Thailand Asset Value Estimation Using Aerial or Satellite Imagery

ArXiv ID: 2307.08650 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Real Estate Valuation, Deep Learning, Siamese Neural Network, Land Price Prediction, Image Processing, Real Estate

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning techniques such as Siamese Networks with EfficientNet and ensemble methods, requiring a solid understanding of neural network architectures and similarity metrics. It demonstrates strong empirical rigor by using real-world datasets from Google Map API and Kasikorn Business Technology Group, reporting specific performance metrics (AUC and MAPE) and practical outcomes like improved recall for land valuation in Thailand.

  flowchart TD
    A["Research Goal: <br>Improve Land Price Prediction <br>Using Aerial/Satellite Imagery"] --> B["Data Collection"]
    
    B --> C["Tabular Data: KBTG Dataset"]
    B --> D["Visual Data: Google Map API"]
    
    C & D --> E["Model Architecture"]
    
    subgraph E ["Siamese Neural Network"]
        E1["Pretrained EfficientNet"] --> E2["Feature Extraction"]
        E2 --> E3["Similarity Scoring"]
    end
    
    E --> F["Ensemble with Tree-Based Models"]
    
    F --> G["Key Outcomes"]
    
    subgraph G ["Results"]
        G1["AUC: 0.81<br>vs Baseline"]
        G2["Recall: 69.55%<br>vs 59.26%"]
        G3["MAPE: 20%"]
    end