오피스 빌딩 등급 결정 요인에 관한 실증연구 - 서울시 오피스 빌딩을 중심으로 -
An Empirical Study on Determinants for Office Building Grade Classification
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The 1997 financial crisis brought about fundamental socio-economic changes throughout the country. In particular, the perception of real estate changed a lot. Stable rental income of office buildings, especially the recognition of investment in real estate has changed. Thus, objective and reliable information on office buildings became more important for related stakeholders : domestic and foreign institutional investors and indirect investors, owners, tenants, etc. In this study we analyzed the determinants of office building grade classification, by using the categorical dependent variable, multi-discriminant analysis and multi-logistic regression. The results of analysis showed Total floor area is the most significant variable in classification. The order of significant variable in classification turns out to be Total floor area(0.589)〉Parking lot(0.213)〉Image(0.147)〉Land Area(0.055)〉Regional(0.037)〉distance to subway(0.006). Classification accuracy of the prediction results for each level varies from 50.6% to 73.5%. These studies will be a very useful information to investors.
Abstract
The 1997 financial crisis brought about fundamental socio-economic changes throughout the country. In particular, the perception of real estate changed a lot. Stable rental income of office buildings, especially the recognition of investment in real estate has changed. Thus, objective and reliable information on office buildings became more important for related stakeholders : domestic and foreign institutional investors and indirect investors, owners, tenants, etc. In this study we analyzed the determinants of office building grade classification, by using the categorical dependent variable, multi-discriminant analysis and multi-logistic regression. The results of analysis showed Total floor area is the most significant variable in classification. The order of significant variable in classification turns out to be Total floor area(0.589)〉Parking lot(0.213)〉Image(0.147)〉Land Area(0.055)〉Regional(0.037)〉distance to subway(0.006). Classification accuracy of the prediction results for each level varies from 50.6% to 73.5%. These studies will be a very useful information to investors.
참고문헌 (12)
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