• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• 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.

• Journal of Digital Contents Society
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• v.7 no.4
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• pp.227-234
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• 2006

While most of the real estate information provided on the web is simply based on the information of the properties like addresses, locations, prices etc, this paper suggests methodologies for the development of a total information system of real estate which can provide, in an one-stop mode, value-added real estate information contents. The total information system of real estate designed in this paper includes a geological information system using a GIS, a property information system using photos and videos, a systemized legal consulting system, a web call center where customers can communicate with real estate experties on the web and lastly marketing analyses system using credit card data.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.4
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• pp.631-646
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• 2013

This paper empirically explores the pattern of causality between bank lending and housing prices in Korea over a period of the early 1990s to the end of 2000s by employing a long term cointegration and short-term time series regression analysis. Although the contemporaneous correlation between bank lending and housing prices is large, the analysis shows that the intense interaction between credit growth and bank lending to household arises from a growth in banking lending responding to an increase in housing prices. In addition, the regulatory change such as the introduction of financial constraints on bank loans such as LTV and DTI in the early and mid-2000s has played a significant role in stabilizing financial and real estate markets.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.151-157
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• 2013

Since the sub-prime mortgage crisis from the US in 2008, the Korean housing market has plummeted. However, the deposit prices of the Korean local lease contract, Chonsei, had been increasing. This increase of Chonsei prices can be a threat to low-income people, most of whom prefer to live in houses with a Chonsei contract. In the housing and Chonsei market, there are many stakeholders with their own interest, hence, simple thoughts about housing and Chonsei market, such as more house supply, will decrease house price, would not work in a real complex housing market. In this research, we suggests system dynamics conceptual model which consists of causal-loop-diagrams for the Chonsei market as well as the housing market. In conclusion, the Chonsei price has its own homeostasis characteristics and different price behavior with housing price in the short and long term period. We found that unless government does not have a structural causation mind in implementing policies in the real estate market, the government may not attain their intended effectiveness on both markets.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.199-205
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• 2020

As many government policies have been announced today regarding real estate, especially housing, interest in prices in the housing market has increased significantly. In this study, I would like to present the direction of government policies by analyzing the relationship among income instability, the psychological condition of real estate price changes and willingness to adjust real estate holding ratio. First, major variables were extracted through the prior study review, and using a survey, data were collected and path analysis was conducted. According to the analysis, the current income instability had a negative impact on the psychological condition of real estate price changes, and a positive influence on the willingness to adjust real estate holding ratio, but the psychological condition of real estate price changes did not have a statistically significant impact on the willingness to adjust real estate holding ratio. Thus, the difference analysis was conducted between groups by dividing the ages and the number of dependents respectively. According to the analysis, the impact of income instability and psychological condition of real estate price changes on willingness to adjust real estate holding ratio differed between groups divided by ages and number of dependents. The results of this analysis will help the government to establish real estate policies and help each household to use the analysis as basic data when they make a decision about real estate. On the other hand, this study has limitations that have only been conducted cross-sectional analysis and analyzing time series changes and differences in perception between regions are going to be conducted in a future study.

• 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.

• Journal of Cadastre & Land InformatiX
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• v.53 no.1
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• pp.65-81
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• 2023

In recent times, there has been a surge in media coverage regarding fraudulent real estate lease scams. These scams have been attributed to speculators who made imprudent gap investments, in response to the sudden decline in real estate prices. The government has established new measures to safeguard tenants and prevent fraudulent real estate lease schemes in response to the growing incidence of tenants falling victim to such scams. Although there have been active research efforts on laws and regulations aimed at protecting tenants, such as the Tenant Protection Act and leasehold registration orders, there is an urgent need for research on the consistency of information across public ledgers, as real estate fraud has surged due to information asymmetry among these ledgers. This study aims to explore matching methodologies using matching keys for building information from real estate-related public ledgers, such as the building register and the road name address ledger, as well as to examine ways to integrate these ledgers with other public ledgers.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.256-263
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• 2021

This paper studies the problems and improvements of government real estate policies. Moon Jae-in government shifted toward regulation and pledge to curb the tax imposed by speculators. It strengthened regulations on reconstruction and bank loans rather than supply, and raised capital gains taxes. As the government implemented measures, emphasizing political logic rather than the economy, the market is unstable and the economy is in a recession. Land has increased the vicious cycle of problems due to population growth, industrialization, urbanization, and wealth growth. Mis-established land policies not only accelerate land prices, but also accelerate the use of disordered land and lead to disruptions in the trading order. In addition, real estate is so difficult to recover from the land problem that it is difficult to contain water that has been spilled once. This is called the irreversible nature of land. Once the land price rises, it is difficult to regain control and reckless development leads to the destruction of the ecosystem, making it difficult to return. This is why such a complex real estate issue should not be implemented as if it were a punishment in a short period of time with government policies. This paper aims to examine the problems of real estate policies and to examine ways to improve them.