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

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2D
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• pp.287-293
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• 2009

The aim of this paper is to analyze resettlement cost and decision factors of resettlement since Redevelopment Promotion Projects. Range of resettlement cost was averagely increased 204% by using actual data. Consequently, the research is operated for aboriginal people in these areas by a questionnaire. The questionnaire ask a payment range of the resettlement cost with 4 stages; 150% and less, 180% and less, 200% and less, excess of 200%. Research scope is consist of Seo-kumsa, Civil Park, Chung-mu and Young-do. These areas are redevelopment of Busan metropolitan city. Resettlement is come under the influence of the resettlement cost and many factors by each specific character. In many alternatives for resettlement, understanding the reason why aboriginal peoples select a certain alternative and if we actualize the proper alternative, aboriginal peoples' resettlement ratio will be increased. Moreover it ask housing characteristic, housing life pattern for understanding aboriginal peoples' characteristic. Also data analysis model is ordinal logistic model'. In analysis result, resettlement cost is 150% of aboriginal assets. and significance parameter is sex, job, income, region, affection, attachment, housing possession type, size and others have influence on aboriginal peoples' resettlement.

• Korean Journal of Construction Engineering and Management
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• v.6 no.6 s.28
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• pp.98-106
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• 2005

The construction cost index and the forecasting model of apartment house can be efficient for evaluating the validness of the fluctuating price, and for making guidelines for construction firms when calculating their profit. In this study the previous construction cost index of apartment house was improved, and the forecasting model based on X-12 ARIMA was developed. According to the result, during the last five years the construction cost, excluding labor expense, has risen approximately to 22.7%. And during next three years, additional 16.8% rise of construction cost is expected. Those quantitative results can be utilized for evaluating the apartment house's selling price in an indirection, and be helpful to understand the variation pattern of the price.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7672-7676
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• 2015

This study examines the dynamic relations between housing price and trading volume in a set of apartment markets in Republic of Korea to explore the informational role of trading volume in predicting the price volatility. Using monthly index data, EGARCH model is utilized to test for volume effect. To estimate the EGARCH-based volatility, two different sets of region are applied for the monthly return. Strong evidence has been found towards housing turnover leading price volatility, this supports previous studies on financial sector(s). These findings also support that trading volume in the housing market contains information on investor sentiment which, in turn, has a valuation effect on the price.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.94-101
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• 2020

This study examined the effect of the sale price by excluding the PHC-pile cost from the construction costs for basic type (CCsBT) as an additional cost. The Ministry of Land, Infrastructure, and Transport excluded the PHC-pile cost in the CCsBT and new method so that only the designed pile quantity was recognized as an additional cost. The effect on the sale price was analyzed by comparing the pile cost of the existing and new methods. For this purpose, seven cases were selected, and the PHC-pile cost was estimated. The existing method was estimated as the ratio of the pile cost to CCsBT. The new method was estimated based on the bill of quantity. As a result, the CCsBT decreased by approximately 2-3% when the PHC-pile cost was calculated in the new method. Furthermore, as a result of comparing the sale price, excluding the PHC-pile cost with the sale price, the CCsBT decreased by approximately 1%. These results are expected to help improve the understanding of the CCsBT. Also, this paper contributes to promoting national housing stability through institutional improvement.

• Journal of the Korean Home Economics Association
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• v.28 no.1
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• pp.57-66
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• 1990

The purpose of this study was to investigate the analytical model of the implicit price according to objective and subjective characteristics of housing. The hedonic price regression was used for estimating the implicit price. The subjectives of this study were 1,143 dwellers who live in Seoul metropolitan area. Taejeon, and Jeonju. Satistical analyses were conducted using frequencies, percentiles, mean, and multiple regression. The major findings were as follows: 1. There was a significant difference in the implict price of the apartment between owners and renters. 2. There was a sginificant difference in the implicit price of the apartment among Seoul metropolitan area, Taejeon, and Jeonju. 3. Using a stepwise multiple regression method, the order of variables as they were entered in the model were different between tenure types (owner/renter), and regions(Seoul metroplitan area/Taejeon/Jeonju). 4. The linear model was the most appropriate noe which explained the housing price. 5. Subjective characteristics of housing in Taejeon and Jeonju had an effect on the housing price more than those in Seoul metropolitan area.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.181-200
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• 2020

This study used the OLS model to estimate the determinants affecting the tenure of a home and then compared the predictive power of each model with SVM, Decision Tree, Random Forest, Gradient Boosting, XGBooest and LightGBM. There is a difference from the preceding study in that the Stacking model, one of the ensemble models, can be used as a base model to establish a more predictable model to identify the volume of housing transactions in the housing market. OLS analysis showed that sales profits, housing prices, the number of household members, and the type of residential housing (detached housing, apartments) affected the period of housing ownership, and compared the predictability of the machine learning model with RMSE, the results showed that the machine learning model had higher predictability. Afterwards, the predictive power was compared by applying each machine learning after rebuilding the data with the influencing variables, and the analysis showed the best predictive power of Random Forest. In addition, the most predictable Random Forest, Decision Tree, Gradient Boosting, and XGBooost models were applied as individual models, and the Stacking model was constructed using Linear, Ridge, and Lasso models as meta models. As a result of the analysis, the RMSE value in the Ridge model was the lowest at 0.5181, thus building the highest predictive model.

• Land and Housing Review
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• v.3 no.1
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• pp.59-68
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• 2012

The current land development cost price system is classified as the creating land by construction price and composition changes that occur sporadically in the process of completion at the source of the factors by incurred cost price. Housing for land cost price system is a lack of objectivity which scheme of the such a gap due to the land in accordance construction and incurred cost price system so far. Therefore, in order to increase the objectivity of costing the costing of predictable surprises should be reflected in the process. Under such a background, this study defined the effective differential ratio as the predictable, estimated them for various characteristics of each business district to reflect. For this, set the properties category of five types to attributes and making the complex category and Look-up table. Which result of model validation is showed a high reliability. Therefore, Continuous accumulation of material in the future, when them to reflect the construction cost, will contribute to the bridge the gap the construction cost between incurred them.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• Journal of the Korea Institute of Building Construction
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• v.10 no.2
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• pp.75-80
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• 2010

With the intensifying of price competition and structural diversifications, the uncertainty of the domestic housing market has been increased. This highlights the importance of the planning stage of construction projects, and the increased need for a higher level of accuracy in approximate estimates. Currently, a number of research and development programs to calculate construction cost at the initial planning stage are being conducted. However, there are few cases in which local characteristics are considered in deriving the results. If local calibration can be conducted during estimates, more accurate cost estimates will be enabled. This could also play a major role in ensuring the success of a project. Therefore, the purpose of this research is to develop a calculation methodology and a model for a local index based on the historical data of public apartment buildings, and to derive a local index that supports accurate construction cost estimates.