Sine the IMF crisis in 1997, the office market has been internalized, and advanced, also being in flux. The purpose of this study is to analyze statistically the price-making factors influencing on the sales price of an office.
The preceding analysis on an office mostly put the rent of office into a dependent variable, however, I have put the office sales prices into a dependent variable. I have also classified independent variables into inherent characteristics of real property including the rent and economic factors. In addition, I have tried to apply the Regression Analysis considering detailed other variables classified by district (central district, Gangnam district, Yeouido·Mapo district, Seoul other districts), ownership type (direct investment by a domestic owner, direct investment by a foreign owner, and indirect investment by a domestic or foreign owner), and type of building (general building, partitioned building)
I have restricted the extent of location into central district (CBD), Gangnam district (KBD), Yeouido·Mapo district (YBD), and Seoul other district. For samples, I have selected offices over GFA 16,529 sq m which have been transacted within these four districts and being and used as the highest and best use. Also, the extent of time has been restricted by sales comparables transacted between year 2003 and the third quarter of 2009. For this study, I have merely chosen 82 cases available to be adopted as an independent variable out of more than 200 cases provided by DTZ Korea for this study.
As a result, influential price-making factors are FAR (Floor area ratio), GALP (Government Announced Land Price) of the year of transaction, the converted Jeonsei price, KOSPI, and the growth rate of economy, but the number of building's storey is not a factor influencing on price.
This study has been particularly to analyze interrelation of the rent as a dependent variable and the sale price, and to adopt general buildings and partitioned buildings as an independent variable.
The limitations of this study are two; insufficient data and long time-interval. Provided that these two limitations are solved, this office model can be generalized and systematized.