• Land and Housing Review
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• v.14 no.3
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• pp.55-75
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• 2023

Based on prior studies on real estate policy, tax policy, and financial policy, this study examined how tax policy and financial policy affected real estate prices using monthly data from January 2014 to December 2021. We performed a VAR model using unit root tests, cointegration tests, as well as conducted impulse response analysis and variance decomposition analysis. The results are as follows. First, the tax regulation index and the financial regulation index had no discernible impact on housing prices. Specifically, a one-sided stabilizing regulatory policy was ineffective and, instead, led to unintended side effects, such as price increases resulting from reduced transaction volume. Secondly, mortgage rates had a negative impact on the housing sale price index. In other words, an increase in interest rates might led to a decrease in housing prices. Thirdly, an increase in the transfer difference, which involves capital gains tax, has a positive effect on housing prices. This led to rising housing prices because the transfer taxes were shifted to buyers, causing them to hesitate to make purchases due to the increased tax burden. Fourthly, both acquisition taxes and mortgage loans had relatively little impact on housing prices.

• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.23-29
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• 2015

In this study, the spatial relevance between the regional housing price data and the spatial distribution of the location-based social media data is explored. The spatial analysis with rasterization was applied to this study, because the both data have a different form to analyze. The geo-tagged Twitter data had been collected for a month and the regional housing price index about sales and lease were used. The spatial range of both data includes Seoul and the some parts of the metropolitan area. 2,000m grid was constructed to consider the different spatial measure between two data, and they were combined into the constructed grids. The Hotspot Analysis was operated using the combined dataset to see the comparison of spatial distribution, and the bivariate spatial correlation coefficients between two data were measured for the quantitative analysis. The result of this study shows that Seocho-gu area is detected as a common hotspot of tweet and housing sales price index data. though the spatial relevance is not detected between tweet and housing lease price index data.

• 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 Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.566-576
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• 2016

It is known that the Real Estate Sales Market and Auction Market are closely interrelated with each other in a variety of respects and the media often mention the real estate auction market as a leading indicator of the real estate market. The purpose of this paper is to analyze the relationships between the housing market and auction market before and after macroeconomic fluctuations using VECM. The period from January 2002 to December 2008, which was before the financial crisis, was set as Model 1 and the period from January 2009 to November 2015, which was after the financial crisis, was set as Model 2. The results are as follows. First, the housing auction market is less sensitive to changes in the housing market than it is to fluctuations in the auction market. This means that changes in the auction market precede fluctuations in the housing market, which shows that the auction market as a trading market is activated. In this respect, public institutions need to realize the importance of the housing auction market and check trends in the housing contract price in the auction market. Also, investors need to ensure that they have expertise in the auction market.

• 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.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.877-888
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• 2017

Spectral analysis is used to determine the frequency of time series data. We first determine the frequency of the series through the power spectrum or the periodogram and then calculate the period of a cycle that may exist in a time series. Estimating the frequency using a Bayesian technique has been developed and proven to be useful; however, the Bayesian estimator for the frequency cannot be analytically solved through mathematical equations and may be handled numerically or computationally. In this paper, we make an inference on the Bayesian frequency through both resampling a parameter by Markov chain Monte Carlo (MCMC) methods and resampling data by bootstrap methods for a time series. We take the Korean real estate price index as an example for Bayesian frequency estimation. We have found a difference in the periods between the sale price index and the long term rental price index, but the difference is not statistically significant.

• Korea Real Estate Review
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• v.28 no.2
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• pp.21-34
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• 2018

The purpose of this study is to investigate the effect of macro-finance variables on the correlation between the housing and stock markets because understanding the nature of time-varying correlations between different assets has important implications on portfolio allocation and risk management. Thus, we adopted the AG-DCC GARCH model to obtain time-varying, conditional correlations. Our sample ranged from January 2004 to November 2017. Our empirical result showed that the coefficients on asymmetric correlation were significantly positive, implying that correlations between the housing and stock markets were significantly higher when changes in the housing price and stock returns were negative. This finding suggested that the housing market has less hedging potential during a stock market downturn, when such a hedging strategy might be necessary. Based on the regression analysis, we found that the term spread had a significantly negative effect on correlations, while the credit spread had a significantly positive effect. This result could be interpreted by the risk premium effect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.480-488
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• 2016

As the subprime mortgage crisis spread globally, it depressed not only the financial market, but also the construction business in Korea. In fact, according to CERIK, the BSI of the construction businesses plunged from 80 points in December 2006 to 14.6 points in November 2008, and the extent of the depression in the housing sector was particularly serious. In this respect, this paper analyzes the influence of the financial market fluctuation on the housing market before and after the Global Financial Crisis using VECM. The periods from January 2000 to December 2007 and January 2008 to October 2015, before and after the financial crisis, were set as Models 1 and 2, respectively. The results are as follows. First, when the economy is good, the Gangnam housing market is an attractive one for investment. However, when it is depressed, the Gangnam housing market changes in response to the macroeconomic fluctuations. Second, the Gangbuk and Gangnam housing markets showed different responses to fluctuations in the financial market. Third, when the economy is bad, the effect of low interest rates is limited, due to the housing market risk.