• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1471-1479
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• 2017

The variability of trade price index of apartment influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly trading price index of apartment data. About 16 years of the monthly data have been used from September 2001 to May 2017. In the ARE model, six macroeconomic variables are used as the explanatory variables for the rade price index of apartment. The six explanatory variables are mortgage rate, oil import price index, consumer price index, KOSPI stock index, GDP, and GNI. The result has shown that trading price index of apartment explained about 76% by the mortgage rate, and KOSPI stock index.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.89-99
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• 2014

Correlation of KOSPI from stock market and Apartment Prices in Seoul HPPCI from real estate market has been found from this research. Furthermore, from the comparison of those indicators' flows, certain precedence was found as well. The purpose of this research is to analyze correlation and precedence among KOSPI, Apartment price in Seoul, HPPCI and CLI. As for predicting KOSPI of stock market and real estate market, it is necessary to find out preceding indices and analyzing their progresses first. For 27 years from the January 1987 to December 2013, KOSPI has been grown by 687%, while CLI showed 443%, Apartment of Seoul showed 391%, HPPCI showed 263% of growth rate in order. As the result of correlation analysis among Apartment of Seoul, CLI, KOSPI and HPPCI, KOSPI and HPPCI showed high correlation coefficient of 0.877, and Apartment of Seoul and CLI showed that of 0.956 which is even higher. Result from the analysis, CLI shows high correlation with stock and real estate market, it is a good option to watch how CLI flows to predict stock and real estate market.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.2
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• pp.301-306
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• 2013

The Ratio of APT jeonse to purchase price was still rising. The interaction of APT Purchase and Jeonse price indices by region analysis in order to analyze this phenomenon, and results were summarized as follows. First, because the regional APT purchase and jeonse prices appears the rise and fall differently by region, regional polarization was deepening. Second, the recently real estate market was analyzed the province's booming real estate and the downturn of the metropolitan area. So, the ratio of APT jeonse to purchase price was continued to rise. Finally, the Ratio of APT jeonse to purchase price changing rate is (+) increased if the APT purchase price changing rate is larger then the APT purchase price changing rate and smaller then is (-) decreased.

• 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.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.147-170
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• 2022

This article analyzes the spillover effects by dividing the weekly rate of return on apartment prices in 70 si-gun-gu (local area) in the Capital Region into three periods: the entire period (April 2008~August 2021); the period before the price surge (April 2008~October 2018); and the period of price surge (November, 2018~August 2021), based on a consideration of the cycle of fluctuations in apartment sales prices and the timing of the current government's policy interventions. The results obtained from this analysis are summarized as follows. First, the analysis of the spillover effects is similar to or different from the results of existing work depending on the period. The analysis of the spillover effects on the entire period and the period before the price surge shows that the 'Gangnam' effect exists in the apartment market in the Capital Region. On the other hand, the analysis of the spillover effects on the period of price surge reveals different results than before. The spillover effect index calculated through the analysis of the rolling sample decreases during the decline in the cycle of apartment sales prices, while the opposite trend is shown during the upward period. Looking at the timing between the peak of the spillover effect index and policy interventions, it appears that the government's policy interventions took place after the peak of the spillover effect index in 2017, before the peak in 2018 and 2019, and around or after the peak after 2020.

• 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.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.171-181
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• 2022

The real estate price index plays key roles as quantitative data in real estate market analysis. International organizations including OECD publish the real estate price indexes by country, and the Korea Real Estate Board announces metropolitan-level and municipal-level indexes. However, when the index is set on the smaller spatial unit level than metropolitan and municipal-level, problems occur: missing values. As the spatial scope is narrowed down, there are cases where there are few or no transactions depending on the unit period, which lead index calculation difficult or even impossible. This study suggests a supervised learning-based machine learning model to compensate for missing values that may occur due to no transaction in a specific range and period. The models proposed in our research verify the accuracy of predicting the existing values and missing values.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.585-600
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• 2012

The purpose of this study related to the liquidity impact of the housing market variables using vector auto-regressive model(VAR) and empirical analysis is to derive some policy implications. October 2003 until May 2012 using monthly data for liquidity variables mortgage rates, mortgage, financial liquidity, as the composite index and nation, Seoul, Gangnam, Gangbuk, the Apartment sales prices were analyzed. Granger Causality Test Results, mortgage rates and mortgage at a bargain price two regions had a strong causal relationship. Since the impulse response analysis, Geothermal difference there, but housing price housing price itself, the most significant ongoing positive (+) reactions were liquidity-related variables are mortgage loans is large and persistent positive (+), financial liquidity weakly positive (+), mortgage interest rates are negative (-), KOSPI, the negative (-) reacted. Liquidity and housing prices that the rise can be and Gangnam in Gangbuk is greater than the factor that housing investment was confirmed empirically. Government to consider the current economic situation, while maintaining low interest rates and liquidity of the market rather than the real estate industry must ensure that activities can be embedded and local enforcement policies should be differentiated according to the policy will be able to reap significant effect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6315-6324
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• 2015

This study analyses the effects of household finances on rental price of apartment in Seoul which play a major role in real estate policy. We estimate VAR models using time series data. Economy variables such as sales price of apartment in Seoul, consumer price index, hiring rate, real GNI and loan amount of housing mortgage, which relate to household finances and influence the rental price of apartment, are used for estimation. The main findings are as follows. In the short term, the rental price of apartment is impacted by economy variables. Specifically, Relative contributions of variation in rental price of apartment through structural shock of economy variables are most influenced by their own. However, in the long term, household variables are more influential to the rental price of apartment. These results are expected to contribute to establish housing price stabilization policies through understanding the relationship between economy variables and rental price of apartment.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.125-139
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• 2021

Continuous subway line expansion over the years in Seoul metropolitan area has contributed to improved accessibility to public transport. Since public transport accessibility has a significant impact on housing decisions, quantitative analysis of correlation between housing prices and public transport accessibility is regarded as one of the most important factors for planning better housing policies. This study defines the reduction of traveling time resulted from the construction of new metro stations despite them not being the closest stations as 'Network Expansion Effect', and seeks to understand how the Network Expansion Effect impacts on housing prices. The study analyzes monthly rent data converted from upfront lump sum deposit, so called Jeonse in Korea, from 2012 to 2018, through 'A Modified Repeat Sales Model.' As a result, the effect of 'Network Expansion' on rental prices in Seoul has stronger during the period of 2017 to 2018 than the base period of 2012 to 2014, which suggests the 'Network Expansion' has a meaningful effect on rent. In addition, in comparison between the most and the least affected group of apartments by 'Network Expansion Effect', the most affected group has more price increase than the least affected group. These findings also indicate that different levels of 'Network Expansion Effect' have various influences on the value of residential real estate properties.