For PRNU-based image manipulation localization, the cor- relation predictor plays a crucial role to reduce false positives considerably, as well as increasing accuracy of manipulation local- ization. In this paper, we propose a novel correlation predictor with a non-parametric learning algorithm, which is Locally Weighted Regression. Instead of fitting a global set of model parameters, a non-parametric learning algorithm fits a model dynamically by sampling the training set based on the pixel in the query image at which the correlation needs to be predicted. Our experimental results suggest that building a model dynamically based on the distance of training examples from the query pixel in the feature space helps to predict the correlation more accurately. Experimental results on benchmark data indicate that integrating the new predictor significantly improves the accuracy of predicted correlation, as well as image manipulation localization performance of PRNU-based forensic detectors.