Many datasets resulting from participant ratings for word norms and also concreteness ratios are available. However, the concreteness information of infrequent words and non-words is rare. This work aims to propose a model for estimating the concreteness of infrequent and new lexicons. Here, we used Lancaster sensory-motor word norms to predict the word concreteness ratios of an English word dataset. After removing the missing values, we employed a stepwise multiple linear regression (SW-MLR) procedure for choosing an optimum number of norms to develop a predictive multiple regression model. Finally, we validate our model using 10-fold cross-validation. The final model could predict concreteness by Residual Mean Standard Error equal to 0.723 and R-Square of 0.515. Also, our results showed that all 11 variables of this dataset except the Head-mouth parameter are useful predictors. In conclusion, as a growing demand to know the concreteness values of non-words and also infrequent words is evident, our statistical method can pave the way for controlled experiments when choosing non-words as a stimulus is critical. [Note: The publication year (2024) shown on the PDF is incorrect. The correct year of publication is 2023.]