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.

本文提出一种利用自监督视觉转换器的新方法来进行房地产估值，该算法利用了机器学习、计算机视觉和房地产数据训练模型来预测一个给定物业的价值。通过使用房屋内部、外部和街景等定性图像以及卧室数、浴室数、平方英尺、土地面积、物业年龄、犯罪率和邻近便利设施等定量特征来评估我们的算法，我们发现该技术能够准确地预测物业的价值，并比传统评估方法表现更好。