For the molecular fragments Variable Connectivity Index (mfVCI), the molecular fragments are defined as the atoms or functional groups and are also regarded as the molecular structure unit for the major influence of the property. Different molecular fragments were endued with different fragments weights. In order to test whether or not the mfVCI could be used for predicting different properties of the same molecules, the Quantitative Structure-Property Relationship (QSPR) models of aqueous solubility (S_w) and n-octanol/water partition coefficients(K_ow) for 36 halogenated benzenes based on the mfVCI were investigated. The fitting goodness of the QSPR models are mainly characterized by determination coefficient (R²), standard deviation (SD), Fischer variance ratio(F), and significant lever (P). The internal cross-validation including leave-one-out and leave-many-out methods with cross-validated correlation coefficients (q_loo² and q_lmo²) was used to evaluate the model's robustness. The external cross-validation was used to evaluate the predictive power of the models developed from the training set by correlation coefficients (q_ext²). The linear relationship was assumed between mfVCI and S_w or K_ow in here. The mfVCI was optimized by using Solver. The target function is the root mean calculated residual sum of squares for training set. The QSPR models with R² value of more than 0.97, q² value of more than 0.89, and q_ext² value of more than 0.95 have the good robustness and predictive ability as well as fitting ability. The mfVCI could distinguish the different role of same atoms or functional groups among different chemical bonding. The results show that the mfVCI could be well used in the prediction of both of aqueous solubility and n-octanol/water partition coefficients for halogenated benzenes. It could describe different properties equally well. The mfVCI maybe play an important role in the development of molecular descriptors on QSPR researches.