• Proceedings of the Safety Management and Science Conference
• /
• 2009.04a
• /
• pp.455-475
• /
• 2009

According to the returns of Housing and business cycle over the period 1992 to 2007, it is a measure of the total volatility faced by investors in Housing properties. First, it isn't a distinct difference from business cycle contrary to U.S. Second, the rise of purchase price in total apartments moves up the consumer price index. According to the cross-sectional dispersion of returns and growth in net operating income (NOI) of apartments, industrial, retail and office properties using panel data for U.S. metropolitan areas over the period 1986 to 2002, it is a measure of the total volatility faced by investors in commercial real estate. To the extent that most of that volatility is difficult to diversify, cross-sectional dispersion may be an appropriate measure of risk.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.3D
• /
• pp.477-487
• /
• 2011

The modified repeat-sales model is employed in this study in order to identify differentiating impacts of time-varying accessibility characteristics on housing price. The results demonstrate that accessibility measures have very differential impacts on housing price over time. The improvement of accessibility through newly built facilities and apartment complex has either increased or decreased housing price. For example, the new subway line 9 has positive impact on housing price nearby, therefore price gap between subway access area and the other parts has been increased. The impact of the wide area facilities such as shopping center and hospital are decreased because they can be used more easily by the new subway line before. However, the small service area facility such as elementary school doesn't lose their impact even though subway accessibility extremely increased. The results imply that new facilities in existing residential site can affect not only housing price but also the other facilities' impact of housing price.

• 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.

• Journal of the Korean Geographical Society
• /
• v.35 no.5
• /
• pp.717-729
• /
• 2000

Housing prices in the Korean housing market dropped at an unprecedented magnitude in 1998 after the economic crisis. With the support of housing policies to boost depressed housing markets, house prices managed to bounce back after the mid-1999. During the period of housing price decline and of its recovery, the degrees of house price changes were not even across the country. The cities could be classified into four groups regarding the differential rates of house price changes. The cities which had higher rates of decrease also had higher rates of increase. On the other hand, some other cities continuously experienced a price fall during the recovery period although the rate of housing price changes were relatively low after the economic crisis. Throught the processes of administering housing market depression due to the crisis of the economy, the cities which could fully redeem the level of house prices in housing markets between the Seoul Metropolitan area and the other parts of the country has been widened.

• Land and Housing Review
• /
• v.2 no.4
• /
• pp.367-377
• /
• 2011

This paper investigates the determination factors' variation of real estate price after sub-prime financial crisis, in korea, using a VAR model. The model includes land price, housing price, housing rent (Jensei) price, which time period is from 2000:1Q to 2011:2Q and uses interest rate, real GDP, consumer price index, KOSPI, the number of housing construction, the amount of land sales and practices to impulse response and variance decomposition analysis. Data cover two sub-periods and divided by 2008:3Q that occurred the sub-prime crisis; one is a period of 2000:1Q to 2008:3Q, the other is based a period of 2000:1Q to 2011:2Q. As a result, Comparing sub-prime crisis before and after, land price come out that the influence of real GDP is expanding, but current interest rate's variation is weaken due to the stagnation of current economic status and housing construction market. Housing price is few influenced to interest rate and real GDP, but it is influenced its own variation or Jensei price's variation. According to the Jensei price's rapidly increasing in nowadays, housing price might be increasing a rising possibility. Jensei price is also weaken the influence of all economic index, housing price, comparing before sub-prime financial crisis and it is influenced its own variation the same housing price. As you know, real estate price is weakened market basic value factors such as, interest rate, real GDP, because it is influenced exogenous economic factors such as population structural changes. Economic participators, economic officials, consumer, construction supplyers need to access an accurate observation about current real estate market and economic status.

• Journal of Digital Convergence
• /
• v.18 no.11
• /
• pp.43-49
• /
• 2020

The purpose of this study was to analyze factors affecting the housing market by setting household loans and M2, which are liquidity indicators, and the industrial production index reflecting economic fluctuations, as variables, and to determine the effect of expected housing prices. An empirical analysis was conducted based on the data from January 2005 to May 2020, and the HP filter was applied to the real house price as the expected house price variable. As a result of the analysis, it was found that real household loans, real M2, and so on, had an effect on house prices, and expectations for past house prices and house prices increased the house prices in the present period. These results show that even though the liquidity expansion is aimed at revitalizing the economy, it can affect housing prices as well.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.5
• /
• pp.3260-3269
• /
• 2014

The construction industry has strong ties with other industries, and so construction company insolvency also has a strong influence on other industries. Prediction models addressing the insolvency of construction company have been well studied. Although factors contributing to insolvency must precede those of predictions of insolvency, studies on these contributing factors are limited. The purpose of this study is to analyze the influence of changes in the housing market on construction company insolvency by using the Vector Error Correction Model. Construction companies were divided into two groups, and the expected default frequency(EDF), which indicates insolvency of each company was measured through the KMV model. The results verified that 10 largest construction companies were in a better financial condition compared to relatively smaller construction companies. As a result of conducting impulse response analysis, the EDF of large companies was found to be more sensitive to housing market change than that of small- and medium-sized construction companies.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.4D
• /
• pp.523-531
• /
• 2008

This study tried to find differences between housing lotting prices and sale prices owing to new multi-family housing price regulation. As the results of this study, they are as follows; First, this study shows housing market in Busan has a preferences of new housing which has a new housing form differing from the existing housing form. For example, the mixed-use apartment with higher stories shows steeper incline than the apartments with the existing forms. Second, the new housing prices are affected by the information that affect the price of the old existing housing. They are rates of green area of an apartment complex, the number of household, accessibility to downtown Busan and etc.. They are also confirmed factors that affect a rise of used-housing price in other studies. Third, brand value of apartments affects new housing prices. For example, if the major construction companies build the new apartment, it shows a rising trend than any other housing. Therefore, the local construction companies are expected to be put on a disadvantage places than major construction companies. Fourth, the lotting prices are the most important cause that lead to rise the new housing prices. Accordingly, the present lotting prices are expected that upward tendency the purchasing prices of the new housing will not continue, because the lotting prices have risen since the government removed lotting price regulations and exceeded the level of used-housing prices. And it denote that importance of housing sub-market which indicates rates of old existing housing market rising, frist preference Gu, second preference Gu, rate of multi-family housing.

• 월간 기계설비
• /
• s.321
• /
• pp.34-37
• /
• 2017

미국의 금리인상에 따른 국내 금리 상승 압박과 정부의 잇따른 부동산 규제, 집단 대출 심사 강화 등 금융대출 조이기로 부동산 시장이 '봄 같지 않은 봄'을 맞고 있다. 더구나 5월 9일 조기 대통령선거를 앞두고 정국불안과 경제 불안정 등으로 매매와 분양시장도 활기를 띠지 못하고 있다. 지난 3개월 동안 분양 실적이나 청약경쟁률도 지난해에 비해 떨어졌다. 부동산 시장 불확실성이 높아지고 있다는 방증이다. 이에 따라 투자자나 실수요자 모두 기대감을 낮추고 옥석을 가리는 안목을 갖고 부동산 시장을 들여다 봐야 한다는 지적이다.

• Korea Real Estate Review
• /
• v.28 no.2
• /
• pp.21-34
• /
• 2018

The purpose of this study is to investigate the effect of macro-finance variables on the correlation between the housing and stock markets because understanding the nature of time-varying correlations between different assets has important implications on portfolio allocation and risk management. Thus, we adopted the AG-DCC GARCH model to obtain time-varying, conditional correlations. Our sample ranged from January 2004 to November 2017. Our empirical result showed that the coefficients on asymmetric correlation were significantly positive, implying that correlations between the housing and stock markets were significantly higher when changes in the housing price and stock returns were negative. This finding suggested that the housing market has less hedging potential during a stock market downturn, when such a hedging strategy might be necessary. Based on the regression analysis, we found that the term spread had a significantly negative effect on correlations, while the credit spread had a significantly positive effect. This result could be interpreted by the risk premium effect.