A novel method for predicting temperature-dependent properties is presented. The method involves the use of measured property values of predictive compounds that are structurally similar to the target compound, and molecular descriptor values. The quantitative structure - structure - property relationship (QS2PR) is used to model a linear relationship between property values of the target and the predictive compounds. Whenever necessary, response modeling methodology (RMM) can be employed to obtain a nonlinear regression model for representing property data of the predictive compounds. The application of the method is demonstrated under a variety of conditions by prediction of the temperature-dependent liquid-density variation of 1-butene, toluene, n-hexane, and n-heneicosane. It is shown that straightforward application of the proposed method provides predictions with accuracy within the experimental error level. An advantage of the proposed method over other similar prediction models is that it does not require measured property values of the target compound.