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An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants
16 references·1 citing·43 second hop
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References

  • 195317. A Value for n-Person Games6,944 cited
  • 2008Determinants of house prices in Turkey: Hedonic regression versus artificial neural network361 cited
  • 2020Predicting property prices with machine learning algorithms293 cited
  • 2011Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics264 cited
  • 2019Towards a Critical Housing Studies Research Agenda on Platform Real Estate206 cited
  • 2018Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments206 cited
  • 2020Understanding house price appreciation using multi-source big geo-data and machine learning205 cited
  • 2006Measuring the neighboring and environmental effects on residential property value: Using spatial weighting matrix196 cited
  • 2017Population migration, urbanization and housing prices: Evidence from the cities in China193 cited
  • 2020A HOUSE PRICE VALUATION BASED ON THE RANDOM FOREST APPROACH: THE MASS APPRAISAL OF RESIDENTIAL PROPERTY IN SOUTH KOREA180 cited
  • 1999Who Was First? An Examination of an Early Hedonic Study175 cited
  • 2013Prediction accuracy in mass appraisal: a comparison of modern approaches174 cited
  • 2017Big Data in Real Estate? From Manual Appraisal to Automated Valuation164 cited
  • 2019Amenity effects of urban facilities on housing prices in China: Accessibility, scarcity, and urban spaces

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149 cited
  • 2019The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence148 cited
  • 2020Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices131 cited
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    • 2023Exploration of women cyclists’ perceived security using tree-based machine learning algorithms7 cited