• Journal of the Korean Geographical Society
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• v.48 no.2
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• pp.272-287
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• 2013

This paper describes the importance of selecting explanatory variables(e.g. land price determinants) in hedonic pricing models employed in predicting real estate price, and explores dynamics of the land price determinants over time. The City of Junju was chosen as the study area, and repeated measured price data of standard lots over 17 years were analyzed. We applied a three-level modeling approach to this data in consideration of its nested data structure and longitudinal characteristics. Main land price determinants we focused on are primarily based on items included in the standard comparison table of land price, which is an official hedonic pricing model used by Government to estimate land price for tax levy. Our result shows that the land price fluctuation over 17 years was not uniform over the whole study area with each neighborhood revealing different price trend, and as such warrants longitudinal model components. In addition, some of determinants previously recognized as important were proved insignificant. It was also found that significant determinants at a particular time point lost its power gradually over time and vice versa. It is expected that more accurate prediction of price would be possible when taken account for this dynamics of price determinants over time in applying hedonic pricing model method.

• Journal of the Korean Geographical Society
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• v.49 no.3
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• pp.405-422
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• 2014

Two important issues in hedonic model are to specify accurate model and delineate submarkets. While the former has experienced much improvement over recent decades, the latter has received relatively little attention. However, the accuracy of estimates from hedonic model will be necessarily reduced when the analysis does not adequately address market segmentation which can capture the spatial scale of price formation process in real estate. Placing emphasis on improvement of performance in hedonic model, this paper tried to segment real estate markets in Gangnam-gu and Jungrang-gu, which correspond to most heterogeneous and homogeneous ones respectively in 25 autonomous districts of Seoul. First, we calculated variable coefficients from mixed geographically weighted regression model (mixed GWR model) as input for clustering, since the coefficient from hedonic model can be interpreted as shadow price of attributes constituting real estate. After that, we developed a spatially constrained data-driven methodology to preserve spatial contiguity by utilizing the SKATER algorithm based on a minimum spanning tree. Finally, the performance of this method was verified by applying a multi-level model. We concluded that submarket does not exist in Jungrang-gu and five submarkets centered on arterial roads would be reasonable in Gangnam-gu. Urban infrastructure such as arterial roads has not been considered an important factor for delineating submarkets until now, but it was found empirically that they play a key role in market segmentation.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.39-56
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• 2019

In this study, we analyzed the effect of the Eungam Loop Line of Seoul Subway Line 6 on the sale price of adjacent row and multi-family houses on the accessibility and structural characteristics of subway stations. This study empirically analyzed a total of 17,938 cases from 2006 to 2017 based on data on the sale price of row and multi-family houses. In summary, the results of this study using the Hedonic Price Model are as follows. First, this study confirms that the Eungam Loop Line characteristics have a positive effect on the sale price as it is adjacent to the subway station. It is noteworthy that the sale price of 100-200m segment has a positive effect, and the sale price of Bulgwang station, which has excellent mobility and connectivity with CBD, YBD and GBD, has a positive effect. Second, this study shows the locational characteristics such as distance to bus stop, distance to mart, and distance to school have influence on the sale price. Third, this study finds the land characteristics such as land area, land shape, land facing, and road width, have significant effects on the sale price. Fourth, this study discovers the sale price is also is also affected by building and floor characteristics such as the type of housing, building area, the number of households, building age, elevator, and floor level.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.523-531
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• 2008

This study tried to find differences between housing lotting prices and sale prices owing to new multi-family housing price regulation. As the results of this study, they are as follows; First, this study shows housing market in Busan has a preferences of new housing which has a new housing form differing from the existing housing form. For example, the mixed-use apartment with higher stories shows steeper incline than the apartments with the existing forms. Second, the new housing prices are affected by the information that affect the price of the old existing housing. They are rates of green area of an apartment complex, the number of household, accessibility to downtown Busan and etc.. They are also confirmed factors that affect a rise of used-housing price in other studies. Third, brand value of apartments affects new housing prices. For example, if the major construction companies build the new apartment, it shows a rising trend than any other housing. Therefore, the local construction companies are expected to be put on a disadvantage places than major construction companies. Fourth, the lotting prices are the most important cause that lead to rise the new housing prices. Accordingly, the present lotting prices are expected that upward tendency the purchasing prices of the new housing will not continue, because the lotting prices have risen since the government removed lotting price regulations and exceeded the level of used-housing prices. And it denote that importance of housing sub-market which indicates rates of old existing housing market rising, frist preference Gu, second preference Gu, rate of multi-family housing.