Real estate is a critical sector in Thailand's economy, which has led to a
growing demand for a more accurate land price prediction approach. Traditional
methods of land price prediction, such as the weighted quality score (WQS), are
limited due to their reliance on subjective criteria and their lack of
consideration for spatial variables. In this study, we utilize aerial or
satellite imageries from Google Map API to enhance land price prediction models
from the dataset provided by Kasikorn Business Technology Group (KBTG). We
propose a similarity-based asset valuation model that uses a Siamese-inspired
Neural Network with pretrained EfficientNet architecture to assess the
similarity between pairs of lands. By ensembling deep learning and tree-based
models, we achieve an area under the ROC curve (AUC) of approximately 0.81,
outperforming the baseline model that used only tabular data. The appraisal
prices of nearby lands with similarity scores higher than a predefined
threshold were used for weighted averaging to predict the reasonable price of
the land in question. At 20\% mean absolute percentage error (MAPE), we improve
the recall from 59.26\% to 69.55\%, indicating a more accurate and reliable
approach to predicting land prices. Our model, which is empowered by a more
comprehensive view of land use and environmental factors from aerial or
satellite imageries, provides a more precise, data-driven, and adaptive
approach for land valuation in Thailand.

研究提出了一种基于相似度的资产估值模型，利用预先训练的 EfficientNet 神经网络结合深度学习和基于树的模型来评估地块相似性，从而提高泰国国内房地产估价方法的准确性和可靠性。