• Journal of Korean Society of Transportation
• /
• v.19 no.4
• /
• pp.19-33
• /
• 2001

In the developed countries, traffic noise is one of most serious problems faced by people's lives. So the importance of the traffic noise is quite well recognized by the infrastructure planners as well as the people. The traffic noise is valued in monetary terms in some countries and it is reflected in estimating the net present value or benefit/cost ratio. On the contrary, the effects of traffic noise are not reflected in the assessment of infrastructure in most cases in Korea. However, as the income level has been increasing, more people have been becoming to put more importance on their living conditions. The purpose of this paper is to estimate the value of traffic noise in the Seoul metropolitan area. The housing price were surveyed to use the quasi-hedonic price technique. By this way, two housing prices at the same floor level in different 128 complexes in the Seoul metropolitan area were surveyed. the actual traffic noise level was also measured. The differences of housing prices and noise levels were analyzed using the various types of regression models. The value is quite different by size of house. The value of large house is higher than that of small house. Since the income level of people in large house is higher than that in small house. it might be said that value of traffic noise for high income people is higher than that for low income people. Moreover, the increase of 1dB(A) noise affects the house price by about 0.3% in Seoul metropolitan area.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.10
• /
• pp.4543-4550
• /
• 2012

The purpose of this paper is to suggest the improvement method to achieve the interactive development effect between high-tech industrial complex and its surrounding areas. For this reason, this paper has conducted an empirical analysis to find out relevant comprehensive factors, affecting nearby housing prices from such plans, especially by reviewing 'Seoul Digital Industrial Complex.' This paper is truly differentiated from previous research by adding a new perspective 'diverse location characteristics', as it focuses not only on 'high-tech facility' characteristics, but also on 'urban function facilities', including 'transportation facilities', 'amenity facilities', 'security facilities', etc. Then, SPSS Version 18.0 was utilized to conduct the multiple regression analysis with the accumulated relevant data and several results were drawn out as following: Firstly, 'deterioration level', 'brand of apartment', etc. are found to be major influencing factors. Secondly, 'educational facilities', 'transportation facilities', 'Cultural & Sports facilities', 'Amenity facilities', etc. are found in the sector of 'location characteristic'. Lastly, 'leading companies within the industrial complex', were also found, affecting nearby housing prices. Therefore, when a housing development project is planned to grant the interactive development effect to high-tech industrial complex and its surrounding housing areas, it is necessary to consider variety factors, such as comprehensive location characteristics and housing complex characteristics, and also proper housing policy measures should be devised in accordance with the actual demand of employees and their dependant family members.

• Environmental and Resource Economics Review
• /
• v.30 no.3
• /
• pp.395-433
• /
• 2021

This paper aims to examine the effects of homeownership and residential types on the economic values of urban green spaces. Green open spaces as public goods provide positive externalities that are comprised of pecuniary and technological externalities. Seoul, South Korea, is used as a case study using choice experiments, with split-sample online respondents of 1,000. The study results evidenced that the differentiation between the two types of externalities is imperative for equitable provisions and efficient management of various urban open spaces. There is a positively significant and substantial impact of homeownership for apartment dwellers, ceteris paribus, but not for house dwellers. For apartments, the efficiency loss can be reduced by increasing green spaces up to the critical point where the marginal cost is at equilibrium with tenants' marginal values. For non-apartment houses, it is not homeownership but the monthly household income that has a significant impact on the amenity value. In general, public benefits from green spaces are equivalent to 16% to 33% of the current residential prices on average for a view or access. Different residential types do not cause a significant impact on the access values. Residential profiles for green spaces were developed, together with tailor-made policy suggestions.

• Journal of the Korean Geographical Society
• /
• v.49 no.6
• /
• pp.917-934
• /
• 2014

Geographically weighted regression(GWR) model has been widely used to estimate spatially heterogeneous real estate prices. The GWR model, however, has some limitations of the selection of different price determinants over space and the restricted number of observations for local estimation. Alternatively, the geographically weighted LASSO(GWL) model has been recently introduced and received a growing interest. In this paper, we attempt to explore various local price determinants for the real estate by utilizing the GWL and its applicability to forecasting the real estate price. To do this, we developed the three hedonic models of OLS, GWR, and GWL focusing on the sales price of apartments in Seoul and compared those models in terms of model fit, prediction, and multicollinearity. As a result, local models appeared to be better than the global OLS on the whole, and in particular, the GWL appeared to be more explanatory and predictable than other models. Moreover, the GWL enabled to provide spatially different sets of price determinants which no multicollinearity exists. The GWL helps select the significant sets of independent variables from a high dimensional dataset, and hence will be a useful technique for large and complex spatial big data.