Real estate appraisal, which is the process of estimating the price for real
estate properties, is crucial for both buys and sellers as the basis for
negotiation and transaction. Traditionally, the repeat sales model has been
widely adopted to estimate real estate price. However, it depends the design
and calculation of a complex economic related index, which is challenging to
estimate accurately. Today, real estate brokers provide easy access to detailed
online information on real estate properties to their clients. We are
interested in estimating the real estate price from these large amounts of
easily accessed data. In particular, we analyze the prediction power of online
house pictures, which is one of the key factors for online users to make a
potential visiting decision. The development of robust computer vision
algorithms makes the analysis of visual content possible. In this work, we
employ a Recurrent Neural Network (RNN) to predict real estate price using the
state-of-the-art visual features. The experimental results indicate that our
model outperforms several of other state-of-the-art baseline algorithms in
terms of both mean absolute error (MAE) and mean absolute percentage error
(MAPE).

使用循环神经网络和最先进的视觉特征来预测房地产价格，结果表明我们的模型在平均绝对误差（MAE）和平均绝对百分比误差（MAPE）方面均优于其他现有算法。