2023, Vol.33, No.3

Korea Real Estate Review 2023 KCI Impact Factor : 0.56

pISSN : 2092-5395 / eISSN : 2733-8339
https://journal.kci.go.kr/krer

This study examines the effect of the real estate industry on other industries as input and output resources. We define the real estate industry based on the two- and three-digit industry classification system as the recent real estate industry expands into other interdisciplinary business sectors. We also aggregate the sub-sector of the real estate–related industry into the broad real estate–related industry. We analyze input–out tables to find the spillover effect and value chain to other related industries by utilizing a total of 17 annual industry input–output tables from the Bank of Korea. This study also extends the spillover effect of the real estate industry along with a fluctuation of macroeconomic conditions. We find that the magnitude of the economic effect of the real estate industry on other industries is large when we classify the real estate industry into the two-digit industry classification system except for the analysis of the additive value impact. We also examine the impulse response analysis (IRA) by utilizing vector autoregression analysis. IRA indicates that macroeconomic variables affect the real estate industry and drive the same directional spillover effect on other industries. Our analysis segregates the national input–output analysis into the regional input–output analysis to identify the economic effect of real estate market fluctuation of the Seoul Metropolitan Area on other regions.

This study attempts to regionalize the Seoul office market by focusing on the investors’ perspectives. The study uses the transaction price data announced by the Ministry of Land, Infrastructure, and Transport and applies the Fuzzy C-Means algorithms, which are machine learning–based clustering techniques. The study found the following: First, as a result of comparing various algorithms, the Spatial Fuzzy C-Means algorithm performs the best clustering. Second, the three major office districts in Seoul form clusters differentiated from other areas. Third, the number of Dongs belonging to each district is smaller than the general consensus. The traditional business district (CBD) includes only parts of Jongno-Gu and Jung-Gu, the Yeouido business district (YBD) is composed of only Yeouido-Dong, and the Gangnam business district (GBD) comprises not many Dongs of Gangnam-Gu and Seocho-Gu. Fourth, by weakening the criteria, the clustering results in different spatial expansions by district. CBD has expanded to Seodaemun-Gu and Yongsan-Gu, whereas YBD has not expanded to Mapo-Gu or Yeongdeungpo. In particular, GBD expands to a considerable number of Dongs in Gangnam-Gu and Seocho-Gu. Fifth, the range of each district changes by period. When the office price increases rapidly, the range of each district is widened, and vice versa. These results show that, unlike the general perception of office districts, the CBD is expanding to Seodaemun-Gu and Yongsan-Gu. Moreover, Yeouido and nearby Mapo-Gu and Yeongdeungpo are not homogenous enough to be viewed as one district, and GBD is not linear but rectangular. Furthermore, Songpa-Gu does not form a spatially connected district with Seocho-Gu and Gangnam-Gu.

Age-period-cohort (APC) analysis is well known for its suitability in the life cycle, aging, and long-term trend studies, with the cohort effect explaining social phenomena as the influx of new generations. The analysis can be divided into two components: the cohort effect and the period effect. In this study, a Bayesian nonlinear model was introduced to estimate the home-ownership rate of the baby boomer generation based on APC analysis. Three types of data were used to ensure the robustness of the results, taking into account the APC linear relationship and findings from previous studies. In some models, age dummies were included to capture the home-ownership rate intuitively. Additionally, a nonlinear model was applied to the finance and labor panels, which maintained the same sample over time, to isolate each effect of APC. This novel approach demonstrated the potential for improving the identification problem of APC in comparison to traditional linear analysis. The study found distinct differences in the housing consumption pattern of the baby boomer generation compared with previous generations. The baby boomers now constitute a significant portion of the elderly population, implying a shift in the implications for housing stability. By addressing the identification issues, the Bayesian nonlinear model enhances the utility of APC analysis and contributes to a great understanding of the differences in housing preference systems among generations.