• KDI Journal of Economic Policy
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• v.36 no.4
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• pp.171-205
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• 2014

This paper investigates the manner in which house prices affect macroeconomic variables through a house price channel by applying the method of Iacoviello (2005) to Korean data, and establishing a DSGE model with complementarity. This paper found that higher LTV ratio coupled with stronger complementarity results in the co-movement in both consumption and housing. For instance, the results show that when the LTV ratio and complementarity stands respectively at 50% and 0.42, an 1% rise in house prices increases consumption by 0.057%, and when the complementarity parameter increases to 0.52 with LTV remains unchanged at 50%, consumption rises by 0.047% per 1% increase in house prices. An increase in house prices leads credit constraints for borrowers to become more loose as value of a house rises as a collateral. The increase in household credit enables more consumer spending, eventually leading to increased consumption. A key link in which house prices are connected to macroeconomic variables is change in consumption. To put it simply, a rise in house prices leads to an increase in consumption, which consequently impacts the overall macro-economy. At this point, complementarity is found, in that the elasticity of intra-temporal substitution between housing and consumption is estimated at 0.42, which plays an important role in the house price channel by amplifying the effects of house prices on consumption.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.175-182
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• 2013

In this paper, the simulation model of house price is presented on the basis of pricing mechanism between the demand and the supply of apartments in seoul. The algorithm of house price simulation model for calculating the rate of price over time includes feedback control theory. The feedback control theory consists of stock variable, flow variable, auxiliary variable and constant variable. We suggest that the future price of apartment is simulated using mutual interaction variables which are demand, supply, price and parameters among them. In this paper we considers three items which include the behavior of apartment price index, the size of demand and supply, and the forecasting of the apartment price in the future economic scenarios. The proposed price simulation model could be used in public needs for developing a house price regulation policy using financial and non-financial aids. And the quantitative simulation model is to be applied in practice with more specific real data and Powersim Software modeling tool.

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

• International conference on construction engineering and project management
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• 2007.03a
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• pp.366-375
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• 2007

Our nation has a problem with discrimination of income distribution and inefficient of resources distribution caused by real estate price rising from a sudden economy growth and industrialization. Specially, in recent years, there is a great disparity of condominium price between the north and south of the Han river. Because the housing price is deciede by the immanent value of a house and neighborhood effects of the regional where the house is situated, the housing price is occurred difference. In this study, I analyzed the differences of housing price determinants about condominium developments in the old and new residential areas, and found the important factors that affect the condominium price using Structural Equation Modeling(SEM) The purpose of study is to analyze the influence of various factors of housing price. Also, this study tried to predict real estate market and to establish previous effective real estate policy.

• Journal of Environmental Policy
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• v.12 no.1
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• pp.3-35
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• 2013

Through Influence between the qualitative Environment-friendly Planning Elements (Independent variable) of eighty Apartment House on the four boundary areas in Metropolitan 2th Newtown as representative case of domestic Composition case of Eco-City and the quantitative deal price of Apartment House (dependent variable), it was analyzed empirically with use of the statistical measuring technique. This study has been meaning that it was groped the scheme of quantitative measuring analysis on environmental economics through the developmental extention for he classification system of the qualitative Environment-friendly Planning Elements on urban & architecture. And, As the result of empirical analysis, it was confirmed empirically that the economical induction effect was much more weight and worthy of notice through Environment-friendly Planning Elements in Metropolitan 2th Newtown influenced to the deal price of Apartment House, particularly it was found noticeable meaning that Environment-friendly Planning Elements of applicabled the green new techniqe like New & Renewable Energy were used already and it was influenced much to the deal price of Apartment House.

• Korea Real Estate Review
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• v.27 no.3
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• pp.41-50
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• 2017

Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications. This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.

• KDI Journal of Economic Policy
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• v.29 no.1
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• pp.113-136
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• 2007

Korean house prices have risen rapidly since year 2001 and there have been some worries that the recent house price hikes are too excessive. This paper empirically analyzes the movement of Korean house prices and derives some implications from it, based on three different theoretical asset pricing models; long-run supply demand model, present value model and general asset pricing model. The results from the analyses show that recent house prices are overall higher than the theoretical prices, thus requiring measures to stabilize house prices hikes.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.689-690
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• 2015

The remodeling of multi-family houses (MFH) has emerged as a significant issue in the construction industry. Many decision makers struggle with the decision to remodel because of insufficient information including standards or methods for projecting the price of their structure after remodeling. In this context, this research analyzed the change in price of MFHs after remodeling. To achieve this research goal, (i) the price data from 14 groups (i.e., MFH renovation cases and equivalent MFH cases without remodeling) were collected, and (ii) the trend of price variation among each group was analyzed. Finally, this research suggests price variations of each group in terms of three different time points (i.e., before remodeling, after remodeling, and the present), which shows the effects of remodeling on the price of MFHs. This research offers a framework for the development of a model that will predict the price of an MFH after remodeling.

• Korean Journal of Construction Engineering and Management
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• v.6 no.6 s.28
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• pp.98-106
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• 2005

The construction cost index and the forecasting model of apartment house can be efficient for evaluating the validness of the fluctuating price, and for making guidelines for construction firms when calculating their profit. In this study the previous construction cost index of apartment house was improved, and the forecasting model based on X-12 ARIMA was developed. According to the result, during the last five years the construction cost, excluding labor expense, has risen approximately to 22.7%. And during next three years, additional 16.8% rise of construction cost is expected. Those quantitative results can be utilized for evaluating the apartment house's selling price in an indirection, and be helpful to understand the variation pattern of the price.

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.411-420
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• 2015

With the rapid increase in the price of house lease, a unique housing form in Korea, a serious social issue has been raised as to the use value of house lease and residence stability of the ordinary people. This study thus aimed to analyze the direct factors that affect lease guaranteed loan and market volatility in order to explore the right direction of financial policy to reduce housing burdens. To this end, the direct variables affecting house lease guaranteed loan, including lease price, transaction price and lending rate, were defined. Vector Error Correction Model (VECM), a time series analysis, was employed to dynamically explain the data. Based on the house lease prices and bank data on loans between January 2010 and December 2014, it was found that the increase in lease price was the direct result of the increase in lease guaranteed loan, not that of the decrease in lending rate or increase in housing transaction price.