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임미화 ( Lim Mihwa ),주현태 ( Joo Hyuntae ),이창무 ( Lee Changmoo ) 한국부동산분석학회 2016 不動産學硏究 Vol.22 No.3
Previous studies have used households` permanent income to estimate long-term housing demand, as in the Mankiw and Weil Model(1989). However, in Mankiw and Weil(1989), the long-term effects of income varying with time were not taken into account. In the current study, by contrast, the fact that income depends on changes in economic growth has been reflected in household characteristics when estimating permanent income. This study analyzes the effect of income variables using the GDP scenario, which compares the effect that permanent as well as economic growth-dependent income can have on long-term housing demand estimates. The results show that the elasticity of permanent income was similar (0.46) to that of previous studies (0.453-0.488), while per capita GDP and per household GDP displayed greater elasticity than that of permanent income. In addition, upon comparing estimates of per household demand with the number of households by KOSIS, the error between variables was not differentiable over short to mid-term horizons. On the other hand, the error of per capita GDP and per household GDP was less than that of permanent income for long-term estimates of over 20 years. This study highlights the importance of carefully selecting income-related variables when estimating long-term housing demand.
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빈집 발생에 미치는 개별주택속성과 경제적 변수의 영향력 비교
임미화 ( Mihwa Lim ) 한국부동산원 2022 부동산분석 Vol.8 No.3
The purpose of this study was to determine the influences of individual housing attributes and economic variables on vacant houses a survey of vacant houses in Gunsan. According to the analysis, individual attributes had a significant influence. The attributes included land area, total floor area, building age, building structure, landlocked land, land shape and roof structure. In terms of regional attributes, the proportion of the population aged 65 or older was significant and the marginal effect was also large. Vacant houses could occur when they are located on landlocked plots where are difficult to enter or exit and they have little value to use due to small floor area. Atypical shape of the land also had significance, but building structures with wood or brick as opposed to concrete and roof structures with wood or tile had a greater influence on vacant houses than all other factors. In light of a large influence of building or roof structure, it was evident that vacant houses are related to the value of use such as remodeling. However, economic variables such as were never significant. The results showed that vacant houses depend more on individual attributes than on economic variables. In particular, structural variables for building or roof had a great deal of influence on the houses. This study explained the importance of managing vacant houses by levels and the fact that vacant houses resulted from dwelling, a consumption value, in terms of selection or use to move a house.
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장영길(Chang, Young-Gil),장성대(Chang, Seong Dae),임미화(Mihwa Lim) 한국부동산연구원 2021 부동산연구 Vol.31 No.1
최근 수도권 주택가격의 상승은 주택 공급 부족이 주된 원인이라는 문제가 제기되었다. 본 연구는 주택 가격에 영향을 미치는 수급 요인의 영향을 검정하고자 한다. 방법은 서울, 수도권, 지방으로 세분하고 주택가격변동성에 영향을 주는 수요 요인인 가구수와, 공급 요인인 주택량, 그리고 경제 요인 등의 영향력을 분석하고자 한다. 종속변수로 주택가격, 독립변수로 준공호수, 세대수, 산업별 종업원수, 통화량, 가계대출액을 사용하여 시계열자료를 구축하였다. 분석모형으로는 ARIMA, GARCH, BREAKS를 사용하였다. 분석결과는 첫째, ARIMA분석에서 주택가격은 모든 지역에서 자기회귀 영향이 크게 강하고, 둘째 BREAKS분석에서는 신규 준공량, 세대수는 가격 상승할 때에 유의미한 요인으로 나타났다. 셋째, GARCH 분석에서 주택가격의 변동성은 과거 불확실성과 예기치 못한 충격이 유의미했고 지역에 따라 가계대출액이나 통화량의 변동성이 유의미하였다. 결론적으로 세대수, 주택공급량, 고용의 변동이 주택가격에 영향을 주는 것으로 분석되었다. 특히 고용에서 수도권과 서울은 제조업 종사자수가 음(-)의 영향력, 정보,금융,보건 종사자수가 주택가격에 양(+)의 유의한 영향력을 주는 것으로 나타났다. Recently, an issue has been raised that a shortage of housing supply mainly caused the surge in housing prices in the metropolitan area. Therefore, to analyze regional differences, this study divided Korea into Seoul, metropolitan areas, and provinces. In addition, this study analyzes the number of households and housing completed as the factors of demand and supply, respectively. Moreover, economic factors that affect housing prices are determined. Dependent variables are housing prices, and the independent variables are completed housing, number of households, employees by industry, money supply (M2), and mortgage loan amount. Moreover, ARIMA, GARCH, and BREAKS are the analysis models used in this study. The findings of this study are as follows. First, ARIMA analysis showed a strong autoregressive effect in all regions. Second, BREAKS analysis showed that new construction and households are significant factors affecting the increase in housing price. Third, in the GARCH analysis, uncertainty and unexpected shock were significant in house price volatility, and the volatility of loan amount and M2 was significant depending on the region. In the case of Seoul and metropolitan areas, manufacturing workers had a negative (-) influence and ICT/finance/health workers had positive (+) influence on housing prices. Results reveal that changes in the number of households, housing supply, and employment affect the housing price. In particular, in terms of employment in Seoul and metropolitan areas, the number of workers in the manufacturing industry has a negative (-) influence on housing price, whereas the amount of information, finance, and the number of health workers has a positive (+) significant influence on the housing price.
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