In recent years there has been substantial interest in small area estimation (SAE) that is largely driven by practical demands. In policy making regarding the allocation of resources to subgroups (small areas), or determination of subgroups with specific characteristics (e.g. in health and medical studies) in a population, it is desirable that the decisions are made on the basis of reliable estimates. A major topic in SAE is estimation of mean-squared prediction errors (MSPEs) for predictors of various characteristics of interest that are associated with the small areas. We propose a simple, unified, Monte-Carlo assisted approach to second-order unbiased estimation of MSPE of a small area predictor. The proposed MSPE estimator is easy to derive, has a simple expression, and applies to a broad range of predictors that include the traditional empirical best linear unbiased predictor (EBLUP), empirical best predictor (EBP), and post model selection EBLUP and EBP as special cases. Theoretical and empirical results demonstrate properties and advantages of the proposed MSPE estimator.