论文标题
位置，位置，位置：基于卫星图像的房地产评估
Location, location, location: Satellite image-based real-estate appraisal
论文作者
论文摘要
购买房屋是人们一生中必须做出的最重要的购买决定之一。关于房地产评估的最新研究重点是将图像数据纳入建模过程之外。这项研究通过使用卷积神经网络来衡量卫星图像和结构化数据的预测性能。与经过结构化数据训练的神经网络的高级基线相比，受训练的CNN模型在MAE中的表现要高7％。此外，滑动窗口热图提供了卫星图像的视觉解释性，揭示了邻里结构在价格估计中至关重要。
Buying a home is one of the most important buying decisions people have to make in their life. The latest research on real-estate appraisal focuses on incorporating image data in addition to structured data into the modeling process. This research measures the prediction performance of satellite images and structured data by using convolutional neural networks. The resulting CNN model trained performs 7% better in MAE than the advanced baseline of a neural network trained on structured data. Moreover, sliding-window heatmap provides visual interpretability of satellite images, revealing that neighborhood structures are essential in the price estimation.
