• Journal of Distribution Science
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• v.12 no.9
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• pp.47-52
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• 2014

Purpose - There has been little research on the variables influencing studio apartment values. This study aims to identify variables affecting the value of studio apartments in Seoul by empirically examining the interaction between sale prices and characteristics studio apartment characteristics. Research design, data, and methodology - We have analyzed data pertaining to 142 studio apartments in September 2010. A regression analysis model is constructed to test the significance of the variables in relation to the studio apartment sale prices per m2 in Seoul. Results - The age of the building is comparatively more significant than land use as the explanatory variable. Land price is the key variable affecting studio apartment sale prices and investors are willing to pay high implicit sale prices for locations that are associated with high land prices. Conclusions - The age of buildings explains a significant portion of the variability of the sale prices of studio apartment. Higher land prices result in higher sale prices for studio apartments. The older the buildings, the lower the sale prices of the studio apartments.

• Journal of Korea Planning Association
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• v.54 no.1
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• pp.148-158
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• 2019

The purpose of this study is to analyze the impact of earthquake on apartment prices. Many studies have been done analyzing the relationship between natural hazards and residential property prices. Most studies have shown that natural hazards have an negative effect on residential property prices, but some studies have shown that natural hazards have an positive effect on residential property prices. These conflicting analysis result from the lack of considering natural hazard frequency at the analysis site. According to literature reviews risk avoidance tendency are already inherent in prices, thus distorting the relationship between natural hazards and prices. That is, in order to analyze the impact of natural hazards on residential property prices, analysis must be carried out in areas where there has not suffered natural hazard for a long time or where there has been no damage before. Nevertheless, previous studies analyzed areas frequently affected by natural hazards. Gyeongju has been recognized as a safe area from earthquake in the past, an 5.8 magnitude earthquake occurred in September 2016. Analysis results focusing on Gyeongju Earthquake case has shown that the earthquake has affected decrease of apartment prices in hazardous areas, and after earthquake apartment prices have risen over time.

• Journal of the Korean Society for Railway
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• v.7 no.2
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• pp.155-162
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• 2004

In order to account for a price variation of apartment that places near a newly constructed subway station, a spatial hedonic model was developed to examine spacial characteristics that affect a purchasing price of an apartment using a White Estimator. In particular, the paper aims to examine various effects of subway 7 construction on an apartment price in Seoul Metropolitan Area. As explanatory variables, an apartment size, distance to a closest subway station, distance to the Central Business District (CBD) of Seoul, the number of years after building, and a lagged variable of the apartment purchasing price were used. The lagged variable plays a role of representing a spatial weighted average of previous prices of other apartments that locate within 3 km from the apartment. For a precise study, an entire sample was divided into two sets, southern area and southwestern area of Seoul, and two different spatial hedonic models were estimated. Not only before and after analysis, but also with and without analysis were conducted to compare with different effects of the spatial characteristics of two areas. The results show that before the construction of the subway 7, the prices of the apartments in the southern area were more sensitive to the apartment size, the distance to a closest subway station, the distance to the CBD, and the prices of the other apartments locating within 3km rather than those in the southwestern area. After the construction, on contrast, it is found that the apartment purchasing prices in the southwestern area are more sensitive than those in the southern area due to people's expectation regarding a new development around the subway station. In addition, the prices of the apartments locating closely with a transfer station are more likely to go up by increase in the apartment size, the distance to the station, and the prices of the other apartments within 3 km. Compared with the negative effects of the distance to the station on the prices in the other models, the positive effect of the distance to the transfer station might be caused by the characteristics of commercial area in which people are not likely to live.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.249-250
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• 2016

Korean government has announced declared prices of apartment reflecting market condition each year. Therefore, on that basis, apartment owners have used as basic data trading apartments and the government has been used to calculate the tax. However, the sales prices and declared prices of apartment has occurred difference depending on the region and the brand. This study has analyzed and compared regional differences in sales price of apartments. The results of this study, we have known that sales price of apartments was a big difference depending on the region and the gross area. Especially, Seoul and Gyeonggi Province are the highest. And sales price of Southeast and urban area are the highest in Seoul. In the future, it is necessary that gap analysis between sales price and declared prices of apartment. And It is needed to develop apartment index considering the region and the gross area.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.89-96
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• 2009

The present study is performed to identify the relationship between apartment prices and distances from freeway interchanges. To collect consistent data and to simplify the sphere of the influence, the Seoul beltway was selected as the target road. Two sides of the Seoul beltway such as the inner side and the outer side are partitioned following the whole beltway line. The maximum distance to be taken care of in data collection was 6.0km from freeway interchanges. Results show that there exist consistent patterns between apartment prices and distances from freeway interchanges. In detail, if the apartment is located on the sphere of the influence, it is shown that a) the prices are increased overall b) the prices are low at very near of interchanges c) the prices are increasing up to the points of about 2.0~4.0km from interchanges d) the prices are decreasing after the points. Additionally, it is shown the pattern observed was following approximately a quadratic curve which is different from the liner curve obtained from the similar study over the sphere of the influence in railway(Subway).

• Journal of Digital Convergence
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• v.15 no.1
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• pp.181-189
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• 2017

The purpose of this research is to set a standard for deciding competitive marketing prices of new supplying apartment houses and to analyze decision factors in sale price of supplying apartment houses with Analytic Hierarchy Process; the resulted model does not use the method that joins the land cost and the cost of construction together, but the method that compares the sales prices of surrounding apartments. This research tries to set a standard for decision of the prices of newly supplying apartment houses by classifying the determinants into the $1^{st}$ step(4 factors), the $2^{nd}$ step(9 factors), and the $3^{rd}$ step(25 factors). According to the process, the relative importance of decision factors in the sale prices is determined and this should be used as the index of sale prices for newly supplying apartment houses when the houses are provided. In addition, through the $2^{nd}$ step including 9 factors, the comparative model for sale prices is defined and the model is presented to be applied in the real business. Subsequent study additionally considering the factors apart from marketing which tries to find a generalized standard needs to be conducted.

• Land and Housing Review
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• v.12 no.4
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• pp.41-59
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• 2021

This study reviews theoretical considerations and past studies about real estate prices, macroeconomic variables, and real estate policies. Monthly data from January 2003 to June 2021 are used, and a VEC model, the most widely used multivariate time series analysis method, is employed for analysis. Through the model, the effects of macroeconomic variables and real estate regulatory policies on real estate prices in Seoul are analyzed. Findings are summarized as follows. First, macroeconomic variables such as money supply and interest rates do not have a significant impact on Seoul's apartment prices. Due to the high demand for housing and insufficient supply, there is a demand for buying a home regardless of macroeconomic booms or recessions. Second, tax and financial regulatory policies have an initial impact on the rise in apartment prices in Seoul, and their influence diminishes over time. Third, anti-speculation zones are expected to decrease apartment prices through the suppression of demand. However, these zones cause a rise in apartment prices. This could be understood as a lock-in effect due to the strengthening of capital gains tax. Fourth, the price ceiling did not decrease apartment prices. These findings propose that, in Seoul, where demand is high and supply is insufficient, the supply of high-quality and sufficient housing should be prioritized over various regulations such as tax regulations, financial regulations, anti-speculation zones, and price caps. Moreover, the findings provide an implication that city-specific real estate policies should be implemented for Seoul rather than regulation-oriented approaches in public policy.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.561-568
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• 2015

In this study we predict apartment prices per unit in Daegu-Gyeongbuk areas by spatial lag and spatial error models, both of which belong to so-called spatial regression model. A spatial weight matrix is constructed by k-nearest neighbours method and then the models for the apartment prices in March, 2012 are fitted using the weight matrix. The apartment prices in March, 2013 are predicted by the fitted spatial regression models and then performances of two spatial regression models are compared by RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error).

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.151-152
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• 2021

The sale price of apartment buildings is a key factor in the success or failure of apartment projects, and the factors that affect the sale price of apartments vary widely, including location, environmental factors, and economic conditions. Existing methods of predicting the sale price do not reflect the nonlinear characteristics of apartment prices, which are determined by the complex impact factors of reality, because statistical analysis is conducted under the assumption of a linear model. To improve these problems, a new analysis technique is needed to predict apartment sales prices by complex nonlinear influencing factors. Using machine learning techniques that have recently attracted attention in the field of engineering, it is possible to predict the sale price reflecting the complexity of various factors. Therefore, this study aims to conduct a basic study for the development of a machine learning-based prediction model for apartment sale prices.

• Journal of the Korean housing association
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• v.21 no.6
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• pp.81-90
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• 2010

The existing most studies on the apartment sales prices have been limited to relatively small size apartment complexes and have not categorized the apartment complexes based on the number of households. Some of them uses the apartment-related indices such as regional value estimates, sales unit price, and view right values. In the case of Seoul Metropolitan Area, the size of apartment complex has been growing to the level of large complex over more than 1,000 households through new town development, redevelopment and reconstruction. People prefers to choose a large scale complex instead of small complex based on their perception that a large scale apartment complex provides more conveniences in living. The result of this analysis revealed that the variables chosen as important determinants of the hedonic price model for large scale apartment complexes were square meters of apartment unit, rent/price ratio, number of bays, distance to the nearest subway station, and heating system method. This means that the sales price of apartment unit will be higher as the square meters of apartment unit increase, as the rent/price ratio decreases, as the distance to the nearest subway station increases, and as the number of bays increase.