• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.151-152
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• 2021

The sale price of apartment buildings is a key factor in the success or failure of apartment projects, and the factors that affect the sale price of apartments vary widely, including location, environmental factors, and economic conditions. Existing methods of predicting the sale price do not reflect the nonlinear characteristics of apartment prices, which are determined by the complex impact factors of reality, because statistical analysis is conducted under the assumption of a linear model. To improve these problems, a new analysis technique is needed to predict apartment sales prices by complex nonlinear influencing factors. Using machine learning techniques that have recently attracted attention in the field of engineering, it is possible to predict the sale price reflecting the complexity of various factors. Therefore, this study aims to conduct a basic study for the development of a machine learning-based prediction model for apartment sale prices.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.211-231
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• 2022

This study attempted to identify the effects of macroeconomic variables such as the All Industry Production Index, Consumer Price Index, CD Interest Rate, and KOSPI on apartment lease prices divided into nationwide, Seoul, metropolitan, and region, and to present a methodological prediction model of apartment lease prices by region using Long Short Term Memory (LSTM). According to VAR analysis results, the nationwide apartment lease price index and consumer price index in Lag1 and 2 had a significant effect on the nationwide apartment lease price, and likewise, the Seoul apartment lease price index, the consumer price index, and the CD interest rate in Lag1 and 2 affect the apartment lease price in Seoul. In addition, it was confirmed that the wide-area apartment jeonse price index and the consumer price index had a significant effect on Lag1, and the local apartment jeonse price index and the consumer price index had a significant effect on Lag1. As a result of the establishment of the LSTM prediction model, the predictive power was the highest with RMSE 0.008, MAE 0.006, and R-Suared values of 0.999 for the local apartment lease price prediction model. In the future, it is expected that more meaningful results can be obtained by applying an advanced model based on deep learning, including major policy variables

• Smart Media Journal
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• v.11 no.3
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• pp.62-73
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• 2022

Researches on the prediction of domestic apartment sales price have been continuously conducted, but it is not easy to accurately predict apartment prices because various characteristics are compounded. Prior to predicting apartment sales price, the analysis of major factors, influencing on sale prices, is of paramount importance to improve the accuracy of sales price. Therefore, this study aims to analyze what are the factors that affect the apartment sales price in Gwangju, which is currently showing a steady increase rate. With 6 years of Gwangju apartment transaction price and various social factor data, several maching learning techniques such as multiple regression analysis, random forest, and deep artificial neural network algorithms are applied to identify major factors in each model. The performances of each model are compared with RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and R² (coefficient of determination). The experiment shows that several factors such as 'contract year', 'applicable area', 'certificate of deposit', 'mortgage rate', 'leading index', 'producer price index', 'coincident composite index' are analyzed as main factors, affecting the sales price.

• 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.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.603-614
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• 2020

This study focuses on the prediction and factors of apartment prices in Seoul using a convolutional neural networks (CNN) model that has shown excellent performance as a predictive model of image data. To do this, we consider natural environmental factors, infrastructure factors, and social economic factors of the apartments as input variables of the CNN model. The natural environmental factors include rivers, green areas, and altitudes of apartments. The infrastructure factors have bus stops, subway stations, commercial districts, schools, and the social economic factors are the number of jobs and criminal rates, etc. We predict apartment prices and interpret the factors for the prices by converting the values of these input variables to play the same role as pixel values of image channels for the input layer in the CNN model. In addition, the CNN model used in this study takes into account the spatial characteristics of each apartment by describing the natural environmental and infrastructure factors variables as binary images centered on each apartment in each input layer.

• The Journal of Bigdata
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• v.5 no.1
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• pp.109-124
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• 2020

In Korea, despite much interest in real estate, it is not easy to predict prices. Because apartments are both residential spaces and investment materials. Key figures affecting the price of apartments vary widely, and there are also regional characteristics. This study was conducted to derive the factors and characteristics that affect the sale price of apartments in S City, Gyeonggi-do. In general, people diagnose that better subway accessibility leads to higher apartment sales price. Nevertheless, in the case of S City, the price was slightly lower as it was closer to Line 1, but the higher the subway accessibility at Shinbundang Line, the higher the price. The five-year average of government bonds and the price were inversely related, and it was found to be proportional to the M2 balance and the price. The floor area ratio and the total number of parking lots had a great influence on the price, and the presence of department stores and discount marts within 1.5 km were the most important factors in the area of cultural aspect.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.3
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• pp.45-52
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• 2018

Currently, apartments have become an important research subject for the overall area of politics, economics, and culture as well as urban architectural study. However, there are few analyses of the research trends related to the current interest in the apartment research and prediction of the future changes of an apartment in politics and industry. In this study, the research information related to the apartment has classified, and the changes in the research trends have analyzed. Based on the classified data, the first thesis and dissertation related to the apartment and changes of academic notation have discovered. In addition, future interests and future research directions through Frequency of Appearance, Degree Centrality Analysis, and Betweenness Centrality Analysis of author keywords were predicted. As a result of the analysis, 'Space,' 'Residential Mobility' and 'Apartment Complex' studies were found to be important research topics throughout the entire period. 'Han Gang Apartment,' 'Small Size Apartment,' 'Civic Apartments,' 'Jamsil,' and 'Child' were newly interested topics until 70's era. '(Super) High-rise Apartment,' 'Perception,' 'Jugong Apartment,' 'Housing Environment,' 'Housewife,' 'Apartment Layout,' and 'Busan' were newly interested topics during the 80's and 90's era. 'Apartment Price,' 'Energy,' 'Remodeling,' 'Noise,' 'Resident Satisfaction,' 'Community,' and 'Apartment Lotting-out' were newly interested topics after the year 2000. New concerns for last decade are found to be 'Super High-rise Apartment', 'Remodeling', 'Indoor'(2007), 'Apartment Reconstruction Project', 'Brand', 'AHP', 'Housing Environment'(2008), 'Ventilation'(2009), 'Apartment Lotting-out'(2010), 'Economic Assessment'(2011), 'Cost'(2012), 'Green Building', 'Apartment Sales', 'Law', 'Society'(2013), 'Floor Impact Noise', 'Seoul'(2014), 'Noise'(2015), 'Hedonic Model'(2016). In addition, following research topics are expected to be active in the future: In maturity stage of the research development is going to be 'Apartment Price', 'Space', 'Management of Apartment Housing'; the hedonic model, which is research growth and development stage, is going to be '(Floor Impact) Noise', 'Community', 'Energy.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.956-959
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• 2020

본 연구에서는 아파트를 대표할 수 있는 내·외적 데이터를 수집하고 인공지능 기술들을 활용하여 아파트 가격을 예측하는 시스템을 구축하고자 한다. 구체적으로 웹크롤링 기법을 통해 수집한 아파트 내·외적 데이터의 변수들에 대한 특성 선택(Feature Selection)을 수행하였고, 다양한 인공지능 기법을 활용하여 부동산 가격 예측 모형을 개발하였다. 아파트 가격 예측 모형 생성을 위해 Linear Regression, Ridge, Xgboost, Lightgbm, Catboost 등의 기계학습 알고리즘을 사용하였고, RMSE를 사용하여 각 예측 모형 간의 성능 비교를 수행하였다. 가장 성능이 좋은 예측 모형은 Xgboost기반 예측 모형이였으며, RMSE값이 약 0.0366으로 가장 낮았으며 테스트 데이터에 대한 정확도는 약 95.1%였다.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.23-34
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• 2011

Property auctions have become a new method for real estate investment because the property auction market grows in tandem with the growth of the real estate market. This study focused on the statistical model for predicting bid price rates which is the main index for participants in the real estate auction market. For estimating the monthly bid price rate, we proposed a new method to make up for the mean of regions and terms as well as to reduce the prediction error using a decision tree analysis. We also proposed a linear regression model to predict a bid price rate for individual auction property. We applied the proposed model to apartment auction property and tried to predict the bid price rate as well as categorize individual auction property into an auction grade.

• 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.