Personal Guarantees, Loan Pricing, and Lending Structure in Finnish Small Business Loans*¹

1. The authors wish to thank Professor Hannu Schadéwitz and the two anonymous reviewers for their insightful comments and extensive inputs to this article. This research is a part of FINNON research project financially supported by the Academy of Finland (Decision No. 116740). The bank is gratefully acknowledged for providing access to the confidential data used in this study.

Abstract

This study analyzes the role of personal guarantees and collateral in the context of two different lending structures: one transaction and the other relationship based. The innish bank data, which were uniquely accessible for the study, enabled an exploration of credit files with specific details of the characteristics of the lending relationship during the period 1995–2001. According to the empirical results, the use of personal guarantees is an indication of transaction‐based lending. Personal guarantees seem to increase the loan premium in transaction‐based loans more than in relationship‐based loans. Close ties between a bank and a firm seem to be a desirable basis for small and medium‐sized enterprise bank lending.

1. The authors wish to thank Professor Hannu Schadéwitz and the two anonymous reviewers for their insightful comments and extensive inputs to this article. This research is a part of FINNON research project financially supported by the Academy of Finland (Decision No. 116740). The bank is gratefully acknowledged for providing access to the confidential data used in this study.

1. The authors wish to thank Professor Hannu Schadéwitz and the two anonymous reviewers for their insightful comments and extensive inputs to this article. This research is a part of FINNON research project financially supported by the Academy of Finland (Decision No. 116740). The bank is gratefully acknowledged for providing access to the confidential data used in this study.

Notes

1. The authors wish to thank Professor Hannu Schadéwitz and the two anonymous reviewers for their insightful comments and extensive inputs to this article. This research is a part of FINNON research project financially supported by the Academy of Finland (Decision No. 116740). The bank is gratefully acknowledged for providing access to the confidential data used in this study.

1 The scope or breadth of bank–firm relationship is a dimension that measures the strength of relationship, in addition to the duration of relationship. For instance, the scope of relationship can define the relationship's breadth by the number of financial services (loans, deposits, savings accounts, financial management services, etc.) the firm has in the bank.

2 In return to getting access to the loan files of this bank, we have promised confidentiality to the bank and the customers regarding identity and location.

3 For more literature on concentrated lending in bank relationships, see Petersen and Rajan (1994), Berger, Demsetz, and Strahan (1999), and Berger and Hannan (1998).

4 All the firms are small businesses with fewer than 30 employees, except one with 160 employees (descriptive statistics, not an explicit restriction).

5 These include 55 loans with fixed rates and 53 lacking loan rate information.

6 The number of observations in our regression models drops dramatically mainly because of the lack of firm‐specific information from financial statements. Thus, the reduced sample size is due to missing data. The regressions include 279–285 observations.

7 Personal guarantee as a part of total collateralization has minor significance in a monetary sense compared with the real assets that are used for collateralization.

8 Our data include 55 observations (4.6 percent) of collateralization that exceeds the value of two (overcollateralized). We have set these values as equal to two for simplicity. We assume that the bank is indifferent to the extent of overcollateralization when it is greater than two.

9 The firm's liabilities to the subject bank include all performing loans as well as bank guarantees to the firm.

10 We use two aggregate risk levels to generate practical interpretation of the information content whether the firm is identified as high‐ or low‐risk firm. We tested regressions also with the five different risk classes and report that the identification to high/low riskiness is more understandable for the reader with the aggregation. A similar setting with the data examination has been implied in Peltoniemi (2007a b) , which is referred to in this approach.

11 The number of observations in our regression models drops dramatically mainly because of the lack of firm‐specific information from financial statements. Thus, the reduced sample size is due to missing data. The regressions include 279–285 observations.

12 The variable structure is used in Table 4/regression 2, replacing the variables duration, financial services, maturity, and loan size, as established accordingly.

13 The variable structure is used in the Table 4/regression 2, replacing the variables duration, financial services, maturity, and loan size, as established accordingly.

Additional information

Notes on contributors

Janne Peltoniemi

Janne Peltoniemi is Head of Degree Programme in Business Management and Principal Lecturer at Centria University of Applied Sciences, Kokkola, Finland, and is affiliated as a post‐doc researcher at the University of Oulu, Department of Accounting and Finance, Oulu, Finland.

Markku Vieru

Markku Vieru is a professor of Accounting at University of Lapland, Finland.