15 research outputs found

    Analysis on Decision Making Factors of Financial Institutions Participating in Real Estate Project Finance

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€,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 studiesโ€”the 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 3.3ใŽก) 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.๋ถ€๋™์‚ฐPF(Project Finance)๋Š” ๋ถ€๋™์‚ฐ ๊ฐœ๋ฐœ์‚ฌ์—…์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ฏธ๋ž˜ํ˜„๊ธˆํ๋ฆ„์„ ๋‹ด๋ณด๋กœ ํ•ด๋‹น ํ”„๋กœ์ ํŠธ์˜ ์ž๊ธˆ์„ ์กฐ๋‹ฌํ•˜๋Š” ๋ฐฉ์‹์ด๋‹ค. ์ตœ๊ทผ ๋ช‡ ๋…„๊ฐ„ ์‹œ์žฅ์˜ ์™ธํ˜•์ด ์„ฑ์žฅ ํ•˜์˜€์œผ๋ฉฐ, ๋น„ ์€ํ–‰๊ธฐ๊ด€ ์ฐธ์—ฌ์ฆ๊ฐ€, ์ƒ๋Œ€์ ์ธ ์‹œ๊ณต์‚ฌ์˜ ์ฑ„๋ฌด๋ณด์ฆ๋น„์œจ ๊ฐ์†Œ ๋“ฑ ๋‚ด์ ์œผ๋กœ๋„ ๋ณ€ํ™”ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋กœ ์ตœ๊ทผ ๊ธˆ์œต๊ธฐ๊ด€๋“ค์˜ ๋ถ€๋™์‚ฐPF ์ฐธ์—ฌ ์˜์‚ฌ๊ฒฐ์ •์š”์ธ์—์„œ ๊ธˆ์œต๊ตฌ์กฐ์˜ ์•ˆ์ •์„ฑ ๋ฐ ํ”„๋กœ์ ํŠธ ์‚ฌ์—…์„ฑ์ด ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ์ปค์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ์— ๋”ฐ๋ผ ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธˆ์œต๊ตฌ์กฐ ๋ฐ ์‚ฌ์—…์„ฑ์ด ๋ถ€๋™์‚ฐPF์ฐธ์—ฌ ๊ธˆ์œต๊ธฐ๊ด€์˜ ์˜์‚ฌ๊ฒฐ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์š”์ธ์„ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ตญ๋‚ด A์ฆ๊ถŒ์‚ฌ์—์„œ 2016๋…„์—์„œ 2019๋…„ ์‚ฌ์ด ๋ชจ์ง‘์ฃผ์„  ํ˜น์€ ์ง์ ‘๋Œ€์ฃผ๋กœ ์ฐธ์—ฌํ•œ PF๋Œ€์ถœ 220๊ฑด์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ๊ทœ๋ช…๋˜์ง€ ๋ชปํ•œ ์š”์ธ์„ ๋ฐํ˜€๋‚ด๊ธฐ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” ํ”„๋กœ์ ํŠธ ๋งค์ถœ ๋Œ€๋น„ PF๋Œ€์ถœ ๋น„์œจ, ๋Œ€์ถœ๊ธฐ๊ด€์ด ์ฃผ ์ƒํ™˜์žฌ์›์œผ๋กœ ํ•˜๋Š” ๋‹ด๋ณด๋ฌผ๊ฑด์˜ ๋‹จ์œ„๋ฉด์ ๋‹น(3.3ใŽก/์ „์šฉ)๊ฐ€๊ฒฉ, ๋Œ€์ถœ์›๋ฆฌ๊ธˆ ์ƒํ™˜์ด ๊ฐ€๋Šฅํ•ด์ง€๋Š” ๋ถ„์–‘๋ฅ ์ˆ˜์ค€, ํŠน์ • ๊ธˆ์œต๊ธฐ๊ด€์˜ ๋Œ€์ถœ์›๋ฆฌ๊ธˆ ์ƒํ™˜์šฐ์„ ์ˆœ์œ„๋ฅผ ๋ณ€์ˆ˜๋กœ ๋ฐ˜์˜ํ•˜์˜€๋‹ค. ๋ถ„์„๋ฐฉ๋ฒ•์€ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„(Multiple Regression Analysis)์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ๊ธˆ์œต๋น„์šฉ์„ ์ข…์†๋ณ€์ˆ˜๋กœ ๋ถ€๋™์‚ฐPF ์ง„ํ–‰ ์‹œ ๊ธˆ์œต๊ธฐ๊ด€์ด ์ธ์‹ํ•˜๋Š” ๋ฆฌ์Šคํฌ์š”์ธ์„ ๋…๋ฆฝ๋ณ€์ˆ˜๋กœ ํ•˜๋Š” ๋ชจํ˜•์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ์ข…์†๋ณ€์ˆ˜์ธ ๊ธˆ์œต๋น„์šฉ์€ ์ด ๊ธˆ์œต๋น„์šฉ(All-in Cost) ์Šคํ”„๋ ˆ๋“œ(Spread)๋ฅผ ์„ ์ •ํ•˜์˜€๊ณ , ๋…๋ฆฝ๋ณ€์ˆ˜๋Š” ์œ„์—์„œ ์–ธ๊ธ‰ํ•œ ๋ณ€์ˆ˜ ์™ธ ์‚ฌ์—… ์ง„ํ–‰๊ฒฝ๊ณผ์— ๋”ฐ๋ฅธ ๋ฆฌ์Šคํฌ ์š”์ธ์ธ ํ† ์ง€๋งค์ž… ๋ฐ ์ธํ—ˆ๊ฐ€๋ฆฌ์Šคํฌ, ์ค€๊ณต๋ฆฌ์Šคํฌ, ๋ถ„์–‘๋ฆฌ์Šคํฌ, ๋Œ€์ถœ์กฐ๊ฑด์— ํ•ด๋‹นํ•˜๋Š” ๋Œ€๋ฆฌ๋ณ€์ˆ˜๋“ค์„ ํ†ต์ œ๋ณ€์ˆ˜๋กœ ๋ฐ˜์˜ํ•˜์˜€๋‹ค. ์ตœ์ ํ™”๋œ ๋ชจํ˜•๊ตฌ์ถ•์„ ์œ„ํ•ด ํ›„๋ฐฉ์ œ๊ฑฐ๋ฒ•(Backward elimination method)์„ ์ ์šฉํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ ํ”„๋กœ์ ํŠธ ๋งค์ถœ ๋Œ€๋น„ PF๋Œ€์ถœ ๋น„์œจ์ด ๋†’์•„์งˆ์ˆ˜๋ก ๊ทธ๋ฆฌ๊ณ  ์ฃผ ์ƒํ™˜์žฌ์›์œผ๋กœ ํ•˜๋Š” ๋‹ด๋ณด๋ฌผ๊ฑด์˜ 3.3ใŽก๋‹น(์ „์šฉ)๊ฐ€๊ฒฉ์ด ๋†’์„์ˆ˜๋ก ๊ธˆ์œต๋น„์šฉ์ด ๋†’์•„์กŒ๋‹ค. ๋˜ํ•œ ๋Œ€์ถœ์›๋ฆฌ๊ธˆ ์ƒํ™˜์ด ๊ฐ€๋Šฅํ•ด์ง€๋Š” ๋ถ„์–‘๋ฅ (Exit๋ถ„์–‘๋ฅ )์ด ๋†’์•„์ง€๊ณ  ํŠน์ • ๊ธˆ์œต๊ธฐ๊ด€์˜ ๋Œ€์ถœ์›๋ฆฌ๊ธˆ ์ƒํ™˜์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋ฐ€๋ ค๋‚ ์ˆ˜๋ก ๊ธˆ์œต๋น„์šฉ์ด ์˜ฌ๋ผ๊ฐ€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ํ†ต์ œ๋ณ€์ˆ˜๋Š” ์ธํ—ˆ๊ฐ€์œ ๋ฌด ๋ฐ ๋ณด์ฆ๊ธฐ๊ด€ ๋“ฑ๊ธ‰ ์™ธ์— ๋ชจ๋‘ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๋ณ€์ˆ˜ ๋Œ€๋ถ€๋ถ„์ด ์„ ํ–‰์—ฐ๊ตฌ์—์„œ ๊ทœ๋ช…ํ•œ ๊ฒƒ๊ณผ ๋™์ผํ•œ ๋ถ€ํ˜ธ๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฏผ๊ฐ„์ž๋ณธ ํ™œ์šฉ์ธก๋ฉด์—์„œ ๋„์‹œ์ •์ฑ…์— ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํ•จ์˜๋ฅผ ๊ฐ–๋Š”๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๊ธˆ์œต๊ธฐ๊ด€๋“ค์€ ์‚ฌ์—…์„ฑ ๋ฐ ๊ธˆ์œต๊ตฌ์กฐ์˜ ์•ˆ์ •์„ฑ์„ ๋ฐ”ํƒ•์œผ๋กœ ์˜์‚ฌ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ ๊ทน์ ์ธ ๋ฏผ๊ฐ„์ž๋ณธ ํ™œ์šฉ์„ ์œ„ํ•ด์„œ๋Š” ๊ณต๊ณต๋ถ€๋ฌธ์ด ์šฉ๋„, ์šฉ์ ๋ฅ  ๋“ฑ ๊ทœ์ œ ์™„ํ™”๋ฅผ ํ†ตํ•ด ์ผ์ •๋ถ€๋ถ„ ์‚ฌ์—…์„ฑ์„ ๋ณด์žฅํ•  ํ•„์š”๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ๋˜ํ•œ ๊ธˆ์œต๊ตฌ์กฐ์˜ ์ธก๋ฉด์—์„œ ๋ฏผ๊ฐ„์ž๋ณธ์˜ ์ƒํ™˜ ์•ˆ์ •์„ฑ์„ ๋†’์ผ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ณต๊ณต์ด ์ƒํ™˜์šฐ์„ ์ˆœ์œ„๊ฐ€ ๋‚ฎ์€ ํ›„์ˆœ์œ„ ๋˜๋Š” ์—์ฟผํ‹ฐ(Equity)์— ์ฐธ์—ฌํ•œ๋‹ค๋ฉด ์„ ์ˆœ์œ„์— ์ฐธ์—ฌํ•˜๋Š” ๊ธˆ์œต๊ธฐ๊ด€๋“ค์˜ ์œ„ํ—˜์ธ์‹์ด ๋‚ฎ์•„์ ธ ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์€ ๊ธˆ์œต๋น„์šฉ์œผ๋กœ ๋ฏผ๊ฐ„์ž๋ณธ์„ ์กฐ๋‹ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ์ด ๊ฒฝ์šฐ ๊ณต๊ณต์€ ํ•œ์ •๋œ ์˜ˆ์‚ฐ์„ ๋‹ค์–‘ํ•œ ์‚ฌ์—…์— ๋ถ„์‚ฐํ•˜์—ฌ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๊ณ  ๊ฒฝ์šฐ์— ๋”ฐ๋ผ ์‚ฌ์—…์„ฑ๊ณต์— ๋”ฐ๋ฅธ ์ด์ตํ–ฅ์œ ๋„ ๊ฐ€๋Šฅํ•˜๋ฏ€๋กœ ๋ณด๋‹ค ํšจ์œจ์ ์ธ ์ž๋ณธ ํ™œ์šฉ์ด ๊ธฐ๋Œ€๋œ๋‹ค. ๋‹ค๋งŒ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์šฐ์„  ์ฆ๊ถŒ์‚ฌ ๋ชจ์ง‘์ฃผ์„  ๋ฐ ์ฐธ์—ฌ ๋Œ€์ถœ๊ฑด์„ ์ค‘์‹ฌ์œผ๋กœ ๋‹ค์–‘ํ•œ ๊ธˆ์œต๊ธฐ๊ด€์˜ ๋Œ€์ถœ๊ฑด์„ ๋ถ„์„ํ•จ์œผ๋กœ์จ, ๊ธˆ์œต๊ธฐ๊ด€์˜ ์ข…๋ฅ˜์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋Š” ์˜์‚ฌ๊ฒฐ์ • ์š”์ธ์˜ ์ฐจ์ด๋ฅผ ๊ทœ๋ช…ํ•˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋‹ค์–‘ํ•˜๊ฒŒ ๋ถ„ํฌํ•˜๋Š” ๋ฌผ๊ฑด ๋ฐ ์‚ฌ์—…์ง€๋ฅผ ์—ฐ๊ตฌํ•จ์œผ๋กœ์จ ๋ถ€๋™์‚ฐPF์˜ ์ผ๋ฐ˜์ ์ธ ์˜์‚ฌ๊ฒฐ์ •์š”์ธ์„ ๊ทœ๋ช…ํ–ˆ๋‹ค๋Š” ์˜์˜๊ฐ€ ์žˆ์œผ๋‚˜, ์‚ฌ์—…ํ˜•ํƒœ ๋ฐ ์„ธ๋ถ€์ ์ธ ์ž…์ง€์กฐ๊ฑด์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ์˜์‚ฌ๊ฒฐ์ •์š”์ธ์˜ ์ฐจ์ด๋ฅผ ๊ทœ๋ช…ํ•˜์ง€๋Š” ๋ชปํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ถ”ํ›„ ๊ธˆ์œต๊ธฐ๊ด€์˜ ํ˜•ํƒœ์— ๋”ฐ๋ฅธ ์˜์‚ฌ๊ฒฐ์ •์˜ ์ฐจ์ด์ , ์‚ฌ์—…๋ฌผ๊ฑด ๋ฐ ์„ธ๋ถ€์ž…์ง€์š”์ธ์ด ๊ธˆ์œต๊ธฐ๊ด€์˜ ์˜์‚ฌ๊ฒฐ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•˜์—ฌ ํ›„์†์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ด ๊ฐ™์€ ๊ตฌ์ฒด์  ์š”์ธ์„ ๋ฐํ˜€๋‚ธ๋‹ค๋ฉด ์ถ”ํ›„ ๋ฏผ๊ฐ„์ž๋ณธ์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ํ•œ์ •๋œ ์ž์›์˜ ํšจ์œจ์  ์ด์šฉ์ด๋ผ๋Š” ์ธก๋ฉด์—์„œ ๋„์‹œ์ •์ฑ…์— ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1.1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.1.2 ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 1.2 ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๊ตฌ์„ฑ 5 1.2.1 ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 5 1.2.2 ์—ฐ๊ตฌ์˜ ๊ตฌ์„ฑ 6 2. ์ด๋ก  ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  8 2.1 ํ”„๋กœ์ ํŠธํŒŒ์ด๋‚ธ์Šค ์ด๋ก ์  ๊ณ ์ฐฐ 8 2.1.1 ๊ฐœ๋… ๋ฐ ํŠน์ง• 8 2.1.2 ์ด๋ก ์  ์ ‘๊ทผ 10 2.2 ๊ตญ๋‚ด ๋ถ€๋™์‚ฐ ํ”„๋กœ์ ํŠธํŒŒ์ด๋‚ธ์Šค 15 2.2.1 ๊ธฐ๋ณธ ๊ตฌ์กฐ 15 2.2.2 ๋Œ€์ถœ ํ˜•ํƒœ 17 2.2.3 ์ฃผ์š” ๋ฆฌ์Šคํฌ 20 2.3 ๊ตญ๋‚ด ๋ถ€๋™์‚ฐ PF ์„ ํ–‰์—ฐ๊ตฌ 26 2.4 ์†Œ๊ฒฐ 30 3. ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ๊ฐ€์„ค 32 3.1 ์—ฐ๊ตฌ๋ฌธ์ œ 32 3.2 ์—ฐ๊ตฌ๊ฐ€์„ค 34 4. ๋ถ„์„์˜ ํ‹€ 36 4.1 ๋ถ„์„์ž๋ฃŒ ๋ฐ ๋ณ€์ˆ˜ 36 4.1.1 ๋ถ„์„์ž๋ฃŒ 36 4.1.2 ์ข…์†๋ณ€์ˆ˜ 36 4.1.3 ๋…๋ฆฝ๋ณ€์ˆ˜ 38 4.2 ๋ถ„์„์˜ ํ๋ฆ„ ๋ฐ ๋ฐฉ๋ฒ• 42 4.2.1 ๋ถ„์„์˜ ํ๋ฆ„ 42 4.2.2 ๋ถ„์„๋ฐฉ๋ฒ• ๋ฐ ๋ถ„์„๋ชจํ˜• 43 5. ๋ถ„์„๊ฒฐ๊ณผ 46 5.1 ๊ธฐ์ดˆํ†ต๊ณ„๋Ÿ‰ 46 5.2 ์ƒ๊ด€๋ถ„์„ 48 5.3 ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„ 50 5.3.1 ๋ถ„์„๊ฒฐ๊ณผ 50 5.3.2 ๋‹ค์ค‘๊ณต์„ ์„ฑ ๊ฒ€์ฆ 53 5.4 ๊ฐ€์„ค๊ฒ€์ฆ ๋ฐ ํ•ด์„ 56 6. ๊ฒฐ๋ก  59 6.1 ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์š”์•ฝ ๋ฐ ์‹œ์‚ฌ์  59 6.2 ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 61 ์ฐธ๊ณ ๋ฌธํ—Œ 63 Abstract 68Maste

    ์™ธํ™˜์œ„๊ธฐ ์ดํ›„ ํ•œ๊ตญ ์ฃผ์‹์‹œ์žฅ ๋ถ„์„ : ๋ชจ๋ฉ˜ํ…€ ํˆฌ์ž์ „๋žต๊ณผ ๋ฐ˜๋Œ€ํˆฌ์ž์ „๋žต์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฒฝ์ œํ•™๋ถ€,2008.2Maste
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