The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.

翻译：近年来，人工智能在房地产市场的应用日益增长。本文提出一种基于自监督视觉Transformer（自监督视觉变换器）的新型房产估值方法，该方法融合了计算机视觉与深度学习领域的最新突破。本算法综合运用机器学习、计算机视觉和特征价格模型，通过对房地产数据的训练实现目标房产价值评估。我们收集并预处理了美国科罗拉多州博尔德市的房地产数据集，用于算法训练、验证与测试。该数据集包含定性图像（包括房屋内部、外部及街景）与定量特征（如卧室数量、浴室数量、建筑面积、土地面积、房龄、犯罪率及配套设施可达性）。我们采用均方根误差（RMSE）等指标评估模型性能。研究结果表明，该技术能以较低的RMSE准确预测房产价值。与未利用房产图像的传统评估方法相比，本算法展现出更优性能，具备实际应用潜力。