• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.163-180
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• 2022

Real estate data can be big data. Because the amount of real estate data is growing rapidly and real estate data interacts with various fields such as the economy, law, and crowd psychology, yet is structured with complex data layers. The existing Relational Database tends to show difficulty in handling various relationships for managing real estate big data, because it has a fixed schema and is only vertically extendable. In order to improve such limitations, this study constructs the real estate data in a Graph Database and verifies its usefulness. For the research method, we modeled various real estate data on MySQL, one of the most widely used Relational Databases, and Neo4j, one of the most widely used Graph Databases. Then, we collected real estate questions used in real life and selected 9 different questions to compare the query times on each Database. As a result, Neo4j showed constant performance even in queries with multiple JOIN statements with inferences to various relationships, whereas MySQL showed a rapid increase in its performance. According to this result, we have found out that a Graph Database such as Neo4j is more efficient for real estate big data with various relationships. We expect to use the real estate Graph Database in predicting real estate price factors and inquiring AI speakers for real estate.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.49-55
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• 2019

This study is on the trend of real estate market using text mining and big data. The data were collected through internet news posted on Naver from August 2016 to August 2017. As a result of TF-IDF analysis, the frequency was high in the order of housing, sale, household, real estate market, and region. Many words related to policies such as loan, government, countermeasures, and regulations were extracted, and the region - related words appeared the most frequently in Seoul. The combination of the words related to the region showed that the frequencies of 'Seoul - Gangnam', 'Seoul - Metropolitan area', 'Gangnam - reconstruction' and 'Seoul - reconstruction' appeared frequently. It can be seen that the people's interest and expectation about the reconstruction of Gangnam area is high.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.499-506
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• 2017

Consideration of influential factors through division of capital market sector and interest rate sector to find and resolve the problems in current housing market and leasing market will become an important index to prepare measures for stabilization of housing sales market and housing lease market. Furthermore, a guideline will be provide you with preliminary data using Big Data to prepare for sudden price fluctuation because expected economic crisis, stock market situation, and uncertain future financial crisis can be predicted which may help anticipate real estate price index such as housing sales price index and housing lease price index.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.47-53
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• 2021

With increasing interest in the 4th industrial revolution, data-driven scientific methodologies have developed. However, there are limitations of data collection in the real estate field of research. In addition, as the public becomes more knowledgeable about the real estate market, the qualitative sentiment comes to play a bigger role in the real estate market. Therefore, we propose a method to collect quantitative data that reflects sentiment using text mining and k-means algorithms, rather than the existing source data, and to predict the direction of housing index through artificial neural network learning based on the collected data. Data from 2012 to 2019 is set as the training period and 2020 as the prediction period. It is expected that this study will contribute to the utilization of scientific methods such as artificial neural networks rather than the use of the classical methodology for real estate market participants in their decision making process.

• Korea Real Estate Review
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• v.28 no.1
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• pp.65-77
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• 2018

This paper analyzes the redemption risk of renters by estimating the LTV and CoLTV with finance market big data (individual credit information) and housing market big data (actual housing transaction data). The analysis showed that when using LTV, the redemption risk was higher in the case of the monthly renter than of the chonsei renter. On the other hand, when using CoLTV, the chonsei renter had a higher redemption risk than the monthly renter. This implies that there is a need to activate a guarantee system, such as risk management using the CoLTV index and the chonsei deposit return guarantee because it is possible for renters to experience losses on their chonsei deposits due to the higher redemption risk. Another implication is that the risk manager should consider the individual characteristics of renters because of the different effects of the redemption risk stemming from the characteristics of the rental contract and the personal characteristics of the renters. CoLTV was just a concept until this study calculated it using housing big data and actual housing transaction information. It helps identify the redemption risk through the characteristics of renters and their contracts.

• Knowledge Management Research
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• v.22 no.1
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• pp.105-120
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• 2021

Recently, there has been a dramatic change in real estate markets with the development of information technology. The word, Proptech, is defined as the real estate transaction innovation motivated by various types of information technology including artificial intelligence, sensing technology and big data. The objective of this study is to provide a value-creation framework for Proptech business based on the understanding of how and what types of values are created and shared, which gives organization to develop strategies and business models. And a new classification scheme of Proptech business is also suggested based on the recognition of created values along the development of Proptech business. Then, the proposed matrix is applied to derive the business value such as intangibility value, relational value and enhancement value with the case analysis on the each components of Proptech business.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.89-97
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• 2021

Blockchain is designed to collect and store the data recorded on the network in one block unit, and is connected and stored back and forth, and its form is similar to how the blocks are connected, so it is called a blockchain. Many companies are trying to popularize blockchain-based services at home and abroad, and blockchains are used in various industries. This study introduces the technical characteristics of the blockchain and deals with application services utilizing the blockchain. Introducing 5 types of blockchain architecture and core technologies and introducing blockchain application services that are used in payment services, blockchain service networks, blockchain real estate platforms, identity verification, cryptocurrency, diamond distribution path tracking, and blog information recording. do. It is expected to increase the understanding of the blockchain and provide usefulness in future blockchain research and service development.

• Informatization Policy
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• v.25 no.4
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• pp.108-120
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• 2018

With the rise of the Fourth Industrial Revolution and I-Korea 4.0, both of which pursue strategies for industrial innovation and for the solution to social problems, the real estate industry needs to change in order to make effective use of available space in smart environments. The implementation of smart spaces is a promising solution for this. The smart space is defined as a good use of space, whether it be a home, office, or retail store, within a smart environment. To enhance the use of smart spaces, efficient decision-making and well-timed and accurate interaction are required. This paper proposes a smart space based on platform which takes advantage of emerging technologies for the efficient storage, processing, analysis, and utilization of big data. The platform is composed of six layers - collection, transfer, storage, service, application, and management - and offers three service frameworks: activity-based, market-based, and policy-based. Based on these smart space services, decision-makers, consumers, clients, and social network participants can make better decisions, respond more quickly, exhibit greater innovation, and develop stronger competitive advantages.

• 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.