• 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.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.65-76
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• 2014

Housing prices are influenced by external shock factors such as real estate policy or economy. Thus, the intervention effect is important for the development of forecasting model for housing price index. In this paper, we examined the degree of effective power of external shock factors for forecasting housing price index and analyzed time series models for efficient forecasting of housing price index. It is shown that intervention models are better than other models in forecasting results using real data based on the accuracy criteria.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.2
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• pp.437-444
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• 2017

The purpose of this study is to analyze the factors affecting the reverse mortgage utilization and satisfaction of the elderly. Based on the survey data of the reverse mortgage demand in 2016, we carried out empirical analysis using the binary logit model and the ordered logit model. First of all, as a result of the empirical analysis using the binary logit model, the determinants of using the reverse mortgage were age, region, assets, household member, children with financial help, and education level. As a result of the empirical analysis using the ordered logit model, the determinants of the satisfaction level of the reverse mortgage were estimated to be age, gender, and region. Based on the results of the empirical analysis, it is necessary to find a way to increase the participation rate of the reverse mortgage and to improve the satisfaction of the user.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

• 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.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.379-383
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• 2010

본 논문은 현재 공동주택에서는 주소를 표기할 때 지번 뒤에 상세주소를 표기하여 단지 내에서 개별적으로 일관성 없는 세부주소로 사용하고 있는 문제점을 보완하기 위해 상세주소 표기 표준화 모형을 제시하였다. 공동주택별로 그 표기방식이 다양해 같은 동을 가동, 101동, 1동, A동, 에이동 등으로 사용하고 있었으며, 같은 층, 호를 지하1층, 지1호, 비01호, B01호,B1호, 1호, F02호 등과 같이 다양하게 표기하는 등, 비정형화로 인한 문제점이 제기 되고 있다. 비정형화로 인한 위치검색 및 위치파악 등의 애로사항으로 행정업무의 자료호환 등의 문제점이 발생하고 있어 표준화된 모형개발이 현실적으로 요구됨에 따라 본 연구에서는 공동주택인 아파트, 연립주택, 다세대주택을 중심으로 상세주소 표기 건축물에 대한 전문가 설문조사를 실시하여 상세주소의 표기방식에 대한 표준화 모형을 개발하였다.

• International Area Studies Review
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• v.14 no.3
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• pp.246-263
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• 2010

This study examines how the long-run relations between housing price and inflation in the United Sates have changed since the year of 2000. Johansen co-integration test, estimation of long-run equilibrium equation, and Granger causality tests are conducted, based on the VECM. Data covers the period from the first quarter of 1975 to the second quarter of 2010. I adopt the recursive estimation method in which the final period of the estimation is expanded by one quarter, starting from the first quarter of 2000. The empirical results are as follows: (1) In spite of the sharp increase of housing price, the long-run relationship of house prices and inflation has been remained stable until 2007, showing that house prices are a stable inflation hedge in the long run. (2) The housing price plunge since 1997 does not seem to be related to the restore of the long-run relationship between housing prices and inflation. (3) Granger causality test results support the hypothesis that inflation granger-causes housing prices with 10% significance level, but reject the hypothesis that housing price granger-causes inflation.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.461-471
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• 2012

The purpose of this paper is to visualize the housing market dynamics with ESDA (Exploratory Spatial Data Analysis) using MATLAB toolbox, in terms of the modeling housing market dynamics in the Busan Metropolitan City. The data are used the real housing price transaction records in Busan from the first quarter of 2006 to the second quarter of 2009. Hedonic house price model, which is not reflecting spatial autocorrelation, has been a powerful tool in understanding housing market dynamics in urban housing economics. This study considers spatial autocorrelation in order to improve the traditional hedonic model which is based on OLS(Ordinary Least Squares) method. The study is, also, investigated the comparison in terms of $R^2$, Sigma Square(${\sigma}^2$), Likelihood(LR) among spatial econometrics models such as SAR(Spatial Autoregressive Models), SEM(Spatial Errors Models), and SAC(General Spatial Models). The major finding of the study is that the SAR, SEM, SAC are far better than the traditional OLS model, considering the various indicators. In addition, the SEM and the SAC are superior to the SAR.

• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.175-189
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• 2015

This study analyzed the determinant factors of elderly housing by housing type according to the Financial Retirement plan of preliminary elders and it aimed to draw a future development scheme of elderly housing. This study used parameters of existing research as control variables and it has a meaningful point that the variables of Financial Retirement plan of elders were verified through the research model which this study used. In addition, there was a difference between the detailed models. As a comprehensive analysis result, the Choice Model of elderly housing type has a difference between determinants, the single-family housing and the multi-family housing, based on the Welfare Facility for the Aged from Financial Retirement plan of elders.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.101-115
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• 2009

The prompt recovery of housing market in Korea became the national task, for which tools that can analyze the influence that changing situation of housing market and new policy may have on the housing market needs to be developed. Thus, this research intends to develop Multi-Agent Housing Market Model and simulation system in Jinju City as a study area. Analyzing the local housing market of Jinju City, then multi-agent model of housing market that consolidates 3 sub-models, house choice model, hedonic model of house price and location choice model is developed. Moreover in order to develop simulation system the model is programmed in the virtual space of which the size is $150{\times}100$ cell including physical shape of city such as road, urban facilities, land use, etc. With the system, simulations are performed to confirm the impact of urban development on the pattern of residential location. As a result, it is found that the residential location can not be easily induced when only road, commercial and convenient facilities are supplied. However, it is also found that since supplying green results in very many residences, arrangement of infrastructure and environmental factor should be considered at the same time for urban development. As conclusion, it is confirmed that the model and simulation system developed in this research smoothly works to be utilized for the analysis of diverse policy experiment and housing market.