• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.10
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• pp.1553-1560
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• 2013

This study focused on the residential real estate market, real estate for sale in the course of the bargaining power of buyers in terms of factors to be extracted was determined. In addition to rental housing, including the number of buyers have several alternatives, the more related information, the buyers have advantage over the sellers in the course of the housing sale process. And if the external conditions and the situations between the parties have changed, how much flexibility to respond to changing and the less costs of to changing, the buyers have advantage over the sellers in the course of the housing sale process.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• Journal of Cadastre & Land InformatiX
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• v.44 no.1
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• pp.195-212
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• 2014

This study was performed in order to contributing the influence factors in the real estate marketing strategy which largely affects the success of a real estate sale and development projects. Articles have to be concluded with the Influence factors of Satisfaction based on the type of Real Estate through PLS Regression Analysis. Building data was conducted by the target population specializing in the real estate sale and development business. Classification of real estate business classified by the way of the residential and non-residential parted. In the case of Housing type in the analysis result, the main factors were made by calculating the each factor such as brand trust, Strengthening the alliance and partnership, Traffic environment, Relaxing the requirements for a Real estate sales, Business presentation, Unsold benefits and Relaxing the requirements for payment. Otherwise, In the case of non-residential in the analysis result, the factors such as Relaxing the requirements for payment, Customer Orientation, living environment, Unsold benefits, Strengthening the alliance and partnership and communication terms made an influence to satisfaction.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.490-497
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• 2007

Since the foreign currency crisis, Korean economy has suffered recession and the government launches residence and real estate policy in order to increase the demand and trade of real estate and to help the economy revitalization. 1 As a result, the rate of economy growth is shown the high increase with the figure of 10.9% in 1999 and 8.8% in 2000. However, it brings overheating market as a negative effect. Although, the government established the policy for the control of speculation, the policy causes instability of economy. This study is to analyze the effect between the residence policy and the housing cost since the foreign currency crisis through housing sale price estimation and housing lease price estimation and is to apply the basis data of the next residence policy.

• The Journal of the Korea institute of electronic communication sciences
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• v.3 no.2
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• pp.129-142
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• 2008

The present paper questionable matter and necessity of real estate information ethics, triangular position method etc. is an escape and for is. And currently the real estate information ethical violate action where becomes the problem the plan which raises an ethical level is an escape and for is. Preceding research led first of all and justice a real estate information ethics and necessity, scope, function and basic principle escape. Also at current law escape sent 'accidental incorrect reports', 'preservation of the list must erase', 'light plagiarism' and 'information is inappropriate managed' in the object of the real estate information ethics which excepts the illegal act. With the real estate relation specialty led interview together and currently actual condition real estate current price information where becomes the problem and article for sale information type nonly-ethical and also the cause analyzed together. This led and also the real estate information ethical raising plan did escape together.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.371-381
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• 2017

There has never been research on Chaos analysis using real estate auction sale price rate in Korea. In this study, three Chaos analysis methodologies - Hurst exponent, correlation dimension, and maximum Lyapunov exponent - in order to capture the nonlinear deterministic dynamic system characteristics. High level of Hurst exponent and the extremely low maximum Lyapunov exponent provide the tendency and the persistence of the data. The empirical results give two meaningful facts. First, monthly time lags of the correlation dimension are coincident with the time period from the approval auction start day to the sale price fixing day. Second, its weekly time lags correspond to the time period from the last day of request for sale price allocation to the sale price fixing day. Then, this study potentially examines the predictability of the real estate auction price rate time series.

• The Journal of Industrial Distribution & Business
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• v.12 no.2
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• pp.41-52
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• 2021

Purpose: This study aims to provide implications for the government's housing supply policy by analyzing the factors that determine the type of real estate holding and household debt. This study started from the awareness that the determinants of household debt differ depending on the type of real estate holding. Research design, data and methodology: Real estate ownership type was classified and analyzed into 4 models: model 1 (1 household 1 house and self-resident), model 2 (1 household multiple real estate ownership and self-resident), model 3 (1 household 1 house and rent residence), model 4 (1 household holds a large number of real estate and rent residence). The analysis method used multiple regression analysis. The dependent variable was household total debt. As independent variables, household debt, annual gross household income, financial assets, real estate net assets, annual repayment, demographic & residential characteristics were used. Results: 1) Model 4 has the highest household debt and the highest gross income, Model 2 has the most real estate mortgage loans and real estate net asset, and Model 1 has the highest real estate mortgage payments. 2) The positive factor of common household debt determinants is real estate net assets, and the negative factor is financial assets. 3) It was the net assets of real estate that acted as a positive factor in common for the four models. In other words, the more financial assets, the less household debt. It was analyzed that the more net assets of real estate, the more household debt. The annual repayment of financial liabilities had no influence on household debt, while the annual repayment of loan liabilities and household debt had a positive relationship. Conclusions: 1) It is necessary to introduce benefits and systems that can increase the proportion of household financial asset. Specific alternatives include tax benefits and reduced fees for financial asset investment. 2) In the case where a homeless person prepares one house for one household, it is necessary to prepare various support measures according to the income level. The specific alternative is to give additional points for pre-sale or apply an interest rate cut incentive for mortgage loans.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.121-129
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• 2016

The purpose of this study is to set a standard for sale prices of supplying apartment houses about decision factors in sale price of supplying apartment houses using Analytic Hierarchy Process. This is done by modeling decision factors in sale price of supplying apartment houses as hierarchy. According to the modeled hierarchy, the relative importance of supplying factors are determined using a survey of a group of real estate experts. In addition, through Analytic Hierarchy Process, the relative importance of phased sale prices of supplying apartment is analyzed in order to set a standard to estimate competitive sale prices of newly supplying apartment houses.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.286-298
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• 2018

This study presents determinants of resettlement considering population, economy, residential environment, policy characteristic, proposes implications for increase of resettlement. The research method deducted determinants of resettlement by union groups using logistic regression, union members are divided to more 10 years group and under 10 years group focused on real estate ownership duration. The analysis results are summarized as follows. More 10 years group has higher age, neighborship, satisfaction about pre-sale price, inside region in redevelopment promotion project, satisfaction about increase of real estate price, the higher resettlement decision probability. Under 10 years group has higher satisfaction about increase of real estate price, satisfaction about pre-sale price, satisfaction about floor area ratio incentive, the higher resettlement decision probability. the political implications must be customized financial support considering economic situations and increase of asset value by real estate ownership duration.

• Journal of Distribution Science
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• v.12 no.9
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• pp.47-52
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• 2014

Purpose - There has been little research on the variables influencing studio apartment values. This study aims to identify variables affecting the value of studio apartments in Seoul by empirically examining the interaction between sale prices and characteristics studio apartment characteristics. Research design, data, and methodology - We have analyzed data pertaining to 142 studio apartments in September 2010. A regression analysis model is constructed to test the significance of the variables in relation to the studio apartment sale prices per m2 in Seoul. Results - The age of the building is comparatively more significant than land use as the explanatory variable. Land price is the key variable affecting studio apartment sale prices and investors are willing to pay high implicit sale prices for locations that are associated with high land prices. Conclusions - The age of buildings explains a significant portion of the variability of the sale prices of studio apartment. Higher land prices result in higher sale prices for studio apartments. The older the buildings, the lower the sale prices of the studio apartments.