Objective Addressing issues of distribution and class imbalance in urban housing market data along with insufficient interpretability of the Hedonic Price Model HPM in explaining the relationship between house price and features a housing pricing model for sub-market division based on generative adversarial networks GAN is proposed. Methods In improving the sub-market division housing pricing model GAN is first introduced as a technical module for data enhancement to generate synthetic samples with diversity and fidelity to increase the diversity of the samples and improve the generalization ability of the model. Subsequently GAN-based data augmentation is integrated with the submarket division to develop a housing pricing model. Finally housing prices are predicted based on the probability of the submarkets to which the houses belong and key influencing factors on the house prices are analyzed to improve the prediction accuracy and interpretability. Results The proposed model is compared with five existing models and a submarket division housing pricing model without GAN based on average absolute percentage error average absolute errorand root mean square error. Algorithm performance is tested using 2020 real estate data from Hangzhou. Conclusion Experimental results demonstrate that after introducing the data augmentation technology module based on GAN this model is superior to comparative models in predicting real estate prices and has the advantage of interpretability