• Journal of Korea Society of Industrial Information Systems
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• v.21 no.6
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• pp.41-51
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• 2016

For the competitive advantages of an real estate web-site, the study investigated the comparison between the real estate portal sites and the independent real estate web-site based on the users' preference. The experiment was conducted with 52 participants for the total 5 sites including two real estate portal sites (Naver real estate, Daum real estate), and independent real estate web-site (the real estate 114, the seoul real estate square, the MK estate). As a result, this study becomes very practical to web-site companies in terms of users' preferences, even though most of real estate web-site studies have focused on credibility. Therefore, it is considered to contribute largely for the advancement of real estate sites developing differentiation strategy through comparative analysis between real estate portal sites and independent real estate web-site.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.41-56
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• 2006

The real estate portal has grown into a successful e-Business model that is combined on and off line. Although IT technologies have shown rapid growth, the real estate portals have failed to satisfy the expectations of the Internet users. Based on Michael Porter's competitive forces framework, this study proposes five competitive strategies for continuing growth of the real estate portals. First, to strengthen bargaining power against supplier, buyer and potential new entrants, the real estate portals need to construct a basic network that is cost efficient and maintains real estate goods and makes profits by collaborative deals. Second, strengthen brand value and endeavor to escape from dependency on the Internet portals. Third, develop services to consider changed circumstances and give a lot of sources to make profit to real estate agencies. Fourth, concentrate on marketing to draw in the Internet users and adapt strategies that have been successful in other fields. Finally, real estate fields can seek out ideas for developing new business models from other successful e-Business models and should benchmark them to reduce expenses to a minimum and increase benefits to a maximum.

• 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 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.185-190
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• 2024

In 2022, we can see the real estate market in Korea going down. Corona 19 and the Russian invasion of Ukraine are cited as the biggest causes for this. These two problems ignited the economic recession, causing prices to fall and subsequently raising exchange rates and interest rates. Due to the aforementioned problems in the previously active real estate market, the number of actual transactions has decreased, resulting in a decline in the real estate market due to high interest rates. Data provided by the public data portal, KOSIS, and the Seoul Metropolitan Government were collected through Logstash, transferred to Elasticsearch, and visualized inflation, exchange rates, and loan interest rates using the dashboard function provided by Kibana, to analyze causes and derive results. In addition, three specific apartments in Nowon-gu and Jongno-gu, which have the highest number of actual transactions in Seoul, are selected and the actual transaction prices that change every month are displayed in the Data Table.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3966-3991
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• 2018

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.102-119
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• 2016

This paper aims at analyzing the role of Korean ethnic network in the settlement process of Korean immigrants, and elucidating their activation strategies. through the case study of Korean Immigrants in London metropolitan area. The main results of this study are as follows. Firstly, the majority of respondents use Korean ethnic networks in the initial immigration process. Secondly, respondents more frequently use Korean ethnic network in the activities such as church, shopping and education. Thirdly, considering the cognition of respondents about the necessity of Korean ethnic networks, respondents prefer supports of Koran government as activation strategies of Korean ethnic network. Finally, a model of activation strategies of Korean ethnic networks is proposed, which is a differentiated and integrated model according to the maturity stage.

• Journal of Distribution Science
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• v.21 no.8
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• pp.83-96
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• 2023

Purpose: This study examined the relationship between the factors (Credibility, Usability) and user Innovativeness of the ChatGPT on TRA (Theory of Reasoned Action; Subjective Norm, Attitude) and Switching Intention. TRA and Innovation Diffusion Theory (IDT) were used. Research design, data and methodology: From April 26 to 27, 2023, an online panel survey agency was commissioned to conduct a survey of GhatGPT users in their 20s and 40s in Korea, and a total of 210 people were used for the final analysis. Verification of the research model was performed using SPSS and AMOS. Results: First, ChatGPT factors (Credibility, Usability) were found to have positive effects on TRA (Subjective Norm, Attitude). Second, ChatGPT user Innovativeness was found to have a positive effect on TRA (Subjective Norm, Attitude). Third, ChatGPT users' TRA (Subjective Norm, Attitude) were found to have positive effects on Switching Intention. Conclusions: These results mean that the superior Usability and Credibility of ChatGPT and the Innovativeness of users have a significant effect on the Switching Intention from existing Portal Service (Naver, Google, Daum, etc.) to ChatGPT. Generative AI such as ChatGPT should strive to develop various services such as improving the convenience of functions so that innovative users can use them easily and conveniently in order to provide services that meet expectations.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.219-220
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• 2022

In order to meet the trend, construction public data are already disclosing not only data generated at the construction site but also various data ranging from inspection reports and public construction contracts through multiple portals. However, unlike the excellence of the open performance evaluated by the number of data, it is difficult to evaluate the specific level of disclosure because there is no case of analyzing the quality, ease of use, and possibility of further opening of the public construction data set. On the other hand, performance measurement is already performed using an internationally agreed evaluation method in different fields such as real estate, population, and environment. So it is essential to analyze the current status of public data openings in the construction field and to derive improvement tasks. Therefore, this study conducted a survey of researchers with the highest system utilization targeting representative public data open systems in the construction field, such as E-AIS(세움터) and KISCON. To ensure fairness and increase comparability, the questionnaire was composed using evaluation items on implementing public data conducted annually by the World Wide Web Foundation, an international non-profit organization. With these responses, we investigated the status of public data disclosure and opinions on data quality and derived tasks to improve public data disclosure in construction through the analysis of the results.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.93-107
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• 2019

The open data portal collects data created by public institutions and opens and shares them according to related laws. With the activation of the Fourth Industrial Revolution, all sectors of our society are demanding high quality data, but the data required by the industry has not been greatly utilized due to the lack of quantity and quality. Numerous data collected in the real world can be implemented in cyber physical systems to simulate real-world problems, and alternatives to various social issues can be found. There is a limit to being provided. Location intelligence is a technology that enables existing data to be represented in space, enabling new value creation through convergence. In this study, to present location intelligence of open data, we surveyed the status of location information by data in open data portal. As a result, about 60% of the surveyed data had location information and the representative type was address. Appeared. Therefore, by suggesting location intelligence of open data based on address and how to use it, this study aimed to suggest a way that open data can play a role in creating future social data-based industry and policy establishment.