• 유통과학연구
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• 제12권9호
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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.

• 국토계획
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• 제54권1호
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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.

• 한국철도학회논문집
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• 제7권2호
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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.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2016년도 춘계 학술논문 발표대회
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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.

• 대한교통학회지
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• 제27권6호
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• pp.89-96
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• 2009

본 연구는 고속도로 인터체인지와 이격거리가 아파트 가격에 미치는 영향관계를 정의하기 위하여 수행하였으며, 국외 선행연구고찰을 바탕으로 연구방향을 설정하였다. 자료수집의 일관성을 확보하기 위하여 세력권을 단순화시키고자 서울외곽순환고속도로의 인터체인지 내측(서울지역)과 외측(경기지역) 주변 최대 6.0km까지의 아파트가격을 조사하였다. 서울외곽순환고속도로 전체를 대상으로 분석한 결과 고속도로 인터체인지의 거리에 따라 아파트가격이 일정한 추세로 분포함을 알 수 있었다. 구체적으로 고속도로 인터체인지 세력권 전체적 측면의 아파트의 가격은 a) 상승하나 b) 인터체인지 인접지역은 상대적으로 감소하며 c) 약 2.0~4.0km에 가장 높은 가격을 보이다 d) 그 이후 인터체인지와 멀어지면서 급격히 감소하는 것을 알 수 있었다. 또한 고속도로 인터체인지 이격거리와 아파트 가격의 추세는 기존 철도(지하철)의 역세권이 가지는 (-)의 1차 선형이었던 것과는 대비되게 2차 형태의 곡선(Polynomial Curve)과 유사한 것을 알 수 있었다.

• 디지털융복합연구
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• 제15권1호
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• pp.181-189
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• 2017

본 연구는 아파트 신규 공급 시 시장 내 경쟁력 있는 분양가격을 결정하기 위한 산정기준을 마련하였다. 도출된 모형은 건설사가 새로운 아파트 분양 시 가격결정을 토지비와 공사비 등 들어가는 비용에 일정 이윤을 합하는 방식이 아니고 최근 공급한 새로운 아파트 단지의 가격을 기준으로 결정하는 방법으로 이루어지고 있음을 고려하여 인근 아파트와 분양가격 비교분석의 모형을 만들었다. 아파트를 새로 공급할 때 결정요인에 대하여 1단계 4가지요인, 2단계 9가지요인, 3단계 25가지 요인으로 계층화하여 분석함으로써 새로이 공급되는 아파트의 가격을 산정하기 위한 기준을 마련하고자 한다. 분양가격 결정요인의 상대적 중요도도 파악하여서 신규공급 아파트 분양가격의 분양시 지표로 활용한다. 또한 2단계 9가지 요인특성을 기준으로 분양가 비교 모형을 만들어 실무에 적용할 수 있도록 제시하였다. 향후에는 마케팅 외적인 요인도 추가적으로 고려하여 일반화된 기준을 찾는 연구가 필요 할 것이다.

• 토지주택연구
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• 제12권4호
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• pp.41-59
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• 2021

본 연구에서는 부동산 가격, 거시경제변수, 부동산정책에 대한 이론적 고찰과 선행연구를 검토하고, 다변량 시계열분석방법으로 가장 널리 사용되는 VEC 모형으로 2003년 1월부터 2021년 6월까지의 월별 자료를 사용하였다. 이를 바탕으로 거시경제변수와 부동산 규제정책이 서울의 부동산 가격에 미치는 영향을 분석하였다. 본 연구의 실증분석 결과를 요약하면 다음과 같다. 첫째, 거시경제변수인 통화량, 금리 등은 서울아파트 가격에 큰 영향을 미치지 못한다는 것이다. 수요는 많고 공급이 충분히 이루어지지 못한 결과 거시경제의 호황, 불황과 상관없이 내 집 마련 등의 수요가 있다는 것이다. 둘째, 조세 및 금융규제정책은 서울아파트 가격 상승에 초기에 영향을 끼치고 시간의 지남에 따라 영향이 축소된다. 셋째, 투기지역지정은 서울아파트 가수요억제를 통한 가격 하락을 예상하지만 오히려 서울 아파트가격 상승을 초래하였다. 이러한 현상은 양도소득세 강화에 따른 매물잠김 현상으로 파악이 된다. 넷째, 분양가상한제는 서울아파트 가격하락에 별다른 영향을 끼치지 못하였다. 이러한 결과에 따르면 수요가 많고 공급이 부족한 서울은 조세규제, 금융규제, 투기지역규제, 분양가상한제 등의 여러 가지 규제보다는 우선적으로 양질의 충분한 주택공급이 우선시 되어야 한다. 또한 정책결정에 있어서 규제일변의 정책보다는 서울지역에 맞는 적합한 부동산 정책을 시행할 필요가 있다는 시사점을 제공한다.

• Journal of the Korean Data and Information Science Society
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• 제26권3호
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• pp.561-568
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• 2015

이 연구에서는 공간회귀모형 중 공간시차모형과 공간오차모형을 이용하여 대구 경북 지역 단위면적당 아파트 매매가격을 예측하였다. k-최근접이웃 (k-nearest neighbours)을 이용하여 공간가중행렬을 구축하였으며, 이를 이용해 2012년 3월의 단위면적당 아파트 매매가격에 대한 모형을 적합시켰다. 적합시킨 공간시차모형, 공간오차모형을 이용하여 2013년 3월의 단위면적당 아파트 매매가격을 예측하였으며 RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error)를 통해 두 모형의 성능을 비교하였다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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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.

• 한국주거학회논문집
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• 제21권6호
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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.