The paper presents method of hybrid prediction system for debt portfolio appraisal. Based on the local area competence, time spread repayment values are predicted by means of hybrid combination of various machine learning techniques. The above methods include among others clustering of references, model selection and enrichment of input variables with prediction outputs from preceding periods. Experimental studies concern the method's configuration influence on its general performance such as number of distinct predictors and number of competence areas.