• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.561-568
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• 2015

In this study we predict apartment prices per unit in Daegu-Gyeongbuk areas by spatial lag and spatial error models, both of which belong to so-called spatial regression model. A spatial weight matrix is constructed by k-nearest neighbours method and then the models for the apartment prices in March, 2012 are fitted using the weight matrix. The apartment prices in March, 2013 are predicted by the fitted spatial regression models and then performances of two spatial regression models are compared by RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error).

• Journal of Digital Convergence
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• v.11 no.11
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• pp.59-70
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• 2013

As people invest most of their asset in real estate, there is high interest in changing in housing and real estate prices in the future for a digital economy. Various variables are affecting the housing and real estate market. Among them, four variables : households, productive population, interest rate and index price are chosen and analyzed representatively. This study is aimed to build decision model of apartment prices in Seoul empirically. From the analysis result the stock index is the only variable which is significant statistically to apartments in Seoul. From this study, the households and productive population show the same direction as shown in the previous studies before but not significant statistically. Among the independent variables, the stock index is chosen as a major variable of determinant of Seoul apartment price. From the result of the research, prediction of stock market should be preceded to forecast the movement of housing and real estate market in the future.

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

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.162-170
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• 2006

Neural network analysis is expected to enhance the forecasting ability for the real estate market. This paper reviews definition, structure, strengths and weaknesses of neural network analysis, and verifies the applicability of neural network analysis for the real estate market. Neural network analysis is compared with regression analysis using the same sample data. The analyses model the macroeconomic parameters that influence the sales price of apartments. The results show that neural network analysis provides better forecasting accuracy than regression analysis does, what confirms the applicability of neural network analysis for the real estate market.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.3-11
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• 2015

Apartment developers consider the initial sales rate as an important indicator for their success of apartment development projects. They tried to achieve a secure level of initial sales rate. In spite of its importance, there is little research on the initial sales rate because of the difficulties in gathering proper data for analysis. This study, however, collects the data in initial sales rates in Su-won from various sources such as construction companies, marketing companies, sales companies and so on. By using this rare data, this study analyses the initial contract rate of apartment and estimates the initial contract rate by sales price. The result of this study shows that important of land area ratio, brand, and distance to park. It is expected that the proposed model will be used for apartment developers in sales planning phase.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.917-934
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• 2014

Geographically weighted regression(GWR) model has been widely used to estimate spatially heterogeneous real estate prices. The GWR model, however, has some limitations of the selection of different price determinants over space and the restricted number of observations for local estimation. Alternatively, the geographically weighted LASSO(GWL) model has been recently introduced and received a growing interest. In this paper, we attempt to explore various local price determinants for the real estate by utilizing the GWL and its applicability to forecasting the real estate price. To do this, we developed the three hedonic models of OLS, GWR, and GWL focusing on the sales price of apartments in Seoul and compared those models in terms of model fit, prediction, and multicollinearity. As a result, local models appeared to be better than the global OLS on the whole, and in particular, the GWL appeared to be more explanatory and predictable than other models. Moreover, the GWL enabled to provide spatially different sets of price determinants which no multicollinearity exists. The GWL helps select the significant sets of independent variables from a high dimensional dataset, and hence will be a useful technique for large and complex spatial big data.

• KIEAE Journal
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• v.17 no.3
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• pp.75-81
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• 2017

Purpose: The energy consumption of apartment units is affected by the lifestyle of the residents rather than system technology. In this study the numerical analysis of assumed energy consumption correlation factors with arbitrary value due to uncertainty. It is intended to be used as a simulation correction value which can be utilized as a predicted value of actual energy usage. The correction value of the simulation is set in the developed form of the existing process that derives the actual usage amount. The simulation results used in the existing evaluation system are used to maintain the useful value as the current system evaluation scale and predict the actual capacity. Method: The method of the study is to statistically analyze the data frames of all complexes capable of collecting the annual energy usage and to reconstruct the population by adding the variables that are expected to be correlated. Repeat the data frame configuration with variables that are assumed to be highly correlated with energy use levels. Determine whether there is correlation or not. The intensity of the external characteristics of the building equipment related to the energy consumption is presented as the quantitative value. Result: The correlation between electricity consumption and trading price since 2010 is analyzed as (Correlation coefficient 0.82). These results are higher than (Correlation coefficient 0.79), which is the correlation between residential area and trading price. This paper signifies the starting point of the methodology that broadens the field of view of verification of simulation feasibility limited to the prediction technique focused on the simulation tool and the element technology scope.The diversified phenomenon reproduction method develops the existing energy simulation method.It can be completed with a simulation methodology that can infer actual energy consumption.

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