This paper compares the predictive powers of hierarchical generalized linear modeling (HGLM), logistic regression, and discriminant analysis with regard to tenure choices between buying property and renting property by sampling the residents of the Greater Taipei area. The results imply that the hit rate and other indicators included in HGLM have better predictive power with regard to tenure choices than the binary logistic regression model and the discriminant analysis model. That is, using HGLM to process nested data can increase prediction accuracy regarding household tenure choices. Furthermore, cross-validation is performed to analyze hit rate stability. The hit rate sequencing from this cross-validation is found to be consistent with the HGLM results, implying that the comparison of the three models in terms of hit rate performance prediction in this study is stable and reliable.