• Korea Real Estate Review
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• v.27 no.1
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• pp.17-32
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• 2017

The openness of residential space directly affecting lighting, view, and ventilation leads to the variation of open floor plan type in apartment construction project. This study intends to substantiate the effect to the apartment price by design property of open floor plan based on actual design information of apartment and price. The open floor plan type and associated design property, and actual transaction price of apartment have been considered as variables for analysis by the hedonic price function model and artificial neural networks model. Research findings indicate that the openness affects the price of apartment positively and the three sides open plan is the most preferred with the highest price. This study aims to provide the implication to the developer in planning and design stage of apartment and the purchaser seeking the suitable price by floor plan design.

• Land and Housing Review
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• v.15 no.2
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• pp.89-106
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• 2024

Apartments constitute 64% of the housing type composition, representing the highest proportion among housing types. This proportion has been increasing annually. Given this trend, apartment prices are likely to have a significant impact on the national economy and people's livelihoods. This study examines the impact of the recent development of Seodaegu Station on the surrounding apartment market, with a specific focus on the effects of the educational environment. To this end, we conduct empirical analysis employing a hedonic price model and spatial autocorrelation analysis, based on actual transaction price data from the Ministry of Land, Infrastructure, and Transport. The study revealed three key findings: first, the development of Seodaegu Station positively impacted apartment prices. Second, this positive effect increases with the proximity to Seodaegu Station. Third, the enhancement of the educational environment nearby the Seodaegu Station development also positively influenced apartment prices. This study aims to serve as baseline research output for the public management of future metropolitan transportation facility development projects and for predicting apartment price trends.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.44-48
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• 2015

Housing price is a key indicator of housing demand. Actual Transaction Price Index of Apartment (ATPIA) released by Korea Appraisal Board is useful to understand the current level of housing price, but it does not forecast future prices. Big data such as the frequency of internet search queries is more accessible and faster than ever. Forecasting future housing demand through big data will be very helpful in housing market. The objective of this study is to develop a forecasting model of ATPIA as a part of forecasting housing demand. For forecasting, a concept of time shift was applied in the model. As a result, the forecasting model with the time shift of 5 months shows the highest coefficient of determination, thus selected as the optimal model. The mean error rate is 2.95% which is a quite promising result.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.245-256
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• 2024

Since 2020, quantitative easing and easy money policies have been implemented for the purpose of economic stimulus. As a result, real estate prices have skyrocketed. In this study, the relationship between sales and rental prices by housing type during the period of soaring real estate prices in Busan was analyzed spatio-temporally. Based on the actual transaction price data, housing type, transaction type, and monthly data of district units were constructed. Among the spatio-temporal analysis models, the SpVAR, which is used to understand the temporal and spatial effects of variables, and the GSTAR, which is used to understand the effects of each region on those variables, were used. As a result, the sales price of apartment had positive effect on the sale price of apartment, row house, and detached house in the surrounding area, including the target area. On the other hand, it was confirmed that demand was converted to apartment rental due to an increase in apartment sales prices, and the sale price fell again over time. The spatio-temporal spillover effect of apartments was positive, but the positive effect of row house and detached house were concentrated in the original downtown area.

• 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.

• Land and Housing Review
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• v.11 no.2
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• pp.33-46
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• 2020

As the fastest growing office transaction volume in Korea, there's been a need for development of indicators to accurately diagnose the office capital market. The purpose of this paper is experimentally calculate to the office price index for effective benchmark indices in Seoul. The quantitative methodology used a Case-Shiller Repeat Sales Model (1991), based on actual multiple office transaction dataset with over minimum 1,653 ㎡ from Q3 1999 to 4Q 2019 in the case of 1,536 buildings within Seoul Metropolitan. In addition, the collected historical data and spatial statistical analysis tools were treated with the SAS 9.4 and ArcGIS 10.7 programs. The main empirical results of research are briefly summarized as follows; First, Seoul office price index was estimated to be 344.3 point (2001.1Q=100.0P) at the end of 2019, and has more than tripled over the past two decades. it means that the sales price of office per 3.3 ㎡ has consistently risen more than 12% every year since 2000, which is far above the indices for apartment housing index, announced by the MOLIT (2009). Second, between quarterly and annual office price index for the two-step estimation of the MIT Real Estate Research Center (MIT/CRE), T, L, AL variables have statistically significant coefficient (Beta) all of the mode l (p<0.01). Third, it was possible to produce a more stable office price index against the basic index by using the Moore-Penrose's pseoudo inverse technique at low transaction frequency. Fourth, as an lagging indicators, the office price index is closely related to key macroeconomic indicators, such as GDP(+), KOSPI(+), interest rates (5-year KTB, -). This facts indicate that long-term office investment tends to outperform other financial assets owing to high return and low risk pattern. In conclusion, these findings are practically meaningful to presenting an new office price index that increases accuracy and then attempting to preliminary applications for the case of Seoul. Moreover, it can provide sincerely useful benchmark about investing an office and predicting changes of the sales price among market participants (e.g. policy maker, investor, landlord, tenant, user) in the future.

• Journal of the Korea Institute of Building Construction
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• v.22 no.1
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• pp.81-90
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• 2022

With apartment purchase prices rising, small and medium-sized cities have been highlighted as areas in which real estate speculation is overheated, and thus designated as target districts for adjustment. In addition, tax policy is constantly being adjusted in an attempt to stabilize real estate prices. The purpose of this study is to analyze the basic effect of tax policy on the purchase price of apartments in small and medium-sized cities. This study selected apartments in the Daejeon area that were constructed between 1990 and 2015. In addition, tax policy was divided into regulatory policy and easing policy based on tax increase and tax cut. This study analyzes the short-term difference of one year before and after the change in the purchase price of apartment houses. In addition, this study set the time when real estate policy was implemented and the actual transaction price of apartments in Daejeon as the analysis targets, and analyzed the correlation between tax policy and apartment sales prices through the NPV technique and T-test results. Through the study, it was found that most tax policies changed apartment purchase prices in the short term.

• Korea Real Estate Review
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• v.28 no.1
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• pp.91-104
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• 2018

In this study, the occurrence and degree of herding behavior as a market participant behavior in a housing market were analyzed. For the analysis method, the actual sales price was applied in the CSAD (Cross-sectional Absolute Deviation) model, which has been used the most of late for herding behavior analysis. For the analysis contents, these were subdivided into region, elapsed year, size, and market condition to analyze the regionality and the internal and external factors. For the study results, first, there was no herding behavior in the entire region of Seoul. By region, herding behavior occurred in the downtown, southeast, and northwest regions, which coincided with the results of the precedent study (Ngene et al., 2017). Second, in the market analysis by elapsed year, herding behavior was captured in dilapidated dwellings. By size, herding behavior was observed in small-scale ($60m^2$ or less) apartments and in $85m^2$ or higher and less than $102m^2$ national housing units. Third, during the time of the global financial crisis, herding behavior was not observed in all the regions, whereas when the market situations were in a boom cycle, it was observed in the northwest region. These results suggest that there is a difference from the stock market, where in a period of recession, herding behavior occurs intensively with the expanding fear of incurring losses. This study is significant in that it analyzed the market participant behaviors in the behavioral economic aspects to better understand the abnormal phenomenon in a housing market, and in that it additionally provides a psychological factor - market participant behavior - in market analysis.