The purpose of this study is to study the price factors of small and medium-sized office buildings located in the three main districts in Seoul. Office buildings are one of the most profitable real estates, with stable rental income during the operating period and capital gains in the future resale ...
The purpose of this study is to study the price factors of small and medium-sized office buildings located in the three main districts in Seoul. Office buildings are one of the most profitable real estates, with stable rental income during the operating period and capital gains in the future resale are attracting attention as an asset owner's investment target. It is recognized as an investment destination for rational asset management because it prefers real assets due to the drop in interest rates of institutional fixed rate products and the instability of the stock market. However, since it is difficult to collect and analyze data because the sales of office buildings are not made public, it is important to have expertise in the relevant field and to identify factors affecting the selling price.
In order to analyze the pricing factors of small and medium-sized office buildings, the target scope of this study was to define small and medium-sized office buildings with a total floor area of 33,000㎡ or less, and the spatial scope was limited to Center Building District, Gangnam Building District and Yeouido Building District in Seoul. The temporal range was divided into two and applied. First, for qualitative analysis, brainstorming and surveys were conducted for real estate experts from December 2019 to February 2020. Second, for quantitative analysis, the analysis was conducted based on sales data of general buildings actually traded in the real estate market in Seoul from 2015 to 2019.
As a qualitative analysis method, this study conducted AHP analysis and Fuzzy analysis for experts in real estate-related fields, and as a result, the top 10 factors influencing the sales price of small and medium-sized office buildings were the distance from public transportation facilities and the number of enterprises. The importance was shown in the following order: tenant composition, distance from the central facility, width and surface of the road, real estate regulations, suitability of customers' liquidity, use of nearby land, building size, and economically active population.
As a quantitative analysis method, OLS analysis and 2-SLS analysis based on the hedonic price model were performed. As a result of the OLS analysis, the variables influencing the sales price of small and medium-sized office buildings were in the order of land price, land area, use district, regional area, front road width, subway station travel time, floor area ratio. In the model, the use district, regional area, front road width, and subway station travel time were followed, and in the second-stage estimation model, the published land price, subway station travel time, floor area ratio, elevator number, total number of parked cars, elapsed years, and coverage ratio were shown.
When comparing the results of qualitative and quantitative analysis, it was found that the important factors influencing the pricing of small and medium-sized office buildings have a close relationship with each other without departing significantly. However, since the group of experts judged that maximizing rental income by recognizing small and medium-sized office buildings as profitable real estate was an important factor in raising the price, the number of tenants and the number of companies were higher in the priority ranking. In addition, experts recognized that the government's real estate development regulations or real estate funds restricting demand have a significant effect on the pricing of small and medium-sized office buildings, but it is difficult to collect and analyze quantitative data. Analysis was limited.
In spite of these limitations, however, this study compared and reviewed only the quantitative analysis method in the previous precedent study with qualitative analysis method. The purpose of this study is to provide information to help rational decision making by borrowing the priority of the derived pricing factors.
The purpose of this study is to study the price factors of small and medium-sized office buildings located in the three main districts in Seoul. Office buildings are one of the most profitable real estates, with stable rental income during the operating period and capital gains in the future resale are attracting attention as an asset owner's investment target. It is recognized as an investment destination for rational asset management because it prefers real assets due to the drop in interest rates of institutional fixed rate products and the instability of the stock market. However, since it is difficult to collect and analyze data because the sales of office buildings are not made public, it is important to have expertise in the relevant field and to identify factors affecting the selling price.
In order to analyze the pricing factors of small and medium-sized office buildings, the target scope of this study was to define small and medium-sized office buildings with a total floor area of 33,000㎡ or less, and the spatial scope was limited to Center Building District, Gangnam Building District and Yeouido Building District in Seoul. The temporal range was divided into two and applied. First, for qualitative analysis, brainstorming and surveys were conducted for real estate experts from December 2019 to February 2020. Second, for quantitative analysis, the analysis was conducted based on sales data of general buildings actually traded in the real estate market in Seoul from 2015 to 2019.
As a qualitative analysis method, this study conducted AHP analysis and Fuzzy analysis for experts in real estate-related fields, and as a result, the top 10 factors influencing the sales price of small and medium-sized office buildings were the distance from public transportation facilities and the number of enterprises. The importance was shown in the following order: tenant composition, distance from the central facility, width and surface of the road, real estate regulations, suitability of customers' liquidity, use of nearby land, building size, and economically active population.
As a quantitative analysis method, OLS analysis and 2-SLS analysis based on the hedonic price model were performed. As a result of the OLS analysis, the variables influencing the sales price of small and medium-sized office buildings were in the order of land price, land area, use district, regional area, front road width, subway station travel time, floor area ratio. In the model, the use district, regional area, front road width, and subway station travel time were followed, and in the second-stage estimation model, the published land price, subway station travel time, floor area ratio, elevator number, total number of parked cars, elapsed years, and coverage ratio were shown.
When comparing the results of qualitative and quantitative analysis, it was found that the important factors influencing the pricing of small and medium-sized office buildings have a close relationship with each other without departing significantly. However, since the group of experts judged that maximizing rental income by recognizing small and medium-sized office buildings as profitable real estate was an important factor in raising the price, the number of tenants and the number of companies were higher in the priority ranking. In addition, experts recognized that the government's real estate development regulations or real estate funds restricting demand have a significant effect on the pricing of small and medium-sized office buildings, but it is difficult to collect and analyze quantitative data. Analysis was limited.
In spite of these limitations, however, this study compared and reviewed only the quantitative analysis method in the previous precedent study with qualitative analysis method. The purpose of this study is to provide information to help rational decision making by borrowing the priority of the derived pricing factors.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.