15 research outputs found
Analysis on Decision Making Factors of Financial Institutions Participating in Real Estate Project Finance
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ๊ฑด์คํ๊ฒฝ๊ณตํ๋ถ,2020. 2. ์ ์ฐฝ๋ฌด.Real estate PF(Project Finance) indicates the method that acquires financial procurement of a real estate development, secured on future cash flow derived from such project. In recent years, the market style evolved both externally and internally, especially with increased participation of non-bank financial institutions coupled with relatively decreased rate of debt guarantee for constructors. Due to these changes, the role that financial structure stability and business opportunity plays in ensuring the participation of financial institutions in real estate PF is growing. Consequently, the present study aims to understand the relevant factors of financial structure stability and business opportunity capable of determining the decision-making of financial institutions involved in real estate PF.
The present study hence analyzed 220 cases of PF loans that participated through arranged recruitments or direct stock loans in a domestic securities company A, between year 2016 and 2019. Furthermore, the study also took into consideration four variables in order to clarify the factors unidentified in the previous studiesโthe ratio of PF loans to the total project sales, the unit price (per 3.3) of a collateral which is the prime repayment resource of the loan institution, the sales rate that allows for the repayment of both principle and interest, and the repayment priority of the financial institution.
In the study, multiple regression analysis was used, and a business model with finance cost as a dependent variable and risk factors perceived by the financial institutes in real estate PF as independent variables was constructed. More specifically, all-in cost spread was selected for the dependent variable (finance cost). For the independent variables, the proxy variables of the aforementioned institution-perceived risk factors as well as risk factors in the construction process such as those of land purchase, building authorization, completion, sales, and loan conditions were accounted for. To ensure model optimization, backward elimination method was applied.
The results showed that finance cost increased as the ratio of PF loans to the total project sales and the unit price (per 3.3ใก) of a collateral increased. The finance cost also increased as the exit sales rate (the sales rate that allows for the repayment of both principle and interest) increased and the repayment priority of the financial institution decreased. The control variables in the study all proved to be statistically significant, with the exception of building authorization state and class of the guarantee institution, and most of the variables showed corresponding statistical signs with those of the previous studies. The significance of the present study in urban policies regarding private capital usage is as follows.
The study indicated that the decision-making processes of financial institutions were based on financial structure stability and business opportunity. Therefore, it is reasonable to say that public sectors ensure business opportunity for active private capital usage by deregulation, with adjustments to factors such as building usage and floor area ratio. Also, it is needed that repayment stability is elevated regarding financial structure. Such change would be plausible, for if the public sectors were to take part in equity or subordinates, the financial institutions participating in seniorities with the resulting lower perceived risk would be able to procure private capital at a relatively low cost. In this case, a more efficient capital management is anticipated, for the public sectors can utilize limited budget in various businesses, and can even profit from business success under some circumstances.
Nonetheless, the present study has following limits. First, it could not clarify the differences in decision-making processes regarding the varying types of financial institutions by analyzing loans made through arranged recruitment and stock participation of various institutions. Further, while it significantly identified general factors relevant to decision-making by examining diverse items and business areas, it could not clarify the different factors influencing participation decisions depending on business types and specific conditions of location. Accordingly, a follow-up study is needed to account for the roles of business types, items, and specific conditions of location in determining an institutions decision-making process. Deriving specific factors in such research will not only allow for a deeper understanding of private capitals, but also certainly contribute to an enhanced urban policy-making with an efficient usage of limited resources.๋ถ๋์ฐPF(Project Finance)๋ ๋ถ๋์ฐ ๊ฐ๋ฐ์ฌ์
์์ ๋ฐ์ํ๋ ๋ฏธ๋ํ๊ธํ๋ฆ์ ๋ด๋ณด๋ก ํด๋น ํ๋ก์ ํธ์ ์๊ธ์ ์กฐ๋ฌํ๋ ๋ฐฉ์์ด๋ค. ์ต๊ทผ ๋ช ๋
๊ฐ ์์ฅ์ ์ธํ์ด ์ฑ์ฅ ํ์์ผ๋ฉฐ, ๋น ์ํ๊ธฐ๊ด ์ฐธ์ฌ์ฆ๊ฐ, ์๋์ ์ธ ์๊ณต์ฌ์ ์ฑ๋ฌด๋ณด์ฆ๋น์จ ๊ฐ์ ๋ฑ ๋ด์ ์ผ๋ก๋ ๋ณํํ๊ณ ์๋ค. ์ด๋ฌํ ๋ณํ๋ก ์ต๊ทผ ๊ธ์ต๊ธฐ๊ด๋ค์ ๋ถ๋์ฐPF ์ฐธ์ฌ ์์ฌ๊ฒฐ์ ์์ธ์์ ๊ธ์ต๊ตฌ์กฐ์ ์์ ์ฑ ๋ฐ ํ๋ก์ ํธ ์ฌ์
์ฑ์ด ๋ฏธ์น๋ ์ํฅ์ด ์ปค์ง๊ณ ์๋ค. ๊ทธ์ ๋ฐ๋ผ ๋ณธ ์ฐ๊ตฌ๋ ๊ธ์ต๊ตฌ์กฐ ๋ฐ ์ฌ์
์ฑ์ด ๋ถ๋์ฐPF์ฐธ์ฌ ๊ธ์ต๊ธฐ๊ด์ ์์ฌ๊ฒฐ์ ์ ๋ฏธ์น๋ ์ํฅ์์ธ์ ํ์
ํ๊ณ ์ ํ์๋ค.
์ด๋ฅผ ์ํด ๊ตญ๋ด A์ฆ๊ถ์ฌ์์ 2016๋
์์ 2019๋
์ฌ์ด ๋ชจ์ง์ฃผ์ ํน์ ์ง์ ๋์ฃผ๋ก ์ฐธ์ฌํ PF๋์ถ 220๊ฑด์ ๋ถ์ํ์๋ค. ๋ํ ์ ํ์ฐ๊ตฌ์์ ๊ท๋ช
๋์ง ๋ชปํ ์์ธ์ ๋ฐํ๋ด๊ธฐ ์ํด ๋ณธ ์ฐ๊ตฌ๋ ํ๋ก์ ํธ ๋งค์ถ ๋๋น PF๋์ถ ๋น์จ, ๋์ถ๊ธฐ๊ด์ด ์ฃผ ์ํ์ฌ์์ผ๋ก ํ๋ ๋ด๋ณด๋ฌผ๊ฑด์ ๋จ์๋ฉด์ ๋น(3.3ใก/์ ์ฉ)๊ฐ๊ฒฉ, ๋์ถ์๋ฆฌ๊ธ ์ํ์ด ๊ฐ๋ฅํด์ง๋ ๋ถ์๋ฅ ์์ค, ํน์ ๊ธ์ต๊ธฐ๊ด์ ๋์ถ์๋ฆฌ๊ธ ์ํ์ฐ์ ์์๋ฅผ ๋ณ์๋ก ๋ฐ์ํ์๋ค.
๋ถ์๋ฐฉ๋ฒ์ ๋ค์คํ๊ท๋ถ์(Multiple Regression Analysis)์ ์ฌ์ฉํ์์ผ๋ฉฐ, ๊ธ์ต๋น์ฉ์ ์ข
์๋ณ์๋ก ๋ถ๋์ฐPF ์งํ ์ ๊ธ์ต๊ธฐ๊ด์ด ์ธ์ํ๋ ๋ฆฌ์คํฌ์์ธ์ ๋
๋ฆฝ๋ณ์๋ก ํ๋ ๋ชจํ์ ๊ตฌ์ถํ์๋ค. ์ข
์๋ณ์์ธ ๊ธ์ต๋น์ฉ์ ์ด ๊ธ์ต๋น์ฉ(All-in Cost) ์คํ๋ ๋(Spread)๋ฅผ ์ ์ ํ์๊ณ , ๋
๋ฆฝ๋ณ์๋ ์์์ ์ธ๊ธํ ๋ณ์ ์ธ ์ฌ์
์งํ๊ฒฝ๊ณผ์ ๋ฐ๋ฅธ ๋ฆฌ์คํฌ ์์ธ์ธ ํ ์ง๋งค์
๋ฐ ์ธํ๊ฐ๋ฆฌ์คํฌ, ์ค๊ณต๋ฆฌ์คํฌ, ๋ถ์๋ฆฌ์คํฌ, ๋์ถ์กฐ๊ฑด์ ํด๋นํ๋ ๋๋ฆฌ๋ณ์๋ค์ ํต์ ๋ณ์๋ก ๋ฐ์ํ์๋ค. ์ต์ ํ๋ ๋ชจํ๊ตฌ์ถ์ ์ํด ํ๋ฐฉ์ ๊ฑฐ๋ฒ(Backward elimination method)์ ์ ์ฉํ์๋ค.
๋ถ์๊ฒฐ๊ณผ ํ๋ก์ ํธ ๋งค์ถ ๋๋น PF๋์ถ ๋น์จ์ด ๋์์ง์๋ก ๊ทธ๋ฆฌ๊ณ ์ฃผ ์ํ์ฌ์์ผ๋ก ํ๋ ๋ด๋ณด๋ฌผ๊ฑด์ 3.3ใก๋น(์ ์ฉ)๊ฐ๊ฒฉ์ด ๋์์๋ก ๊ธ์ต๋น์ฉ์ด ๋์์ก๋ค. ๋ํ ๋์ถ์๋ฆฌ๊ธ ์ํ์ด ๊ฐ๋ฅํด์ง๋ ๋ถ์๋ฅ (Exit๋ถ์๋ฅ )์ด ๋์์ง๊ณ ํน์ ๊ธ์ต๊ธฐ๊ด์ ๋์ถ์๋ฆฌ๊ธ ์ํ์ฐ์ ์์๊ฐ ๋ฐ๋ ค๋ ์๋ก ๊ธ์ต๋น์ฉ์ด ์ฌ๋ผ๊ฐ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ๋ณธ ์ฐ๊ตฌ์ ํต์ ๋ณ์๋ ์ธํ๊ฐ์ ๋ฌด ๋ฐ ๋ณด์ฆ๊ธฐ๊ด ๋ฑ๊ธ ์ธ์ ๋ชจ๋ ํต๊ณ์ ์ผ๋ก ์ ์ํ ๊ฒ์ผ๋ก ๋ํ๋ฌ์ผ๋ฉฐ, ๋ณ์ ๋๋ถ๋ถ์ด ์ ํ์ฐ๊ตฌ์์ ๊ท๋ช
ํ ๊ฒ๊ณผ ๋์ผํ ๋ถํธ๋ฅผ ๋ํ๋๋ค. ๋ณธ ์ฐ๊ตฌ๋ ๋ฏผ๊ฐ์๋ณธ ํ์ฉ์ธก๋ฉด์์ ๋์์ ์ฑ
์ ๋ค์๊ณผ ๊ฐ์ ํจ์๋ฅผ ๊ฐ๋๋ค.
์ฐ๊ตฌ๊ฒฐ๊ณผ ๊ธ์ต๊ธฐ๊ด๋ค์ ์ฌ์
์ฑ ๋ฐ ๊ธ์ต๊ตฌ์กฐ์ ์์ ์ฑ์ ๋ฐํ์ผ๋ก ์์ฌ๊ฒฐ์ ์ ๋ด๋ฆฌ๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ๊ทธ๋ฌ๋ฏ๋ก ์ ๊ทน์ ์ธ ๋ฏผ๊ฐ์๋ณธ ํ์ฉ์ ์ํด์๋ ๊ณต๊ณต๋ถ๋ฌธ์ด ์ฉ๋, ์ฉ์ ๋ฅ ๋ฑ ๊ท์ ์ํ๋ฅผ ํตํด ์ผ์ ๋ถ๋ถ ์ฌ์
์ฑ์ ๋ณด์ฅํ ํ์๊ฐ ์๋ ๊ฒ์ผ๋ก ํ๋จ๋๋ค. ๋ํ ๊ธ์ต๊ตฌ์กฐ์ ์ธก๋ฉด์์ ๋ฏผ๊ฐ์๋ณธ์ ์ํ ์์ ์ฑ์ ๋์ผ ํ์๊ฐ ์๋ค. ์ด๋ฅผ ์ํด ๊ณต๊ณต์ด ์ํ์ฐ์ ์์๊ฐ ๋ฎ์ ํ์์ ๋๋ ์์ฟผํฐ(Equity)์ ์ฐธ์ฌํ๋ค๋ฉด ์ ์์์ ์ฐธ์ฌํ๋ ๊ธ์ต๊ธฐ๊ด๋ค์ ์ํ์ธ์์ด ๋ฎ์์ ธ ์๋์ ์ผ๋ก ๋ฎ์ ๊ธ์ต๋น์ฉ์ผ๋ก ๋ฏผ๊ฐ์๋ณธ์ ์กฐ๋ฌํ ์ ์์ ๊ฒ์ผ๋ก ํ๋จ๋๋ค. ์ด ๊ฒฝ์ฐ ๊ณต๊ณต์ ํ์ ๋ ์์ฐ์ ๋ค์ํ ์ฌ์
์ ๋ถ์ฐํ์ฌ ํ์ฉํ ์ ์๊ณ ๊ฒฝ์ฐ์ ๋ฐ๋ผ ์ฌ์
์ฑ๊ณต์ ๋ฐ๋ฅธ ์ด์ตํฅ์ ๋ ๊ฐ๋ฅํ๋ฏ๋ก ๋ณด๋ค ํจ์จ์ ์ธ ์๋ณธ ํ์ฉ์ด ๊ธฐ๋๋๋ค.
๋ค๋ง, ๋ณธ ์ฐ๊ตฌ๋ ๋ค์๊ณผ ๊ฐ์ ํ๊ณ๊ฐ ์๋ค. ์ฐ์ ์ฆ๊ถ์ฌ ๋ชจ์ง์ฃผ์ ๋ฐ ์ฐธ์ฌ ๋์ถ๊ฑด์ ์ค์ฌ์ผ๋ก ๋ค์ํ ๊ธ์ต๊ธฐ๊ด์ ๋์ถ๊ฑด์ ๋ถ์ํจ์ผ๋ก์จ, ๊ธ์ต๊ธฐ๊ด์ ์ข
๋ฅ์ ๋ฐ๋ผ ๋ฌ๋ผ์ง ์ ์๋ ์์ฌ๊ฒฐ์ ์์ธ์ ์ฐจ์ด๋ฅผ ๊ท๋ช
ํ์ง ๋ชปํ์๋ค. ๋ํ ๋ค์ํ๊ฒ ๋ถํฌํ๋ ๋ฌผ๊ฑด ๋ฐ ์ฌ์
์ง๋ฅผ ์ฐ๊ตฌํจ์ผ๋ก์จ ๋ถ๋์ฐPF์ ์ผ๋ฐ์ ์ธ ์์ฌ๊ฒฐ์ ์์ธ์ ๊ท๋ช
ํ๋ค๋ ์์๊ฐ ์์ผ๋, ์ฌ์
ํํ ๋ฐ ์ธ๋ถ์ ์ธ ์
์ง์กฐ๊ฑด์ ๋ฐ๋ผ ๋ค๋ฅด๊ฒ ๋ํ๋ ์ ์๋ ์์ฌ๊ฒฐ์ ์์ธ์ ์ฐจ์ด๋ฅผ ๊ท๋ช
ํ์ง๋ ๋ชปํ์๋ค. ๊ทธ๋ฌ๋ฏ๋ก ์ถํ ๊ธ์ต๊ธฐ๊ด์ ํํ์ ๋ฐ๋ฅธ ์์ฌ๊ฒฐ์ ์ ์ฐจ์ด์ , ์ฌ์
๋ฌผ๊ฑด ๋ฐ ์ธ๋ถ์
์ง์์ธ์ด ๊ธ์ต๊ธฐ๊ด์ ์์ฌ๊ฒฐ์ ์ ๋ฏธ์น๋ ์ํฅ์ ๋ํ์ฌ ํ์์ฐ๊ตฌ๊ฐ ํ์ํ ๊ฒ์ผ๋ก ๋ณด์ธ๋ค. ์ด ๊ฐ์ ๊ตฌ์ฒด์ ์์ธ์ ๋ฐํ๋ธ๋ค๋ฉด ์ถํ ๋ฏผ๊ฐ์๋ณธ์ ๋ํ ์ดํด๋ฅผ ๋์ผ ์ ์์ผ๋ฉฐ, ์ด๋ฅผ ํตํด ํ์ ๋ ์์์ ํจ์จ์ ์ด์ฉ์ด๋ผ๋ ์ธก๋ฉด์์ ๋์์ ์ฑ
์ ๋์์ด ๋ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.1. ์๋ก 1
1.1 ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ 1
1.1.1 ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ 1
1.1.2 ์ฐ๊ตฌ์ ๋ชฉ์ 4
1.2 ์ฐ๊ตฌ์ ๋ฒ์ ๋ฐ ๊ตฌ์ฑ 5
1.2.1 ์ฐ๊ตฌ์ ๋ฒ์ 5
1.2.2 ์ฐ๊ตฌ์ ๊ตฌ์ฑ 6
2. ์ด๋ก ๋ฐ ์ ํ์ฐ๊ตฌ ๊ฒํ 8
2.1 ํ๋ก์ ํธํ์ด๋ธ์ค ์ด๋ก ์ ๊ณ ์ฐฐ 8
2.1.1 ๊ฐ๋
๋ฐ ํน์ง 8
2.1.2 ์ด๋ก ์ ์ ๊ทผ 10
2.2 ๊ตญ๋ด ๋ถ๋์ฐ ํ๋ก์ ํธํ์ด๋ธ์ค 15
2.2.1 ๊ธฐ๋ณธ ๊ตฌ์กฐ 15
2.2.2 ๋์ถ ํํ 17
2.2.3 ์ฃผ์ ๋ฆฌ์คํฌ 20
2.3 ๊ตญ๋ด ๋ถ๋์ฐ PF ์ ํ์ฐ๊ตฌ 26
2.4 ์๊ฒฐ 30
3. ์ฐ๊ตฌ๋ฌธ์ ๋ฐ ๊ฐ์ค 32
3.1 ์ฐ๊ตฌ๋ฌธ์ 32
3.2 ์ฐ๊ตฌ๊ฐ์ค 34
4. ๋ถ์์ ํ 36
4.1 ๋ถ์์๋ฃ ๋ฐ ๋ณ์ 36
4.1.1 ๋ถ์์๋ฃ 36
4.1.2 ์ข
์๋ณ์ 36
4.1.3 ๋
๋ฆฝ๋ณ์ 38
4.2 ๋ถ์์ ํ๋ฆ ๋ฐ ๋ฐฉ๋ฒ 42
4.2.1 ๋ถ์์ ํ๋ฆ 42
4.2.2 ๋ถ์๋ฐฉ๋ฒ ๋ฐ ๋ถ์๋ชจํ 43
5. ๋ถ์๊ฒฐ๊ณผ 46
5.1 ๊ธฐ์ดํต๊ณ๋ 46
5.2 ์๊ด๋ถ์ 48
5.3 ๋ค์คํ๊ท๋ถ์ 50
5.3.1 ๋ถ์๊ฒฐ๊ณผ 50
5.3.2 ๋ค์ค๊ณต์ ์ฑ ๊ฒ์ฆ 53
5.4 ๊ฐ์ค๊ฒ์ฆ ๋ฐ ํด์ 56
6. ๊ฒฐ๋ก 59
6.1 ์ฐ๊ตฌ๊ฒฐ๊ณผ ์์ฝ ๋ฐ ์์ฌ์ 59
6.2 ์ฐ๊ตฌ์ ํ๊ณ 61
์ฐธ๊ณ ๋ฌธํ 63
Abstract 68Maste
์ธํ์๊ธฐ ์ดํ ํ๊ตญ ์ฃผ์์์ฅ ๋ถ์ : ๋ชจ๋ฉํ ํฌ์์ ๋ต๊ณผ ๋ฐ๋ํฌ์์ ๋ต์ ์ค์ฌ์ผ๋ก
ํ์๋
ผ๋ฌธ(์์ฌ) --์์ธ๋ํ๊ต ๋ํ์ :๊ฒฝ์ ํ๋ถ,2008.2Maste