• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.125-139
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• 2021

Continuous subway line expansion over the years in Seoul metropolitan area has contributed to improved accessibility to public transport. Since public transport accessibility has a significant impact on housing decisions, quantitative analysis of correlation between housing prices and public transport accessibility is regarded as one of the most important factors for planning better housing policies. This study defines the reduction of traveling time resulted from the construction of new metro stations despite them not being the closest stations as 'Network Expansion Effect', and seeks to understand how the Network Expansion Effect impacts on housing prices. The study analyzes monthly rent data converted from upfront lump sum deposit, so called Jeonse in Korea, from 2012 to 2018, through 'A Modified Repeat Sales Model.' As a result, the effect of 'Network Expansion' on rental prices in Seoul has stronger during the period of 2017 to 2018 than the base period of 2012 to 2014, which suggests the 'Network Expansion' has a meaningful effect on rent. In addition, in comparison between the most and the least affected group of apartments by 'Network Expansion Effect', the most affected group has more price increase than the least affected group. These findings also indicate that different levels of 'Network Expansion Effect' have various influences on the value of residential real estate properties.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.457-469
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• 2010

The government real estate policy has repeatedly relaxed and reinforced controls under the mutually contradictory targets. Switching over the supporting policy after the IMF crisis to the regulating policy from 2003, the government housing policy began to generate ill effects due to various regulations. This stud carefully investigates and statistically tests the transmissions of variations in the housing prices between the metropolitan areas in the early stage of the preceding administration, under the effect of the supporting scheme, and those in the late stage, under the effect of the restricting scheme. The distinctive feature between the two periods is found to be much simplified interrelationships of the price variations in the latter period. Consolidated leading role of capital sphere, by concentrated economic strength, suggest the obvious imbalance between variations in the metropolis housing prices.

• KDI Journal of Economic Policy
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• v.44 no.4
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• pp.79-108
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• 2022

This study examines the impact of sentiment shock, which is defined as a stochastic innovation to the Housing Market Confidence Index (HMCI) that is orthogonal to past housing price changes, on aggregate housing price changes and housing price volatility. This paper documents empirical evidence that sentiment shock has a statistically significant relationship with Korea's aggregate housing price changes. Specifically, the key findings show that an increase in sentiment shock predicts a rise in the aggregate housing price and a drop in its volatility at the national level. For the Seoul Metropolitan Region (SMR), this study also suggests that sentiment shock is positively associated with one-month-ahead aggregate housing price changes, whereas an increase in sentiment volatility tends to increase housing price volatility as well. In addition, the out-of-sample forecasting exercises conducted here reveal that the prediction model endowed with sentiment shock and sentiment volatility outperforms other competing prediction models.

• Korea Real Estate Review
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• v.24 no.1
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• pp.15-26
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• 2014

Using multivariate GARCH model, we estimate the relationship between the housing sale prices and lease prices in the Korean housing market. In the analysis of relationship between the rate of changes in sale and lease prices, the correlation coefficient of the apartment and detached house is higher than that of the townhouse. By housing type, the correlation coefficient between detached house and townhouse is higher than between apartment and detached house or apartment and townhouse. By housing size, there are no significant different results between the sales price and the rental price. The correlation coefficient between medium and small size is the highest in the apartment housing market, whereas the correlation coefficient between large and medium size is the highest in the detached housing market, resulting from the fact that people may be more interested in medium- and small-sized apartment and large- and medium-sized detached house. In the detached housing market, the correlation coefficient between large-medium size and medium-small size in the rental price is higher than that of sales price. This result implies that the process of the decision making between purchasing and leasing a house might be different.

• Spatial Information Research
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• v.21 no.6
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• pp.81-89
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• 2013

Recently, several studies conducted for other countries show that housing price has very close relationship with personal or neighborhood level obesity. Also these studies suggest the use of housing price as a new SES(Socio-Economic Status) variable for health related studies. In this study, whether this relationship can be found in regions of the Seoul Metropolitan Area is investigated. The results of this study show that, as in the cases of other countries, the regions with SES represented by higher housing prices show lower obesity levels. Further, the results show that the differences in regional housing prices well explain the variations of regional obesity levels as other traditional SES variables do. This finding indicates that housing price which is objectively, continuously, and spatially measured in Korea can be used as a new SES indicator for health research in Korea.

• 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.

• Korean System Dynamics Review
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• v.12 no.2
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• pp.5-36
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• 2011

Seoul citizens are currently suffering from high housing price. Home prices have risen more rapidly than salaries so owning a housing unit (apartment, condominium, or single-family home) in Seoul is becoming more difficult than ever. Therefore, this research examines the behavior of average Seoul citizen in owning housing unit in Seoul, Korea, particularly in terms of the length of time required to afford a house unit. This research estimates that it will take about 18.75 years in maximum after getting a job (12.75 years after purchasing the housing unit) to own housing unit in Seoul that is currently valued at $300,000 where the growth rate of income is 2.97% and consumption price increases at a rate of 2.95% per annum. Finally in this research, the optimal growth rate of housing price is estimated ranged from 3.5 to 4.0% minimizing the loan payoff period.

• Journal of Korean Society of Rural Planning
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• v.21 no.1
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• pp.9-17
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• 2015

The purpose of this study was to analyze the preferences for the physical features of senior congregate housing. The survey was conducted to target the elderly households living in senior congregate housing and general elderly households living in the rural. The results of this study were as follows. Showed that housing conditions are more important than environmental conditions, elderly households living in senior congregate housing. Among them was the most important house prices and rents. Also among the external factors such as environmental conditions is the distance to the workplace were very important. So the elderly households living in congregate housing showed that more important considering the economic aspects. Finally, want to be the foundation of sustainable housing policies for rural elderly households.

• Journal of the Korean housing association
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• v.22 no.5
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• pp.47-57
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• 2011

The purpose of this research is to demonstrate the necessity of conserving and maintaining eco-parks by estimating their economic value. Wonheung-ee Park in Sannam 3 District of Cheongju City was chosen as the subject and a quantitative estimation was conducted. The quantitative analysis utilized the hedonic price model that estimates the value of non-market goods. The summarized results of this study are follows. The subject park influenced the prices of its neighboring apartments. The most important factor was the distance between the park and the subject apartment. When the distance was longer than 400m, the impact was greatest. The quantitative assessment also showed that apartment prices and the distance between an apartment and the park had a negative relationship. When the distance increased by 1%, apartment prices decreased by 0.430%. This means that within a certain distance, the closer an apartment is to the park, the higher is the price. Demonstrating the economic value of eco-parks, this study also supports the importance of preserving eco-areas. It generally shows that when we develop a city, we should refrain destroying the ecosystem.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.