• Journal of The Korean Digital Architecture Interior Association
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• v.10 no.2
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• pp.13-20
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• 2010

Uncertainty in housing price fluctuation has great impact on the overall economy due to importance of housing market as both place of residence and investment target. Therefore, estimating housing market condition is a highly important task in terms of setting national policy. Primary indicator of the housing market is a ratio between rent and transaction price of housing. The research explores dynamic relationships between Rent-Transaction price ratio, housing transaction price and jeonse rental price, using Vector Autoregressive Model, in order to demonstrate significance of shifting rent-transaction price that is subject to changes in housing transaction and housing rental market. The research applied housing transaction price index and housing rental price index as an indicator to measure transaction and rental price of housing. The price index and data for price ratio was derived from statistical data of the Kookmin Bank. The time-series data contains monthly data ranging between January 1999 and November 2009; the data was log transformed to convert to level variable. The analysis result suggests that the rising ratio between rent-transaction price of housing should be interpreted as a precursor for rise of housing transaction price, rather than judging as a mere indicator of a current trend.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.1
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• pp.89-98
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• 2012

While the housing transaction price of Seoul tends to be stagnant or declining in line with the housing market recession since 2007, the jeonse price keeps continual increase. Such flow of jeonse price change has a serious influence on ordinary person's housing stability seriously. Therefore, it is very meaningful in terms of social policy to analyze the trend of recent jeonse price change. This study aims to have an empirical analysis of structural characteristics of the trend of recent jeonse price change. After the review of various previous studies, this study selected housing jeonse price index, non-sold house quantity, jeonse vs. transaction price rate, and housing construction performance as analytical variables, and employed monthly time series resources from January 2007 to April 2011. As a result, when the housing supply reduced, the potential quantity for jeonse market reduced that occurred unbalance of supply and demand in jeonse market. In turn, it caused the increase of jeonse price. And, in case of jeonse vs. transaction price rate change, the rate increased which means the increase of required rate of return of invested demand. As such, the increase of market risk degenerates the investment sentiment which caused the reduction of quantity for jeonse market as a submarket.

• Land and Housing Review
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• v.9 no.4
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• pp.1-9
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• 2018

Since year 2000, French housing and rent prices rose at a rapid rate and the housing market has been overheated. Face to this phenomena, the French government enacted a new law Alur which is a legislatif tool to control the private housing rent price for the cities, where the tension of the housing market is very high. This new law has impacted the housing market in two major ways. First, for the 38 cities designated by this law, the rent price's increase rate can not rise above the IRL, which is the rent reference index. Secondly, this law also permits local authorities to control the housing rent's price following the concrete price guidance. Especially in Paris, the city applicated this method for private rental housing since 2015. This city classified its own area by 14 zones. Based on the market surveys of each sector, local authority made a guidance for private housing rent's price. The guideline is consisted of average prices, maxima and minima price by types, which is classified by the construction year, number of rooms and furnished or not. Therefore, this study aims to understand french housing rent's price control system and draw implementation for korean housing rent policies. This research is meaningful for it introduces recent foreign regislations which could be helpful to control the housing market in Korea.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.11-23
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• 2021

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

• Journal of Distribution Science
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• v.11 no.10
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• pp.63-72
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• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.151-157
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• 2013

Since the sub-prime mortgage crisis from the US in 2008, the Korean housing market has plummeted. However, the deposit prices of the Korean local lease contract, Chonsei, had been increasing. This increase of Chonsei prices can be a threat to low-income people, most of whom prefer to live in houses with a Chonsei contract. In the housing and Chonsei market, there are many stakeholders with their own interest, hence, simple thoughts about housing and Chonsei market, such as more house supply, will decrease house price, would not work in a real complex housing market. In this research, we suggests system dynamics conceptual model which consists of causal-loop-diagrams for the Chonsei market as well as the housing market. In conclusion, the Chonsei price has its own homeostasis characteristics and different price behavior with housing price in the short and long term period. We found that unless government does not have a structural causation mind in implementing policies in the real estate market, the government may not attain their intended effectiveness on both markets.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.499-506
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• 2017

Consideration of influential factors through division of capital market sector and interest rate sector to find and resolve the problems in current housing market and leasing market will become an important index to prepare measures for stabilization of housing sales market and housing lease market. Furthermore, a guideline will be provide you with preliminary data using Big Data to prepare for sudden price fluctuation because expected economic crisis, stock market situation, and uncertain future financial crisis can be predicted which may help anticipate real estate price index such as housing sales price index and housing lease price index.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.115-130
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• 1999

The price structure of housing market varies depending upon market price policy rather than low or high price policy because of IMF. The object of this study is to develop an analysis system for analyzing housing market and its demand. The analysis system consists of four major categories: macro index analysis, market decision analysis, housing market analysis, and consumer analysis. We model each category by using a variety of techniques such as generalized linear model, categorical analysis, bubble analysis, drill-down analysis, price sensitivity meter analysis, optimum price index analysis, profit index measurement analysis, correspondence analysis, conjoint analysis, and multidimensional scaling analysis. Seoul apartment data is analyzed to demonstrate the practical usefulness of the system.

• Land and Housing Review
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• v.10 no.1
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• pp.9-18
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• 2019

This study examined the dynamic change in the co-movement between the house price rates with the network methods of Mantegna (1999). First, Capital area and non-capital area form independent clusters which have the heterogeneous co-movement pattern. In other words, Capital and non-capital areas have low connectivity in the housing market. Also, if the co-movement between capital areas have been strengthened, the co-movement between non-capital areas have been weakened. The results of the dynamic analysis show that the degree of the co-movement in the housing market is continuously increased. The members of the co-movement group in the capital area are strongly steadied by all periods. However, the members in the non-capital area have been changed according to the period. Accordingly, it is necessary to establish policies based on various information for the housing market of the non-capital area rather than policies targeting the capital area. In addition, Apartments in Korea are more likely to be used as investment or speculative assets than other types of houses. It has been confirmed that this is Gangbuk, which is locatied in the northern part of Seoul, appears to be a region where the Spillover Effects of price fluctuation can be triggered in the housing and apartment market. However, the housing market in Gangnam, which is locatied in the southern part of Seoul, was divided into low systematic risk.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1431-1436
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• 2018

We study the scaling of the price fluctuation in the Korean housing market. From the numerical analysis, we show that the normalized return distribution of the housing price, P(r), has a fat-tail and is well approximated by a power-law, $P(r){\sim}r^{-({\alpha}+1)}$, with ${\alpha}{\simeq}3$ for the whole data set. However, if we divide the data into groups based on the trading patterns, then the value of ${\alpha}$ for positive tail and negative tail can be different depending on the trading patterns. We also find that the autocorrelation function of the housing price decays much slower than that of the stock exchange markets, which shows a unique feature of the housing market distinguished from the other financial systems.