Residential property valuation using a hybrid fuzzy deep learning approach

The growing importance of housing is not hidden from anyone in terms of the profound and significant effects it has on the various social, political, and economic dimensions of countries; Therefore, accurate and reliable price estimation definitely facilitates policy-making in this field. Hundreds of factors may affect property prices in different situations as a subset of structural, spatial, and socio-economic factors. Therefore, considering these factors, property pricing should be done efficiently. Due to the complex nature of the real estate market, research has used common deep learning algorithms such as DNN, RNN, CNN, etc., but these algorithms are not very suitable for tabular data. On the other hand, the deep learning models in property pricing are also completely definite and do not take into account data uncertainty.

In this article, we have tried to pay attention to the tabular structure of real estate data in applying deep learning methods. The TabNet deep new architecture is used for this purpose. In addition, at the same time as the learning process, it makes feature selection fully interpretable. In this study, also using existing combination techniques, fuzzy logic is combined with deep learning algorithms to learn complex problems faster and more accurately, to overcome the shortcomings of the certainty of deep learning models and not consider the inherent uncertainty of the data in this models. In this study, using the existing combination techniques, also using spatial information system (GIS) to provide a clearer evaluation to ensure full visualization of the spatial pattern of property properties as well as the relationship between these properties and pricing and spatial variables are included in the valuation model. In order to evaluate the proposed methods, real estate data of District 5 of Tehran were used.

The order and prioritization of the impact of features on the pricing of Tehran residential properties by the TabNet algorithm indicate the significant impact of spatial factors. So that in this ranking, after the area, the two spatial characteristics of latitude and longitude have the second and third ranks, respectively. Basically, latitude and longitude indicate the criteria of neighborhoods and the type and prestige of different places in the city, and the social class of different streets and neighborhoods in the city, which is clearly a factor in influencing the price. Finally, TabNet, DNN, CNN, RNN, LSTM, Autoencoder algorithms as well as XGBoost machine learning algorithms were used for the  Tehran data set, and RMSE, MA and  evaluation criteria were compared, which according to the  criterion, a 5% improvement in accuracy was achieved by using TabNet. Finally, the RMSE of the FuzzyTabNet hybrid algorithm for Tehran data decreased by 4/65% compared to the basic TabNet algorithm. The fuzzy Autoencoder network also improved by 5/52% compared to the common Autoencoder network.