• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.419-430
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• 2023

Currently, NFTs have no dominant application except for the proof of ownership for digital content, and it also have small liquidity problem, which makes their price difficult to predict. Real estate usually has very high barriers to investment due to its high pricing. Real estate can be converted into NFTs and also divided into small value fungible tokens (FTs), and it can increase the the volume of the investor community due to more liquidity and better accessibility. In this document, we implement and design a system that allows ordinary users can invest on high priced real estate utilizing Black Litterman (BL) model-based Portfolio investment interface. To this end, we target a set of real estates pegged as collateral and issue NFT for the collateral using blockchain. We use oracle to get the current real estate information and to monitor varying real estate prices. After tokenizing real estate into NFTs, we divide the NFTs into easily accessible price FTs, thereby, we can lower prices and provide large liquidity with price volatility limited. In addition, we also implemented BL based asset portfolio interface for effective portfolio composition for investing in multiple of real estates with small investments. Using BL model, investors can fix the asset portfolio. We implemented the whole system using Solidity smart contracts on Flask web framework with public data portals as oracle interfaces.

• Korean Journal of Construction Engineering and Management
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• v.11 no.1
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• pp.101-112
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• 2010

The Korean real estate market currently is experiencing a slowdown due to the global economic crisis which has resulted from subprime mortgage lending practices. In response, the Korean government has enforced various policies, based on intend to deregulate real estate speculation, such as increasing the Loan to value ratio (LTV) in order to stimulate housing supply, demand and accompanying housing transactions. However, these policies have appeared to result in deep confusion in the Korean housing market. Furthermore, analyses for housing market forecasting particularly those which examine the impact of the international financial crisis on the Korean real estate market have been partial and fragmentary. Therefore, a comprehensive and systematical approach is required to analyze the real estate financial market and the causal nexus between market determining factors. Thus, with an integrated perspective and applying a system dynamics methodology, this paper proposes Korean Real Estate and Mortgage Market dynamics models based on the fundamental principles of housing markets, which are determined by supply and demand. As well, the potential effects of the Korean government's deregulation policies are considered by focusing on the main factor of these policies: the mortgage loan.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.2
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• pp.243-252
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• 2020

Recently, the proportion of housing lease has been increasing to an overwhelming level in line with the increase of single-person households and the change in the form of housing. In the normal case, the use of rental-type housing is subject to a housing lease agreement through a licensed real estate agent. In the event of a transaction conclusion, licensed real estate agent shall issue a contract containing the personal information of the lessee, the renter, and the licensed real estate agent to the transaction party. In this case, it is necessary for the lessee to provide the contract to a third party. This paper analyzes relevant laws and regulations and the status of housing transactions, focusing on personal information processed between offline housing lease agreements. And when issuing a contract through IRTS, we propose a way to protect personal information by providing a third party in three forms: information Data Subject-based, Purpose of usage-based De-identification, and Certificate of Contract.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.11
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• pp.281-288
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• 2018

The block chain, which is characterized by a distributed ledger that stores the same data in several places, has various technical features including security and stability. Due to these characteristics, various researches are being conducted on the application of the block chain. In this paper, we consider the issues to be considered for applying the block chain to the Korea Real Estate Administration Intelligence System (KRAS). Based on this, we propose a block chain reference model for KRAS including a system configuration method and a consensus algorithm.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.214-224
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• 2023

Fraudulence, cheating, and deception can occur in the commercial real estate (CRE) industry, besides the difficulty in searching for and transferring properties while ensuring the operation is processed through an authoritative source in a trusted manner. Nowadays, real estate transactions use neutral third parties to sell land. Indeed, properties can be sold by the owners or third parties multiple times or without a proper deed. Moreover, third parties request a large amount of money to mediate between the seller and buyer. Methods: We propose a new framework that uses a private blockchain network and predefined BPMN instances to enable the fast and easy recording of deeds and their proprietary transfer management controlled by the government. The blockchain allows for multiple verifications of transactions by permitted parties called peers. It promotes transparency, privacy, trust, and commercial competition. Results: We demonstrated the easy adoption of blockchain for land registration and transfer. The paper presents a prototype of the implemented product that follows the proposed framework. Conclusion: The use of Blockchain-based solutions to resolve the current land registration and transfer issues is promising and will contribute to smart cities and digital governance.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.271-278
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• 2020

Modern financial services go beyond the stage of internet banking, and new concepts of financial transactions such as Internet of Things, mobile banking, electronic payments, and fintech have emerged. As a result, banks are less influential in financial transactions, and changes are being demanded. In the present era, the basic business of banks has decreased, and it is transforming into a space where both consumer finance work and reside. The bank office stands for the brand image of the bank, and it is represented by trust with customers in the basic business of financial transactions, and the rise in real estate value is a natural social phenomenon due to the nature of the location and location of real estate owned by the bank. The business method and space of the bank office that meets the new paradigm of the modern society is an inefficient space only for the convenience and rest of consumers, but it must be used as a variety of spaces suitable for the region to increase the functional value of the bank office. Through this study, as a convenience space for consumers, various service facilities should be introduced to understand the characteristics of the region as a convenience space for consumers, and various service facilities should be introduced to meet the needs of consumers, and the bank office should be improved as a complex service space for local residents.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• 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 Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.35-41
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• 2015

Real estate appraisal methods include profit-based, cost-based and comparison-based measures. The purpose of this study is to scientifically quantify the comparison-based method mostly utilized in valuating real estate property among the appraisal methods. The comparison method is to estimate the value of target property from other previously-traded property cases by comparing and adjusting their temporal gap, spatial gap and space-time gap. In appraisal practices, this comparison method is used generally for land property. If based on previous transactions; prices, time point of transaction, region and individual factors were analyzed to valuate. If based on official land values; official value, time point, region and individual factors are analyzed. Of these, the individual factors are an important process of comparing individual characteristics where real estate appraisers' subjective assessment could intervene. Though appraisers, as experts make generally precise assessment, still, it is a subjective judgment open to difference between appraisers themselves, causing disputes from time to time. In this recognition, the study seeks to quantify such a subjective assessment of appraisers by running GIS analysis on individual factor components including street condition, access condition;and plotting condition.