• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• International journal of advanced smart convergence
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• 제11권1호
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• 아태비즈니스연구
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• 제13권3호
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• pp.227-243
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• 2022

Purpose - This study surveyed real estate financial consumers and financial company staff regarding the components of the financial consumer protection system to seek detailed improvement plans for the Financial Consumer Protection Act. Design/methodology/approach - The Analytic Hierarchy Process (AHP) technique is applied. Findings - Both consumers and staff highly evaluated the importance of the preventive measures in the main classification factors. Regarding the sub-classification factors of preventive measures, consumers emphasized the responsible management of investment staff and financial institutions; however, the staff stressed the principle of effectiveness and efficiency. Regarding the elements of ex-post remedies, consumers answered that fast remedy would have a significant effect. At the same time, staff believed that punitive measures hinder free trading and investment activities. Regarding the sub-sub classification factors of prevention measures, the consumers value responsible management of staff and financial companies, while the staff tend to prefer the importance of the self-regulatory governance. Research implications or Originality - Based on the above results, financial regulatory authorities should find a balance between preventive and ex-post components once focusing on preventative measures. Our paper is one of the first research findings in this field of financial consumer protection system in Korea.

• 부동산연구
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• 제27권3호
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• pp.87-102
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• 2017

본 연구의 목적은 서울시 소재 비주거용집합부동산의 국세청 기준시가와 행정안전부 시가표준액의 과세형평성과 공동주택(아파트)과의 과세형평성 상호비교에 있다. 연구결과는 첫째, 비주거용집합부동산 시가표준액의 구청별 과표 현실화율(AR)의 격차가 크게 나타났다, 둘째, 비주거용집합부동산 시가표준액의 분산계수(COD)가 크게 나타나 수평적 불형평성이 있음이 확인되었다. 셋째, 비주거용집합부동산 시가표준액이 고가자산이 저평가되는 역진적 수직적 불형평성이 있음이 확인되었다. 따라서 비주거용집합부동산의 시가표준액의 평가 시 시가의 반영 및 토지와 건물을 합산 평가하여 과세형평성을 이루는 것이 필요할 것으로 판단된다. 본 연구를 통하여 비주거용부동산 실거래기반 공시제도로의 전환 시 제도개선에 기여를 할 것으로 본다.

• 한국전자통신학회논문지
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• 제6권5호
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• pp.757-763
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• 2011

본 연구는 부동산 경매에서 유치권 제도의 문제점을 파악하고 이를 바탕으로 절차법적측면을 중심으로 개선방안을 제시하고자 하였다. 부동산경매에서 유치권의 문제점을 해결하기 위해서는 집행법원은 부동산의 점유관계에 있어서 일정한 기준을 정할 필요성이 있고, 유치권의 성립여부를 판단하여 첫 매각기일에 공시하는 것이 타당하다. 또한 부동산 경매절차에서 유치권 권리신고를 의무화하는 제도적 개선이 요구되며, 유치권에 대한 현황조사를 강화하고, 매각물건명세서에 유치권에 관한 사항을 포함하여 보다 공신력을 부여해야 할 필요성이 있다. 더불어 허위 및 과장 유치권자에 대해서는 법적 처벌을 보다 강화하여 부동산경매절차에 있어서 유치권의 법적 안정성을 도모 할 필요가 있다.

• 한국산학기술학회논문지
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• 제12권9호
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• pp.3856-3868
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• 2011

최근까지 부동산 부분에 대한 전반적인 연구경향은 부동산시장 및 시장분석에 관한 논문이 중심을 이루었고, 부동산중개인의 직무능력 향상을 위한 교육프로그램개발에 대한 연구는 상대적으로 부족한 실정이었다. 따라서 본 연구는 충남지역(천안)을 중심으로 부동산 비즈니스 현장에서 경영활동 중인 부동산중개인을 대상으로 직무능력 향상을 위한 교육프로그램에 대한 니즈를 파악하기 위하여 실증분석을 하였다. 분석결과는 다음과 같다. 첫째, 부동산중개인의 직무능력 향상을 위한 교육내용에 대한 설문에서는 응답자의 대다수가 부동산 권리관계분석 및 법적지식, 부동산경영 회계, 부동산마케팅, 부동산 정책의 이해 등이 절실하게 필요한 것으로 나타났다. 이와 같은 변화의 움직임은 진화하는 고객들의 욕구를 충족시켜 줄 수 있는 최적의 대안이 교육훈련과정으로부터 발생됨을 인식하기 때문이라 하겠다. 둘째, 부동산마케팅 전략에 대하여 조사한 결과, 응답자의 대부분이 고객 욕구를 충족시키기 위해 새로운 형태의 마케팅전략이 필요함을 인식하고 있는 것으로 나타났다. 이와 같은 현상은 정보기술(Information Technology)등 패러다임 변화에 따라 인터넷 광고 등과 같은 새로운 형태의 마케팅 전략이 경영현장에서 요구되기 때문인 것으로 판단된다. 셋째, 산학연계를 통한 부동산관련 교육프로그램 필요성에 대하여 조사한 결과, 응답자 가운데 92%가 대학의 평생교육원에서 진행하고 있는 부동산 교육프로그램의 운영이 필요하다고 응답하였다. 또한 지역에 소재한 대학의 자원(resources)을 이용한 부동산중개인의 재학습 과정이 필요한 것으로 나타났다. 이외에도 효과적이고 효율적인 교육프로그램을 위해서는 대학 평생교육원의 인적 자원(human resources)을 활용한 부동산중개인의 직무능력 향상을 위한 '부동산 계약학과' 형태의 교육훈련시스템 운영을 요구하고 있는 것으로 나타났다. 넷째, 부동산중개인의 직무능력 향상을 위해 정규과정에서 수강하고 싶은 교육내용의 순위를 조사한 결과, 부동산경영(44.2%)과 부동산마케팅(42.3%)부분이 빈도가 높게 나타났다. 이와 같은 응답결과는 부동산경영과 부동산마케팅 마인드를 통해 고객들의 니즈를 파악하려는 의지를 보여주는 것이라 하겠다. 경영지식의 습득을 위해서는 정규과정 형태의 교육프로그램 운영보다는 비정규과정 형태의 교육훈련 프로그램을 더욱 더 선호하는 것으로 나타났다. 그러나 본 연구는 부동산중개인의 특성을 고려, 천안지역에서 부동산중개업을 운영하고 있는 대상만을 중심으로 표본을 추출하였다. 따라서 모집단을 보다 광범위하여 다양한 지역을 중심으로 조사할 필요가 있다. 천안지역 이외에도 부동산 비즈니스 활동이 활발하게 진행되고 있는 지역(평촌, 일산, 분당)과 부동산학을 전공하는 학부생과 대학원생들을 대상으로 부동산중개인의 직무능력 향상을 위한 니즈를 실증적으로 분석할 필요가 있다. 이를 통하여 부동산중개인들이 고객만족과 직무능력 향상을 위하여 필요로 하는 요인들을 측정함으로서 맞춤형 교육프로그램개발을 위한 정보를 얻을 수 있을 것이다. 이와 같은 연구결과를 통하여 측정된 직무능력 향상을 위한 교육프로그램개발 변수들은 대학의 부동산학 커리큘럼에 접목됨과 동시에 부동산학의 발전을 위한 실사구시적 정보(information)로 유용하게 사용될 수 있을 것이다.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.355-363
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• 2022

The debt maturity structure has a significant impact on a company's financial situation. Any debt maturity structure decisions substantially impact investment decisions due to changes in capital cost and dividend decisions due to cash flow consequences. This study used the system generalized method of moment (Sys-GMM) to investigate the debt maturity structure of real estate companies listed on the Ho Chi Minh Stock Exchange (HOSE) in the duration from 2008 to 20019. It found that the firm size, liquidity, and tangible assets affected the decision on debt maturity structure. The tangible asset had the most significant impact on the possibility for companies to access long-term loans. This finding revealed that the majority of the real estate companies listed on HOSE borrowed money from banks. Such decisions are most likely affected by the collateral. Another finding of the study is that financial institutions had a major impact on loan maturity structure, whereas the effects of the financial market were negligible. Besides, the real estate companies listed on HOSE seemed not to pay attention to changes in inflation, economic growth, and institutional qualities when deciding on the debt maturity structure.

• 국제학술발표논문집
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• The 7th International Conference on Construction Engineering and Project Management Summit Forum on Sustainable Construction and Management
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• pp.62-72
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• 2017

In this study, liner unit root tests and panel unit root tests to the ratio of city to regional house price were applied to examine the ripple effects across 28 cities in the Yangtze River Delta region. Then invert LM unit root tests with two structural breaks for 10 representative cities were conducted. The results showed that there is overwhelming evidence of the existence of ripple effect in the Yangtze River Delta region, while segmentation is restricted to a small group of cities in which there is no long-run relationship with the Yangtze River Delta region average; compared to no- and one-break case, there is overwhelming evidence of a ripple effect with the LM test with two structural breaks. Furthermore, the results of the Granger causality test showed that changes in house prices in Shanghai, Nanjing and Hangzhou have led to changes in house prices in other cities. The findings of this research make certain contributions to the improvements of research system of ripple effect among regional house prices in the Yangtze River Delta Region,and could be referenced by other markets of other cities.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.23-29
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• 2020

Multilayer sub-contracting is a significant practice among the world, including Hong Kong. When a principal contractor secured a project from a developer, the specific jobs will usually be breaking down and sub-contractors with the lowest bid [1]. The adoption of multilayer sub-contracting has been a controversy issue which is considered as a two-side blade. While certain studies have been carried out to examine both the contributions, damages and improvements for multi-layer subcontracting, the construction industry and researchers are still waiting for a solid measure to enhance the system. Hence, this research attempts to study the advantages, disadvantages, conducts a comparison between single and multilayer sub-contracting and measures of current Hong Kong construction industry based on literature review, questionnaire and in-depth interviews. To achieve the objectives, Analytic Hierarchy Process (AHP) and total weighted score methods are adopted to examine and rank the criterion. The findings of this study provide a good basis for understanding the major reasons and problems caused by the adoption of multilayer sub-contracting. Besides, the identified safety perspective explores a new perspective regarding to issues of multi-layer subcontracting, which will serve as a solid foundation for further research to enhance safety performance. Finally, the findings of measurements towards improvement of multilayer sub-contracting will also provide a solidsolution for construction industry.

• 한국환경복원기술학회지
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• 제5권5호
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• pp.45-57
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• 2002

This study aims to suggest the applied zoning system for preventing reckless developments due to individual industrial facility construction. According to the types of reckless developments which has been found in Hwasung, the applied zoning system including 4 kinds of districts can be proposed. Collective Inducement District can be designated to where many industrial facilities have already existed with poor infrastructure, and the other districts such as Residential Area Protection District, Reservoir-Around and Agricultural Area Protection District, or Landscape Protection District may be designated to the sites where reckless developments due to individual industrial facility construction may happen. This zoning system seems to be used as a guideline of new zoning of revised NLUPL(National Land Use and Plan Law) after the execution.