Deep learning recently attracts considerable attention thanks to its powerful computational capacities in image processing and natural language processing. More and more real estate brokers provide online “Deep” expert systems to help clients with their inquiry of targeted properties before deciding on the transaction of properties. The real estate appraisal is one of the most significant concerns for the clients. In the appraisal process, the estimation of house price depends not only on its attributes but also their neighbors. The influence from neighbors is known as peer-dependence, which is not directly measurable. Thus, real estate appraisal can be improved if the valuation includes the peer-dependence measurement. In this paper, we propose a peer-dependence valuation model (PDVM), which is capable of converting the peer-dependence-based valuation problem into a sequence prediction problem. In the proposed model, we first develop a method, K-nearest similar house sampling (KNSHS), to generate sequences from the to-be-value house and nearby houses. Secondly, the bidirectional long short-term memory (B-LSTM) layers extract the features of sequences. Finally, the fully connected (FC) layer estimates the house price based on the features. The experimental results indicate that our model outperforms the other state-of-the-art machine learning models being used for real estate appraisal.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.