The affiliated school district of a real estate property is often a crucial concern. How to automate the identification of residential homes located in a favorable educational environment, however, is largely unexplored until now. The availability of heterogeneous estate-related data offers a great opportunity for this task. Nevertheless, it is such heterogeneity that poses significant challenges to their amalgamation in a unified fashion. To this end, we develop G-LRMM model to integrate digital price, textual comments, and geographical location information together. The proposed approach is able to capture the in-depth interaction among multi-Type data greatly. The evaluation on the dataset of Beijing property market justifies the benefits of our approach over baselines. The further comparison among different components is also conducted and demonstrates their important roles. Moreover, the proposed model can offer useful insights into modeling heterogeneous data sources.