• Journal of Digital Convergence
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• v.17 no.9
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• pp.47-54
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• 2019

This study confirms the polarity of news articles on apartment prices using Opinion Mining which has widely been used for a big data analysis. The analyses were carried out utilizing internet news articles posted on the Naver for two years: 2012 and 2018. We proposed a sentiment analysis model and modeled a topic-oriented sentiment dictionary construction methods. As a result of analyzing the proposed sentiment analysis model, it was confirmed that there was a difference according to the tendency of the media companies in selecting social issues at the time of rising apartment prices. At the same time, we were able to find more affirmative articles in the media companies which share similar sentiment with the government in charge. In this paper, we proposed a sentiment analysis model that can be used in real estate field and analyzed the polarity of unformatted data related to real estate. In order to integrate them into various fields in the future, it is necessary to build the sentiment dictionaries by themes, as well as to collect various unformatted data over extended periods.

• Korean Journal of Construction Engineering and Management
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• v.18 no.6
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• pp.47-56
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• 2017

As of 2017, there are 848 million households living in apartment and 55.87% of Aged apartments over 15 years old. The allowable standard for remodeling the apartment is more than 15 years and the market for remodeling the apartment will continue to increase. For the success of the remodeling project feasibility analysis is important but the existing feasibility analysis of new construction and reconstruction is being used for remodeling feasibility analysis. Therefore, it is necessary to study the feasibility analysis of customized remodeling without increasing the number of households according to the building law. Purpose of this paper is to develop a feasibility analysis methodology for customized remodeling projects by deriving the factors affecting the formation of land prices and building prices in apartment. According to the concept of price formation of the apartment, the analysis method of the customized remodeling of the old apartment using the factors affecting the Land Price Indexes, Officially Assessed Individual Land Price, House Price Indexes, and Officially Assessed Individual House Price was suggested. The Stair Price Algorithm developed in this research can be utilized at the stage of selecting remodeling contractors after the remodeling housing association is established.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.517-524
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• 2020

The study examines factors affecting apartment prices in the real estate market of Ho Chi Minh City, Vietnam. The study uses primary data based on surveys of customers who have traded successfully, and collects transaction data from real estate trading companies that are the top investors in Ho Chi Minh City real estate market. The collected data include 384 observations in a total of 24 districts, detailing that each district surveyed on a minimum of four projects, each project carried out a survey on a minimum of four apartments. The survey collected 339 valid questionnaires for analysis and model testing. This study employs multivariate regression with the data of 339 observations. The research results reveal that five significant factors affect positively the price of apartments in Ho Chi Minh City - apartment area, toilet and bedroom, apartment floor, reference price, and apartment interior. Besides, there are three significant factors affecting negatively the price of apartments - next price trend, distance to city center, and potential building. From the results, the research proposes solutions in the pricing of apartments in the real estate market in Ho Chi Minh City - better information system, a real estate transaction index, and stricter management of small brokerage activities.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.27-35
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• 2021

The study intends to identify the effects of infrastructure property on an apartment house by analyzing the price variation affected by factors constituting the quality of the transit services of each individual station in urban railway system based on hedonic price model. The research findings indicate that the prices depending on the transit users have increased from 7.8% to 12.2% in Seoul and decreased from 6.1% to 12.9% in Gyeonggi, which implies that a lower number of transfer users has a positive effect on housing prices in Seoul unlike Gyeonggi. It also is noteworthy that the distance to the urban railway station had a negative effect on housing prices in Seoul and positive effect in Gyeonggi. Taking these results together, in Seoul, the increase in the number of transit users had a negative effect on neighborhood housing prices. When analyzed by segments, however, an additional negative effect was observed only in the apartments located within the radius of 100 meters. It is also found that the impact of transit users varies according to the regional characteristics, such as the density of commercial facilities and the population density, and the spatial extent of negative effect also showed regional differences. These results provide implications for the planning of new stations, new cities, and land use of existing areas around stations.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.61-93
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• 2019

This study is to apply Hedonic Price Method in analyzing residents' preferences for three types of urban green space (UGS, rivers, urban parks, and forests) near the apartment complexes in Seoul. Based on hedonic price function estimation results, residents in Seoul preferred for the urban amenity that was provided by the view and accessibility (in terms of both within 10 minutes and distance) of rivers and urban parks near the apartment complexes, but not forests. The annual benefits calculated using the shadow prices are about 550~600 thousand won for the urban park views and about 800 thousand won for the accessibility, which is 2-3 times higher than river views and accessibility. On the other hand, forest views and accessibility did not have significant effects on apartment prices, except the view of Bukhan mountain for the residents of Gangbuk area. Based on the empirical results, Seoul residents' preferences for urban parks would have important implications for the urban park sunset program that will be initiated from July 2020.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.433-442
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• 2021

This study analyzed the housing market before and after the New Stay movement which was introduced in 2015. In this study, the territories having a New Stay Project and non-involved territories were analyzed based on the apartment price changes according to supply for 12 months before and after the movement date. This study used the difference-in-differences statistical technique. A comparison was carried out in Michuhol-gu, Incheon between Dowha-dong where a New Stay Project was executed, and Sungeui-dong where no project was executed, based on the movement date. It was seen that the price level in the former territory was higher than the latter demonstrating that the introduction of the New Stay Project in Dowha-dong lowered the apartment prices nearby (Sungeui-dong). A comparison in Gwonseon-gu, Suwon between Omogcheon-dong where a New Stay Project was executed and Gosaek-dong where there was no such project, based on the movement date showed that the introduction of the New Stay Project in Omogcheon-dong seemed to lower or stabilize the apartment prices nearby (Gosaek-dong). These results imply that the apartment prices in nearby areas can be stabilized if the supply volume of company-type rental houses is increased.

• KDI Journal of Economic Policy
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• v.26 no.1
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• pp.3-33
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• 2004

This paper investigates the effects of inflation on real estate prices, particularly the discrepancy between the sales and chonsei prices of housing, in an economy in which real interest rates are secularly declining due to the fall in capital productivity. When real interest rates fall, real estate prices rise relative to chonsei prices, and thus the well-known adverse effect of inflation, or the discrepancy between the value of financial assets (or chonsei principal) and the value of real assets (or real estate), is aggravated although the monetary authority maintains the same rate of inflation. This theoretical prediction can help explain the trend of the ratio of apartment sales prices to chonsei prices. That is, the stabilization of inflation relative to real interest rates appears to have contributed to the secular stabilization of this ratio in the 1990s, while the fall in real interest rates appears to have led to the rise of this ratio since 2001.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.488-497
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• 2015

The recent increase of chonsei has raised the degree of lease burden of households, and a new residential lease price index needs to be introduced to measure such degree of lease burden. In order to convert the burden into an index, the calculation method of the K-HAI, which is announced by the Korea Housing Financing Corporation, is applied by replacing house purchase with lease. From the calculation, the residential lease prices index of the first quarter of 2014 is estimated to be approximately 114, indicating that the cost of lease exceeds 35% of income. The result of analysis on the trend of the residential lease prices index from the first quarter of 2012 to the present in Seoul indicates that the residential lease prices index in Seoul has continued to increase, compared to that of the entire country. The results of this study will be a foundation to find a solution for the stabilization of chonsei and investigate the degree of lease burden by region when establishing a sustainable housing policy.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.89-99
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• 2014

Correlation of KOSPI from stock market and Apartment Prices in Seoul HPPCI from real estate market has been found from this research. Furthermore, from the comparison of those indicators' flows, certain precedence was found as well. The purpose of this research is to analyze correlation and precedence among KOSPI, Apartment price in Seoul, HPPCI and CLI. As for predicting KOSPI of stock market and real estate market, it is necessary to find out preceding indices and analyzing their progresses first. For 27 years from the January 1987 to December 2013, KOSPI has been grown by 687%, while CLI showed 443%, Apartment of Seoul showed 391%, HPPCI showed 263% of growth rate in order. As the result of correlation analysis among Apartment of Seoul, CLI, KOSPI and HPPCI, KOSPI and HPPCI showed high correlation coefficient of 0.877, and Apartment of Seoul and CLI showed that of 0.956 which is even higher. Result from the analysis, CLI shows high correlation with stock and real estate market, it is a good option to watch how CLI flows to predict stock and real estate market.

• 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.