Image Based Appraisal of Real Estate Properties
This work addresses real estate appraisal for buyers and sellers by leveraging easily accessible online data, though it is incremental as it applies existing computer vision methods to a new domain.
The paper tackles real estate price estimation by using a Recurrent Neural Network (RNN) with visual features from online house pictures, achieving improved performance over baseline algorithms in terms of mean absolute error (MAE) and mean absolute percentage error (MAPE).
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).