Land Price Prediction with Machine Learning in Mueang Khon Kaen District

Authors

  • Yosita Sriwuttisap Student, Department of Computing Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand https://orcid.org/0009-0005-4958-6211
  • Dusita Sungklinhom Student, Department of Computing Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand https://orcid.org/0009-0009-7214-3637
  • Sakpod Tongleamnak Lecturer, Dr., Department of Computing Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand https://orcid.org/0000-0001-9027-7836
  • Thanaphon Tangchoopong Lecturer, Department of Computing Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand https://orcid.org/0009-0008-9197-4703

DOI:

https://doi.org/10.14456/jcct.2024.8

Keywords:

Land Price Assessment, Machine Learning, Important Factors

Abstract

This research aims to develop a model of land price assessment and study of factors influencing land prices with machine learning used as a guideline for determining the estimated price to be close to the actual purchase price in Mueang Khon Kaen District from land price information traded on websites in enforcement department of 193 locations, and land price assessment information in land department of 1,500 locations in this study the factors involved include appraisal value, property type, land size, distance, and the average appraisal value of five nearby plots of land. The models used for analysis are Regression Tree, Random Forest, Gradient Boosted Trees, and Linear Regression. Which, the land price assessment from the case study by model measurement in MAE, RMSE, R-squared, Grid Search, and Cross-validation to selected model parameters and evaluate their performance. The results found that the model with the best predictive performance is Gradient Boosted Trees in R-squared at the highest of 0.80, MAE, and RMSE at the lowest of 7929.40, and 15281.33, respectively. Feature Importance in the locations with the most influence on prediction, followed by area size, average appraised value from five nearby locations, and property type.

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Published

08/15/2024

How to Cite

Sriwuttisap, Y., Sungklinhom, D. ., Tongleamnak, S. ., & Tangchoopong, T. (2024). Land Price Prediction with Machine Learning in Mueang Khon Kaen District. Journal of Computer and Creative Technology, 2(2), 71–86. https://doi.org/10.14456/jcct.2024.8

Issue

Vol. 2 No. 2 (2024): Journal of Computer and Creative Technology Vol. 2 No. 2 (May - August 2024)

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Reseach Article

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