• Korea Real Estate Review
• /
• v.28 no.1
• /
• pp.65-77
• /
• 2018

This paper analyzes the redemption risk of renters by estimating the LTV and CoLTV with finance market big data (individual credit information) and housing market big data (actual housing transaction data). The analysis showed that when using LTV, the redemption risk was higher in the case of the monthly renter than of the chonsei renter. On the other hand, when using CoLTV, the chonsei renter had a higher redemption risk than the monthly renter. This implies that there is a need to activate a guarantee system, such as risk management using the CoLTV index and the chonsei deposit return guarantee because it is possible for renters to experience losses on their chonsei deposits due to the higher redemption risk. Another implication is that the risk manager should consider the individual characteristics of renters because of the different effects of the redemption risk stemming from the characteristics of the rental contract and the personal characteristics of the renters. CoLTV was just a concept until this study calculated it using housing big data and actual housing transaction information. It helps identify the redemption risk through the characteristics of renters and their contracts.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.3
• /
• pp.499-506
• /
• 2017

Consideration of influential factors through division of capital market sector and interest rate sector to find and resolve the problems in current housing market and leasing market will become an important index to prepare measures for stabilization of housing sales market and housing lease market. Furthermore, a guideline will be provide you with preliminary data using Big Data to prepare for sudden price fluctuation because expected economic crisis, stock market situation, and uncertain future financial crisis can be predicted which may help anticipate real estate price index such as housing sales price index and housing lease price index.

• Korea Real Estate Review
• /
• v.28 no.4
• /
• pp.75-88
• /
• 2018

The purpose of this study is to analyze the effect of the spread on housing mortgage loans. In particular, this study analyzes how the spread has a decisive effect on housing mortgage loans when a structural change occurs in the spread. For the sake of empirical analysis, this study utilizes the housing mortgage loan, housing mortgage loan interest rate, COFIX interest rate, and spread. The period of analysis is from December 2010 to December 2017. Results of the analysis show that there is a statistically significant structural change in the spread and housing mortgage loans (May and June 2015, respectively). It is estimated that the structural change in the spread has an influence on the structural change in housing mortgage loans. In addition, the effect of the spread on housing mortgage loans is larger than the effect of the COFIX interest rate and the housing mortgage loan interest rate. This indicates that the adjustment of the spread is a significant burden on housing mortgage loans. As economic uncertainties both internally and externally are increasing, pressure on interest rate hikes is also increasing. Considering these circumstances, interest rate hikes will be inevitable in the future. If the base interest rate and the spread increase simultaneously at Korea's current economic level, it will obviously lead to an economic recession as the burden on the repayment of principal and interest of housing mortgage loans will increase. Therefore, it is imperative that financial authorities prepare institutional arrangements in order to protect financial consumers by preventing arbitrary calculation of the spread, which would not be objective and would not be transparent from the banks.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.91-113
• /
• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.