• Journal of the Korean housing association
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• v.23 no.2
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• pp.69-77
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• 2012

The purpose of this study was to explore housing cost burden of young single- or two-person households in the United States who have recently moved for job-related reasons. Total 580 households were selected from 2009 American Housing Survey public-use microdata for data analysis. The findings are as follows: (1) Targeted single-person households were characterized as younger households with higher educational attainment, lower household income, and greater proportion of renters, multifamily housing residents and households with housing cost burden than other households; (2) two-person households showed a higher income level and lower housing cost burden; (3) characteristics that showed significant influences on housing cost burden were household size, householder's age, gender, race and educational attainment, household income level and tenure type; and (4) a linear combination of household size, household income, whether or not a low-income household, residency in metropolitan area, and home structural type were found to be most efficient to predict a single- or two-person household's housing cost burden regardless of the household size.

• Journal of Agricultural Extension & Community Development
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• v.24 no.1
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• pp.63-81
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• 2017

The purpose of this study is to analyze the changes of time series, the use by farm type and the causes of farm household debt. First, the mid and long term changes in farm household debt over the past 50 years have increased. Since 2010, the share of non-agricultural debt has exceeded the share of agricultural debt. Second, as a result of the analysis of the farm household debt use by farm type - full time&part time, farming type, land size, age, family members - there was a difference between the agricultural and the non-agricultural debt according to the type of farm household in a significant level of 1%. Finally, as a result of the cause analysis of the farm household debt, the related non-agricultural expenditure variables and the dummy variable of the manager's age, family member and land size has a common influence on the farm household debt increase.

• Korean Journal of Human Ecology
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• v.21 no.3
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• pp.611-628
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• 2012

The purpose of this study was to examine influence of home energy cost on housing cost burden of Korean and U.S. monthly renter households in urban areas and to explore influences of household and housing characteristics on their home energy cost burden. Microdata for this research was extracted from the 2011 Korean Household Budget Survey and 2009 American Housing Survey. Monthly renter households in urban areas were initially grouped based on household income, followed by a detailed analysis of housing and home energy cost. Findings are as follows: (1) The maximum ratio of home energy cost to household house hold income in Korea was 49% compared to 83% in the US; (2) Energy cost to income ratio were found to have significant influences on housing cost burden and lower income households' housing cost burden was found more vulnerable to their energy cost; (3) In general, the energy cost burden of low-income renter households in Korea tended to be influenced by household size, the number of household members staying at home during daytime hours and housing unit size. The energy cost burden of low-income renter households in the U.S. tended to be influenced by home structure type, size and age, the householder's age, race, educational attainment, the household size, number of wage earners per household, income, and the number of household members between 7 and 17 years of age.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.377-391
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• 2024

Accurate household projections are essential for sectors such as housing supply and tax policy planning, given the rapid social changes like declining birthrates, an aging population, and a rise in single-person households that impact household size and type. Korea introduced its first register-based census in 2015, transitioning from five-year general survey-based approach to an annual administrative data-based census. This change in census allows for more frequent and effective capturing the rapid demographic shifts and trends. However, this change in census has caused challenges in future projection by the existing household projection model due to the rapid dynamics. This paper proposes a new household projection method, the N-point Modified Exponential Model (MEM), that accurately reflects register-based census data and mitigates the impact of rapid demographic changes, in three types: the Weighted N-point MEM, the Regression-based N-point MEM, and the Rolling Weighted N+point MEM. Using register-based census data from 2016 to 2020 to forecast household headship rates by age, household size, and household type to 2051, the N-point modified exponential model outperformed the existing model in both long- and short-term forecast accuracy, suggesting its suitability as a future household projection model for Korea.

• Journal of Family Resource Management and Policy Review
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• v.14 no.1
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• pp.135-159
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• 2010

The purpose of the study was to investigate the characteristics and economic status of deficit households compared to surplus households. Data from The Household Income and Expenditure Survey 2005 by NSO and 50, 207 salary/wage earners' households were used for the analysis. The statistical methods used were GLM, logit, and cluster analyses. The analysis results showed that 25.3 percent of the households were deficit households. Approximately half of the lowest 20% income group were deficit households. Income deficit households earned 1, 273 thousand less than that of surplus households, whereas consumption of deficit households was 1, 006 thousand more than that of surplus households. The average propensity of consumption of deficit households was 142.1. According to the logit analysis, factors contributing to the probability of belonging to a deficit household included income level, household size, age and educational level, occupation, homeownership, car ownership, and wife's employment status. Deficit households were classified into 5 types: 1) health care expenditure-dominated group, 2) housing expenditure-dominated group, 3) education expenditure-dominated group, 4) money transfer-dominated group, and 5) overall-overconsumption group. The overall-overconsumption group was the largest group of all at 58.5%. It was found that for all five groups, the changes in household size, income group, home ownership, and occupation of the individual were variables that influenced the probability of belonging to a certain group.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2503-2508
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• 2013

Single-person households in the Seoul Metropolitan Area have increased sharply during recent 20-30 years. Despite of decreasing in the total population, the number of single-person household is predicted to increase continuously. However, the effect of single-person household growth on the domestic transport sector has not been studied concretely. In this study, the differences on trip generation characteristics by household size and attributes were figured out by analyzing Seoul Metropolitan Area Household Travel Behavior Survey (SMA-HTBS). Firstly, trip generation rates (trips/day/person) were produced by household attribute, household member attribute and trip attribute based on SMA-HTBS. Secondly, trip generation rate of single-person household and that of multi-person (2 or more) household were compared by significance test. It was found that trips generation characteristics of single-person household is quite different from those of multi-person household by housing type, residential type, living area, and transport mode. The result of this paper is expected to contribute developing more sophisticated trip generation model and transport policy reflecting trip generation characteristics of single-person household.

• Journal of the Korean housing association
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• v.21 no.2
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• pp.123-132
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• 2010

The housing market requires customized housing to be supplied according to the various characteristics of households. Multinominal Logistic Regression was used to analyze the effects of variables of household characteristics according to the number of family members on the choice of housing size in the scope of the whole country's housing market. Analysis showed that the number of family members has its own characteristics. When a household has a smaller number of family members, there are more variables affecting choice of housing size. Living and housing expenses variables served as significant variables that affect all household types. Results showed that households with more living and housing expenses are more likely to choose a large sized house and where households have a greater number of family members, there is more influence on that choice. The age of the householder was only found to be a meaningful variable in 1-2 person households and 3-4 person households, particularly in the choice of a small or large sized house. This shows that the age of the householder does not play an important role in choosing medium sized houses for households of under 4 people, but affects the choice of small and large sized houses. The academic ability of household members also served as a significant variable. While 1-2 person households with high academic ability tend to select a large sized house, 3-4 person households with high academic ability tend to select a small sized house. It is observed that members of both 1-2 person households and 3-4 person households tend to select their house between a large sized house and a small sized house in order to own their own houses. The result of this research suggests that there are various and detailed variables on the choice of housing size. Especially, a notable result is that household characteristics more significantly affect the housing size choice of 1-2 person households, while the trend of an aging society will more significantly affect a 3-4 person households' choice of a large sized house. Therefore, a study on the choice of housing size according to characteristics of elderly households and 1-2 person households should be continually analyzed.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.1
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• pp.36-46
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• 1997

Recently, The rural-pocket parks in Korea have been constructed for the recreation activities of the rural residents. The purpose of this study is to suggest planning guidelines focused on the minimum size. The methods used in this study are surveying the satisfaction of rural residents, analyzing the size of Rural-Pocket Parks recently constructed, and comparing with pocket parks in Seoul. The major findings of this study can be summarized as follows : 1) The average size of rural-pocket park in Kyung-Ki province is 650square meters per village and 2.19square meters per capita. and the relation between current rural-pocket park size and the number of household can be described as The current Size of Rural-Pocket Park = 7 ㎡${\times}$ Number of Households 2) The minimum size based on the rural residents'satisfaction is 633square meters at the 62 households level, and can be described as The Size of Rural-Pocket Park = 10㎡ ${\times}$ Number of Households 3) The most required activities by the rural residents are relaxation, children's playing and sports. and the most required facilities are ‘Jung-Ja’and ‘Combined Playing facility’. 4) The size required to include the basic recreational facilities at the level of household 20, 50, 100 are 132square meters. 386square meters, 633square meters respectively. The size per household is 7square meters and can be described as 5) As conclusion about 7square meters per household is recommended for the minimum size of rural-pocket park. and at least 140square meters is required as minimum size of rural-pocket park for the basic recreational facilities. The minimum Size of Rural-Pocket Park=㎡${\times}$Number of Households

• Journal of the Korean Home Economics Association
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• v.28 no.3
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• pp.105-125
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• 1990

The 78 housekeeping books were analyzed to find out the structure of income and expenditure of the farm household. The selected farm households were classified into 4 different farming types such as rice-cultivating, vinyl house, fruit-growing, and livestock farming. The results are summarized as follows : 1) The farm housekeeper ought to rationally manage farm household money income, because agricultural income was preponderated to several months regardless of farming types. 2) Farm household income was primarily dependent upon agricultural income and non-agricultural income in the livestock farming and rice-cultivating farm houshold respectively. 3) order of living expenses of the total farm households were recreation and entertainment expenses, food expenses, education expenses, and housing, fuel & light expenses in size. The major expenses were education expenses, food expenses and miscellaneous expenses in rice-cultivating, vinyl house and livestock farming, and fruit growing farm households respectively. 4) Balance of income and expenses of the farm household, s its time, size, and pattern of increase and decrease, was different by farming types. 5) Household expenses increased in February, May, August and December, though disposable income reversely decreased in February, April, August and December compared to each former month. So, special consideration should be taken into budget planning for household money management in February, August and December.

• Journal of the Korean Home Economics Association
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• v.35 no.1
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• pp.277-290
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• 1997

This study analyzed household expenditure patterns using the 1990 Korean Consumer Expenditure Survey and identified the factors determining a household's probability to be in an expenditure pattern. The results of cluster analysis revealed five expenditure patterns: Education and Recreation-dominated; Necessities-dominated; Miscellaneous expenditure-dominated Housing dominated; and Transportation and Communication-dominated patterns. Based on the results of descriptive and Logit analyses household income household size age of household head and housing status were major factors to determine the type of househld expenditure pattern.