• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.144-155
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• 2015

This paper is to understand investment's efficiency and performance of commercial real estate assets diversified by use and district. To do so, this paper divides two different commercial real estate markets(office build market and retail real estate market) in Seoul city by district into "GBD(Gangnam Business District), YBD(Yeouido Business District), and CBD(Central Business District)" and "GBD(Gangnam Business District), SBD(Shinchon Business District), and CBD(Central Business District)" respectively, configures these districts each other to structure portfolios as its portion varies based on Markowitz's Mean-Variance principle, and looks at risk-return relationship of portfolios to find out efficiency, performance, and optimal investment chosen based upon Sharpe's Performance Index. As a result, the portfolio configured by "10 to 30% of office building asset at CBD" and "70 to 90% of retail real estate asset at CBD" is shown to be the most optimal, suggesting the highest quarterly Sharpe's performance index of 2.7118~2.7776 with quarterly rate of return of 1.826%~1.838% and quarterly standard deviation of 0.573~0.589. Furthermore, it is obvious that diversified portfolio configured by use(office-retail) shows better investment performance than that by district with same type of asset(office-office or retail-retail). Finally, results driven from this research will play an important role to stimulate real estate and construction markets through enlarging ideas as to diversified investment by use and district on real estate indirect investment products.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.535-545
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• 2019

If the real estate auction market indicators are relevant and meaningful, they can be meaningful information to the real estate market in connection with general real estate. The purpose of this study is to examine whether time-supply logic is applied in auction market by identifying time series correlations for the number of auctions, the auction rate, and the auction price rate, which are major indicators of real estate auction market. The real estate types were classified into three categories: residential real estate, land, and commercial real estate. The monthly time series of auctions in the metropolitan real estate were compiled for 96 months. Based on this data, the auction market model for each type was established and the mutual influences between the indicators were analyzed. As a result, the supply and demand indicators, the number of auctions and the auction rate, showed the nature of supply and demand according to the supply and demand logic of the market. However, the correlation was high for residential real estate and relatively low for commercial real estate. the auction rate has a long-term impact on price indicators, especially residential real estate, which is quantitatively explanatory and significant. The three auction-related indicators differ in degree, but there is a correlation, especially for residential real estate, which can be useful information for policy making.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.219-226
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• 2012

Recently real-estate investment business is standing out as a new plan for creation of source of income. In this paper, we suggested appropriate real-estate investment strategy through the reconstruct case study of existing one-storied building. That is, we showed the efficient process of decision and propel to reconstruct and the key points for lease business and post management after building completion. Also, we analyzed the rate of return of commercial building investment in order to find its optimum dealing time. Therefore the results of this paper are expected to be a help to old ages and persons laying plans for a similar business.

• Korean Journal of Construction Engineering and Management
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• v.18 no.1
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• pp.90-99
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• 2017

This paper is to understand the impact of systematic risk on capitalization rate at office building market and retail real estate market in Seoul and to see if CAPM(Capital Asset Pricing Model) is applicable. For this, this paper considers eight different office building and retail real estate markets in Seoul city area, called GBD, YBD, CBD, and Other Business District, and GBD, SBD, CBD, and Other Business District, to find out if there is any positive-linearity between systematic risk and capitalization rate for each business district not. Then, this paper tries to verify applicability of CAPM to four office building markets and four retail real estate markets with Black, Jensen, and Scholes(1972)'s statistical methodology. At last, the result shows that there is positive linearity between systematic risk and capitalization rate only GBD except Others(YBD, CBD, and other business district) in office building market. In addtion, SBD and CBD, they could be figured out that it is not efficient market because increasing systematic risk declines capitalization rate in retail real estate market. However, CAPM is not applicable in all office building(GBD, YBD, CBD, and other business district) and retail real estate markets(GBD, SBD, CBD, and other business district) in Seoul.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.1
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• pp.127-133
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• 2013

The present study, the determinants of Lease-hold of the commercial real estate needs, interior and facilities, transit Lumpur, Daehangno and Dongsoong analysis of a questionnaire survey of mall tenant sales, density results affects the biggest presence, sales, traffic, density, the interior and facilities, and the impact was negligible. Also analyzed as a result of analysis of gender, age, education, Category, income differences combination of external factors and internal factors impact on key money that This result provides a new founder and stores the previous tenants want to contribute to the success of your business.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.29-37
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• 2020

This study analyzed the importance of factors according to the investor's perspective through a survey of residential real estate experts using AHP and fuzzy theory. Analysis results showed that rent, profitability, traffic accessibility, commercial and infrastructure, and financial regulation are important in common. By expert group, financial and credit groups cited profitability, rent, traffic accessibility, supply and tax benefits, construction and development groups cited traffic accessibility, rent, direct access, profitability, commercial area and infrastructure, and appraisal and evaluation groups cited rent, profitability, transportation accessibility, financial regulation and supply as the most important factors. This showed that it had a preference characteristic that was associated with work. In other words, it focuses most on the financial perspective in investment characteristics, and it values convenience such as accessibility to transportation and commercial districts and infrastructure as its location characteristics. In addition, it was found that easing financial regulations in the market is important to expand investment in real estate. This study aims to help the business feasibility analysis of residential property developers and rational decision-making of general investors who are consumers, taking into account the various perspectives of the expert group.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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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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• v.11 no.1
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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.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.28-35
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• 2013

The construction industry has introduced the Leadership in Energy and Environmental Design (LEED) rating system to promote objective evaluations of the sustainability of buildings. Three important values to consider when implementing sustainability are the associated environmental, social, and economic impacts. Recently, researchers have begun to investigate the real estate value of LEED certified buildings in terms of the rental cost, occupancy rate, cost per unit area, and resale value in order to better understand the economic benefits of the LEED rating system. However, the economic benefits also encompass economic effects such as the impact of LEED certified buildings on neighborhood real estate values surrounding the certified buildings. This research examines whether the enhanced real estate value of LEED certified buildings in New York City extends to surrounding commercial buildings, utilizing spatial analysis via a Geographic Information System (GIS) and the hedonic pricing method to derive meaningful economic relationships. The results provide practical insights into the economic effect of LEED certified buildings that will be of interest to city officials and planners, as well as the owners, developers, investors and other stakeholders of surrounding buildings.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.1
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• pp.71-85
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• 2013

This paper examines the impact of urban space structure on real estate markets, especially on commercial real estate markets. Based on a large scale of GIS dataset, volumes of each land use type are examined. This vast dataset enables 3-dimensional analysis of land use in the entire Seoul area, overcoming the limits of previous research relying on simple 2-dimensional analysis. After then, the Herfindahl index is used to calculate the level of mixed-uses. It analyzes whether a building price is influenced by circumjacent commercial buildings and its residential development pattern. The regression outcomes verify that a nearby area's development patterns make an impact on an office building price. It shows the possibility that a new-urbanism's argument can be actualized.