• Journal of Distribution Science
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• v.17 no.1
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• pp.11-19
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• 2019

Purpose - We document the impact of economic policy uncertainty (EPU) in the US and China on the dynamic spillover effect of macroeconomics such as stock price, housing price in Korea. Research design, data, and methodology - We use the nine variables to analyze the effect which produces a result among the EPU indexes of the US and China on economic variables which is the consumer price index (CPI), housing purchase price composite index, housing lease price, the stock price index in banking industry, construction industry and distribution industry, and composite leading indicator from January 1995 to December 2016 with the Vector Error Correction Model. Result - The US EPU index has significantly a negative relation on the CPI, housing purchase price index, housing lease price index, the stock price index in banking industry, construction industry, and distribution industry in Korea. Conclusions - We find the dynamic effect of the EPU indexes in the US and China on the macroeconomics returns in Korea. This study has an empirical evidence that the economy market in Korea is influenced by the EPU index of the US rather than it of China. The higher EPU, the more risky the economy of in Korea.

• The Journal of Industrial Distribution & Business
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• v.7 no.3
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• pp.27-36
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• 2016

Purpose - Considering the importance of housing needs to real estate market, domestic studies on real estate prices from the perspective of demand are basically based on macro-data, but relatively few are associated with micro-data of urban real estate demand. We try to find a reliable relation of elasticity of demand and commercial housing market. Research design, data, and methodology - In this paper, we have derived housing demand theoretic method and have utilized micro-data of residential family housing survey of downtown area in Kunming City in October, 2015 to estimate income elasticity and price elasticity of housing demand respectively and make a comparative analysis. Results - The results indicate that income elasticity and price elasticity of families with owner-occupied housing are both larger than those of families with rental housing. Income elasticity of housing demand of urban residential families in Kunming is far below the foreign average and eastern coastal cities level, however, the corresponding price elasticity is far higher. Conclusions - We suggest that housing affordability of urban families in western China are constrained by the level of economic development, and the current housing price level has exceeded the economic affordability and psychological expectation of ordinary residents. Furthermore, noticing the great rigidity of housing demand, the expansion space of housing market for improvement and for commodity is limited.

• Land and Housing Review
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• v.10 no.1
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• pp.9-18
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• 2019

This study examined the dynamic change in the co-movement between the house price rates with the network methods of Mantegna (1999). First, Capital area and non-capital area form independent clusters which have the heterogeneous co-movement pattern. In other words, Capital and non-capital areas have low connectivity in the housing market. Also, if the co-movement between capital areas have been strengthened, the co-movement between non-capital areas have been weakened. The results of the dynamic analysis show that the degree of the co-movement in the housing market is continuously increased. The members of the co-movement group in the capital area are strongly steadied by all periods. However, the members in the non-capital area have been changed according to the period. Accordingly, it is necessary to establish policies based on various information for the housing market of the non-capital area rather than policies targeting the capital area. In addition, Apartments in Korea are more likely to be used as investment or speculative assets than other types of houses. It has been confirmed that this is Gangbuk, which is locatied in the northern part of Seoul, appears to be a region where the Spillover Effects of price fluctuation can be triggered in the housing and apartment market. However, the housing market in Gangnam, which is locatied in the southern part of Seoul, was divided into low systematic risk.

• Journal of the Korean housing association
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• v.18 no.3
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• pp.63-72
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• 2007

The purpose of this study is to identify the impact of the building, site, and region characteristic factors on the annual average price rising rate of apartment housing in Seoul. The data were consisted of 272 apartment units in Seoul. A survey included checking the drawing documents and interview with apartment maintenance staffs and real estate agencies from October 2006 to February 2007. Data were analyzed with descriptives, frequency, crosstabs, and linear regression by SPSS/PC for Window. The linear regression model was employed to evaluate the price rising rate in apartment housing. Following results were obtained. The price rising rate for pyeong ($3.3m^2$) of apartment housing was determinated by the district zone, the construction company's brand name, the building age, the building stories, the floor space index, the building-to-land ratio, the green space rate, and the distance from the downtown. Especially, the district zone was the most important factor that affected the price rising of apartment housing in Seoul. Therefore, the policy has to focus to solve the imbalance between autonomous districts with the collaborated tax.

• 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.

• Land and Housing Review
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• v.11 no.3
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• pp.1-9
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• 2020

This study fulfills an empirical analysis how the physical factors affect the formation of housing price with the block unit. Block unit houses are a type of housing that pursues comfort and convenience in that the characteristics of individual houses and apartment houses are mixed. Existing studies have focused only on the physical characteristics of various planning elements such as block-type residential complexes. Nevertheless, it is not known whether the physical characteristics of block-type residential complexes reflect the preferences of actual consumers. In addition, there are no sufficient studies on how to evaluate them from the market side. In this study, block-level detached housing sites the target complexes with 10 or more households built between 2002 and 2019. The target areas for analysis are 163 complexes in Paju, Namyangju, Goyang, Suwon, Yongin, Ansan, Gimpo, Incheon, Seongnam, Hwaseong and Gwangju, Gyeonggi-do. The physical elements that make up the unit housing were classified through factor analysis. Finally, regression analysis was conducted to establish the basis determining the price-forming factors. As a result of the analysis, the factors that influenced the price were the site area and the number of community facilities. The variable with negative influence was the distance from Seoul. Based on the results of this study, it can be said that the influence on price formation in various areas was confirmed by presenting the relationship between the facility composition and price of a detached house.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.27-43
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• 2020

Purpose - This study explores the impact of the real estate industry on related industries for the perspective of Chinese steel companies. Design/methodology/approach - The impact of housing prices on the 41 listed steel companies' performance was analyzed by using the panel data model. We used two kinds of housing price indexes that are set in the panel data models to estimate the range of the real estate market, driving the performance growth of steel listed companies. Moreover, the net profit of steel companies is used as the dependent variable. To test the stability of the model, ROA used as a dependent variable for the robustness test. Also, to avoid the time trend of housing prices, this paper selects the growth rate of housing prices as the primary research variable. After Fisher-type testings, there is no unit root problem in both independent and dependent variables. Findings - The results indicated that the rise in the housing price has a positive influence on the steel company performance. When the housing price increases by 1%, the net profit of steel enterprises will increase by 5 to 20 million yuan. Research implications or Originality - In this paper, empirical data at the micro-level and panel model are used to quantify China's real estate industry's driving effect on the iron and steel industry, providing evidence from the microdata level. It helps us to understand further the status and role of China's real estate industry in the economic structure.

• Journal of the Korean housing association
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• v.25 no.6
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• pp.39-47
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• 2014

The background of this study is to examine the structure of housing price of which characteristics are not equal but hierarchical in the apartment complexes. So, the purpose of this study is to analyze the influences of apartment complex characteristics on the housing price within the same regional boundary by HLM. The data used as dependent variables were the market prices of 938 units from 29 apartment complexes by stratified sampling. The 2nd level independent variables is the Housing complex characteristics which are composed of the housing complex & locational variables and the 1st level independent variables are the unit characteristics. The results are as follows. First, the first model shows that the 2nd level variables explains 68% of the housing prices. Second, the influential variables of the 1st level unit variable are 'dwelling exclusive area', 'floor of dwelling' and 'direction of dwelling'. Third, the influential variables of the housing complex variables in the 2nd level are 'lot area', 'the building-to-land ratio', 'the number of unit', 'the number of parking lots per unit', 'Green space area' and 'open space area per unit'. The last, the influential variables of the housing locational variables in the 2nd level are 'distance to subway and park' and the number of school and park within a radius of 1km.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.115-130
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• 1999

The price structure of housing market varies depending upon market price policy rather than low or high price policy because of IMF. The object of this study is to develop an analysis system for analyzing housing market and its demand. The analysis system consists of four major categories: macro index analysis, market decision analysis, housing market analysis, and consumer analysis. We model each category by using a variety of techniques such as generalized linear model, categorical analysis, bubble analysis, drill-down analysis, price sensitivity meter analysis, optimum price index analysis, profit index measurement analysis, correspondence analysis, conjoint analysis, and multidimensional scaling analysis. Seoul apartment data is analyzed to demonstrate the practical usefulness of the system.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1431-1436
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• 2018

We study the scaling of the price fluctuation in the Korean housing market. From the numerical analysis, we show that the normalized return distribution of the housing price, P(r), has a fat-tail and is well approximated by a power-law, $P(r){\sim}r^{-({\alpha}+1)}$, with ${\alpha}{\simeq}3$ for the whole data set. However, if we divide the data into groups based on the trading patterns, then the value of ${\alpha}$ for positive tail and negative tail can be different depending on the trading patterns. We also find that the autocorrelation function of the housing price decays much slower than that of the stock exchange markets, which shows a unique feature of the housing market distinguished from the other financial systems.