人們可以輕易辨識圖片的內容，從房屋外觀分析出該房屋的價錢、屋齡等， 機器也可以在辨識度上有很好的結果。本研究以多模態學習為基礎，取用內政部 不動產成交案件實際資訊資料供應系統提供的資料，加上利用 Google 街景服務取 得每個地址的街景圖。使用 XGBoost 演算法對圖片做處理，並利用隨機森林 (Random Forest)、線性迴歸等演算法，建立模型，預測房價。
依據全台六都的都市發展計劃，高雄市公告公園綠地佔都市計畫區面積百分 比居於首位，因此本研究除了探討房屋本身的條件如何影響預測房價的結果，同 時使用 Google distance API，探討公園綠地此一變數對房價呈現多大的影響。實證 結果，圖片本身能夠達成預測房價，加入房屋原始資料後，提升預測準確率。此 外，實驗證明，700 公尺內擁有越多公園綠地的房屋，顯著呈現高房價。

People can easily view the picture to judge the price of a house through the building age, environment conditions and appearances. Machines can also reach a good result as humans do. This study focuses on multi-modal learning by using the actual transaction information from the Ministry of the Interior’s real estate data system and the street view images downloaded from Google Street View service. We convert images by XGBoost and add new converted data to build a new model to predict house prices.
According to Taiwan government’s urban development plan, Kaohsiung City possesses the largest percentage of green space coverage compared to the other five special municipalities of Taiwan. Therefore, to discover how green space affects the prediction of house prices, this study also used Google distance to discuss the importance of green space. From the empirical results, the accuracy of combining the transaction data with images is improved. Furthermore, our experiments show that the distance within 700 meters from the house to green space, the influence of the green space on house prices is significant.

論文審定書 i
摘要 ii
Abstract iii
List of Figures v
List of Tables vi
1. Introduction 1
2. Background and Related Works 2
2.1. Related Work on House Prices 2
2.2. Multimodal Learning 4
2.3. Google Street View 6
2.4. Ensemble learning 8
2.5. EXtreme Gradient Boosting (XGBoost) 9
2.6. Random Forest 10
3. Methodology 11
4. Experiment and Discussion 14
4.1. Environment setup 15
4.2. Transaction Dataset 15
4.3. Green Space Feature 16
4.4. Images Feature 17
4.5. Performance Comparison 18
4.6. Important Variables 21
4.7. Discussion 24
5. Conclusion 24
Reference 26
中文文獻 33
Appendix A 34

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