real estate stakeholders to provide accurate property value estimations automatically. Traditional valuation models are subjective and inaccurate, and previous studies have shown that machine learning (ML) approaches perform better in real estate valuation. These valuation models are based on structured tabular data, and few consider integrating multi-source unstructured data such as images. Most previous studies use fixed feature space for model training without considering the model performance variation brought by various feature configuration parameters. To fill these gaps, this study uses Hong Kong as a case study and proposes an enhanced ML-based real estate valuation framework with feature configuration and multi-source image data fusion, including exterior housing photos, street view and remote sensing images. <sup></sup>Eight ML regressors, namely, Random Forest, Extra Tree, XGBoost, Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear Regression (MLR) are used to formulate ML pipelines for training. The SHapley Additive exPlanations (SHAP) method is used to examine the effects of images on housing prices. The experimental results show that the model performances using different feature configuration parameters are significantly different, indicating the necessity of feature configuration to obtain more accurate and reliable predictions. Extra Tree performs significantly better than other models. Half of the top 10 significant features are image features, and incorporating multi-source image features can improve property valuation accuracy. Nonlinear associations exist between image features and housing prices, and the spatial distribution patterns of image feature values and corresponding SHAP main effects vary significantly from the city centre to the suburbs. These findings contribute to a better understanding of AVM development with image fusion and the nonlinear associations between image features and housing prices for public authorities, urban planners, and real estate developers.</p></div>2025-05-19T17:36:05ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0321951.g006https://figshare.com/articles/figure/Feature_configuration_and_fusion_framework_/29101794CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291017942025-05-19T17:36:05Z |
| spellingShingle | Feature configuration and fusion framework. Lin Deng (127858) Medicine Neuroscience Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified structured tabular data spatial distribution patterns source unstructured data shapley additive explanations real estate stakeholders multiple linear regression experimental results show real estate valuation automated valuation model lightgbm ), k approaches perform better formulate ml pipelines eight ml regressors source image fusion source image features image feature values remote sensing images nonlinear associations exist div >< p consider integrating multi traditional valuation models image fusion image features nonlinear associations valuation models mlp ), feature configuration better understanding enhanced ml urban planners street view significantly different reliable predictions random forest public authorities previous studies nearest neighbors machine learning incorporating multi housing prices findings contribute extra tree city centre case study |
| status_str | publishedVersion |
| title | Feature configuration and fusion framework. |
| title_full | Feature configuration and fusion framework. |
| title_fullStr | Feature configuration and fusion framework. |
| title_full_unstemmed | Feature configuration and fusion framework. |
| title_short | Feature configuration and fusion framework. |
| title_sort | Feature configuration and fusion framework. |
| topic | Medicine Neuroscience Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified structured tabular data spatial distribution patterns source unstructured data shapley additive explanations real estate stakeholders multiple linear regression experimental results show real estate valuation automated valuation model lightgbm ), k approaches perform better formulate ml pipelines eight ml regressors source image fusion source image features image feature values remote sensing images nonlinear associations exist div >< p consider integrating multi traditional valuation models image fusion image features nonlinear associations valuation models mlp ), feature configuration better understanding enhanced ml urban planners street view significantly different reliable predictions random forest public authorities previous studies nearest neighbors machine learning incorporating multi housing prices findings contribute extra tree city centre case study |