• Land and Housing Review
• /
• v.12 no.2
• /
• pp.33-45
• /
• 2021

Due to recent changes in government policy, officetels have received attention as alternative assets, along with the uplift of office and apartment prices in Seoul. However, the current officetel price indexes use small-size samples and, thus, there is a critique on their accuracy. They rely on valuation prices which lag the market trend and do not properly reflect the volatile nature of the property market, resulting in 'smoothing'. Therefore, the purpose of this paper is to create the officetel price index using transaction data. The data, provided by the Ministry of Land, Infrastructure and Transport from 2005 to 2020, includes sales prices and rental prices - Jeonsei and monthly rent (and their combinations). This study employed a repeat sales model for sales, jeonsei, and monthly rent indexes. It also contributes to improving conversion rates (between deposit and monthly rent) as a supplementary indicator. The main findings are as follows. First, the officetel price index and jeonsei index reached 132.5P and 163.9P, respectively, in Q4 2020 (1Q 2011=100.0P). However, the rent index was approximately below 100.0. Sales prices and jeonsei continued to rise due to high demand while monthly rent was largely unchanged due to vacancy risk. Second, the increase in the officetel sales price was lower than other housing types such as apartments and villas. Third, the employed approach has seen a potential to produce more reliable officetel price indexes reflecting high volatility compared to those indexes produced by other institutions, contributing to resolving 'smoothing'. As seen in the application in Seoul, this approach can enhance accuracy and, therefore, better assist market players to understand the market trend, which is much valuable under great uncertainties such as COVID-19 environments.

• Korean Journal of Construction Engineering and Management
• /
• v.22 no.4
• /
• pp.59-65
• /
• 2021

Since the 2016 "Housing Act Partial Amendment" and the "2018 Housing Comprehensive Amendment Plan", interest in the pre sale system and post sale system of apartment houses has been on the rise. In order to compare the advantages and disadvantages of the pre sale system and the post sale system of apartment houses, and to establish the basis for the institutional policy of the post sale system, a questionnaire survey method was used for tenants of the apartment house from the public side, and issues of time and cost. The time series analysis method is intended to suggest an appropriate time for payment of contributions. Accordingly, through a review of existing theories and literature, the post sale system of public and private institutions was organized, and through a questionnaire survey, the path to securing pre sale money, product information of the model house, and the degree of awareness of the effect of the post sale system were investigated. For the post sale fund support and payment method, it is necessary to increase the commercial line for existing financiers from the user's point of view, and it is necessary to operate in consideration of the economic power of the pre sale market by region. Both 60% post sale and 80% post sale have a price range of up to KRW 10 million, and the total interest rate is 5.0%, and the annual interest rate is about 2.8% for 60% post sale, and about 2.1% for 80% post sale, which is lower than the current 3.1%. I need an interest rate. The research is a perception survey targeting a total of 5,213 households in a sample of after sale apartments in public institutions. As the actual values are analyzed using a time series on the effects of market supply and demand and market prices, there is a limit to applying them to prospective residents of private apartments. In addition, to respond to first time tenants, a questionnaire survey was conducted on five complexes that have moved in within the last five years.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.10
• /
• pp.4543-4550
• /
• 2012

The purpose of this paper is to suggest the improvement method to achieve the interactive development effect between high-tech industrial complex and its surrounding areas. For this reason, this paper has conducted an empirical analysis to find out relevant comprehensive factors, affecting nearby housing prices from such plans, especially by reviewing 'Seoul Digital Industrial Complex.' This paper is truly differentiated from previous research by adding a new perspective 'diverse location characteristics', as it focuses not only on 'high-tech facility' characteristics, but also on 'urban function facilities', including 'transportation facilities', 'amenity facilities', 'security facilities', etc. Then, SPSS Version 18.0 was utilized to conduct the multiple regression analysis with the accumulated relevant data and several results were drawn out as following: Firstly, 'deterioration level', 'brand of apartment', etc. are found to be major influencing factors. Secondly, 'educational facilities', 'transportation facilities', 'Cultural & Sports facilities', 'Amenity facilities', etc. are found in the sector of 'location characteristic'. Lastly, 'leading companies within the industrial complex', were also found, affecting nearby housing prices. Therefore, when a housing development project is planned to grant the interactive development effect to high-tech industrial complex and its surrounding housing areas, it is necessary to consider variety factors, such as comprehensive location characteristics and housing complex characteristics, and also proper housing policy measures should be devised in accordance with the actual demand of employees and their dependant family members.

• Journal of Family Resource Management and Policy Review
• /
• v.13 no.3
• /
• pp.103-122
• /
• 2009

The purpose of this study is to investigate what owners think of their housing and their community by scrutinizing households, thereby leading to a conclusion of how these communities have developed and the advantages they provide to their owners. This study was conducted by thoroughly interviewing one member from each of a total of 10 households living in apartments and villas in the Gangnamgu area of Seoul between August 5, 2008, and September 25, 2008. The results can be summarized as follows: First, all interviewees were female, within the range of 40 years to 50 years of age, and with high educational backgrounds. They were living in mid-level income or higher households but were characterized by frugal attitudes. Second, their households held a personal and familial meaning to them, one of providing replenishment, rest, and a place to share diverse feelings with their family. Certain factors such as being a convenient place for education, rising housing prices, a large area of greenery made possible by Yangjae Cheon, and so forth contributed to giving a special significance to their housings. The interviewees all thought the convenience of education and the rising housing prices in the Gangnam area were the most important factors. Third, the interviewees indicated the following sociocultural perspectives of Gangnam housings: They provide a good environment for education due to the densely clustered academies in the Daechi-dong area. There are many opportunities to meet neighbors with similar educational and economic backgrounds. There are vast areas of greenery such as Yangjae Cheon. There is access to highly advanced cultural and shopping facilities such as COEX, Seoul Arts Center, etc. There are no amusement centers located near the housing districts. There are convenient transportation methods and facilities. They are subject to jealous looks from people living outside the Gangnam area. Lastly, it seems that no significant community spirit exists among the dwellers of each apartment or villa. However, matters of self-interest such as construction problems, which contribute greatly to creating personal wealth, were exceptions when the dwellers united as a single household.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• autumn
• /
• pp.353-358
• /
• 2001

Since the financial crisis broke out, liquidity has become the critical issue in housing construction industry. In order to secure liquidity, it is prerequisite to precisely forecast cash flow. However, construction companies have failed to come up with a systematic process to manage and forecast cash flow. Until now, companies have solely relied on the prediction of profits and losses, which is carried out as they review business feasibility. To obtain more accurate cash flow forecast model, practical pattern of payments should be taken into account. In this theory, basic model that analyzes practical housing payment collection pattern resulting from prepayments and arrears is described. This model is to complement conventional cash flow forecast scheme in the phase of business feasibility review. Analysis result on final losses in cash that occur as a result of prepayment and arrears is considered in this model. Additionally, in the estimation of construction cost in the phase of business feasibility review, real construction prices instead of official prices are applied to enhance accuracy of cash outflow forecast. The proportion of payment made by a bill and changes in payment date caused by rescheduling of a bill are also factored in to estimate cash outflow. This model would contribute to achieving accurate cash flow forecast that better reflect real situation and to enhancing efficiency in capital management by giving a clear picture with regard to the demand and supply timing of capital.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.4
• /
• pp.228-234
• /
• 2021

Real estate market participants need to have a sense of predicting real estate prices in decision-making. Commonly used methodologies, such as regression analysis, ARIMA, and VAR, have limitations in predicting the value of an asset, which fluctuates due to unknown variables. Therefore, to mitigate the limitations, an artificial neural was is used to predict the price trend of apartments in Seoul, the hottest real estate market in South Korea. For artificial neural network learning, the learning model is designed with 12 variables, which are divided into macro and micro factors. The study was conducted in three ways: (Ed note: What is the difference between case 1 and 2? Is case 1 micro factors?)CASE1 with macro factors, CASE2 with macro factors, and CASE3 with the combination of both factors. As a result, CASE1 and CASE2 show 87.5% predictive accuracy during the two-year experiment, and CASE3 shows 95.8%. This study defines various factors affecting apartment prices in macro and microscopic terms. The study also proposes an artificial network technique in predicting the price trend of apartments and analyzes its effectiveness. Therefore, it is expected that the recently developed learning technique can be applied to the real estate industry, enabling more efficient decision-making by market participants.

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

• Environmental and Resource Economics Review
• /
• v.30 no.3
• /
• pp.395-433
• /
• 2021

This paper aims to examine the effects of homeownership and residential types on the economic values of urban green spaces. Green open spaces as public goods provide positive externalities that are comprised of pecuniary and technological externalities. Seoul, South Korea, is used as a case study using choice experiments, with split-sample online respondents of 1,000. The study results evidenced that the differentiation between the two types of externalities is imperative for equitable provisions and efficient management of various urban open spaces. There is a positively significant and substantial impact of homeownership for apartment dwellers, ceteris paribus, but not for house dwellers. For apartments, the efficiency loss can be reduced by increasing green spaces up to the critical point where the marginal cost is at equilibrium with tenants' marginal values. For non-apartment houses, it is not homeownership but the monthly household income that has a significant impact on the amenity value. In general, public benefits from green spaces are equivalent to 16% to 33% of the current residential prices on average for a view or access. Different residential types do not cause a significant impact on the access values. Residential profiles for green spaces were developed, together with tailor-made policy suggestions.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.2
• /
• pp.59-76
• /
• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.1
• /
• pp.173-183
• /
• 2017

We investigate the factors affecting the price of apartments using the spatial and temporal data of private real estate prices. The factors affecting the price of apartment were analyzed using geographical and temporal weighted regression (GTWR) model which incorporates the temporal and spatial variation. In contrast to the OLS, a general approach used in previous studies, and GWR method which is most widely used for analyzing spatial data, GTWR considers both temporal and spatial characteristics of the house price, and leads to better description of the house price determination. Year of construction and floor area are selected as the significant factors from the analysis, and the house price are affected by them temporally and geographically.