This paper provides a credit risk quantification system for banks to estaminate the credit risk of loans to small and mediume nterprises(SMEs). As we know, the most difficult thing for banks to handle SME loans is whose financial reporting lacks transparency and no valuable reference. We use non-financial variables and employ the logisitic regression to develop the credit risk predict model. We concludet: first, when construct a SMEs credit rating system, non-financial factors should be seriously considered and adopted. Second, because of positioned different stage of firm life cycle, the credit rating model should be set up differently by different stage of firm. Third, SME loans should to make much of establishing ¡§relationship-based¡¨ in order to meet the various demands of risk management.

The thesis is focused on loans for the financing of housing needs (mortgage loans, construction loans and consumer loans on the property). The work deals with the important parameters of each product. In particular, the definition of the purpose of financing, interest calculation, interest rate guarantees, collateral, repayment duration. Furthermore, the work focuses on state aid granted in these products, as well as credit risk, which is the issue of lending inextricably linked.