VAR모형을 이용한 주가, 금리, 물가, 주택가격의 관계에 대한 실증연구

김재경 한국유통과학회 2013 유통과학연구 Vol.11 No.10

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-WeusedKoreanmonthly 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.

중국 주택가격 지수간의 상호관계 실증연구

이기영(Lee, Ki Ryoung),서윤규(Seo, Yun Keuy) 한국부동산학회 2012 不動産學報 Vol.50 No.-

1, CONTENTS (1) RESEARCH OBJECTIVES This article tried to examine into relations and mutual effects between China's representative housing price indices such as housing sales price index, commodity and building price index, commodity housing sales price index. (2) RESEARCH METHOD This study, which performed verification of Granger causality setting the period before the occurrence of financial crisis as observation period, and analysis to response and descriptive ability about correlation and mutual impact between three housing price indices through the impulse response analysis and variance decompositions in time series analysis. (3) RESEARCH FINDINGS The study findings showed that housing sales price index of China's representative three housing price indices has a larger description ability and impact than other two housing price indices to unexpected changes, and if housing sales prices are impacted, which in turn, brings a great response to other two housing price indices, but they do not general a response to themselves. 2. RESULTS The study confirmed if there are any change in relations between housing price indices when values after financial crisis are integrated. If values after 2008 global financial crisis are included, it shows generally similar trends to the experimental results before the crisis, so the financial crisis will be evaluated as have not given a notable change on relations and mutual effects between housing sales price index, commodity and building price index, commodity housing sales price index.

경매시장의 주택가격지수 추정에 관한 연구 -강남3구의 아파트를 중심으로-

이해경 ( Hae Kyeong Lee ),방송희 ( Song Hee Bang ),이용만 ( Young Man Lee ) 한국부동산분석학회 2010 不動産學硏究 Vol.16 No.2

The purpose of this paper is to develop a housing price index in the real estate auction market and to analyse the relationship between auction market and private negotiation market. We use the data on auction for condominium in Gangnam-gu, Seocho-gu and Songpa-gu, Seoul from Q1 2001 to Q2 2009. The SPAR(sale price to appraisal ratio) index model is adopted to estimate the index. After estimating the index, it is compared with the Transaction-based Housing Price Index which is made and released by the government to trace housing price change in the private negotiation market. We test null hypothesis for the equality of both indices and check up the cross correlation between two indices. We find that the null hypothesis are not rejected and the auction price index is coincident with the Transaction-base Housing Price Index. Next, we compare the auction price index with the KB Housing Price Index which is one of the appraisal-based index for the private negotiation market. We find that the auction price index is more volatile than the KB Housing Price Index and leads the KB Housing Price Index by 1 quarter. The result seems to come from the smoothing of the KB Housing Price Index.

VAR 모형을 이용한 주택시장 분석

김재경(Kim, Jae Gyeong),이왕무(Lee, Wang Mu) 한국지적학회 2013 한국지적학회지 Vol.29 No.2

주택시장이 국민경제에서 차지하는 비중이 크고 경제에 미치는 파급효과가 크기 때문에 VAR모형을 이용하여 아파트매매가격에 대한 전국지수, 서울지수, 수도권지수, 6대광역시지수들 간의 관계분석, 그리고 아파트가격지수들과 물가의 동태적 상관관계를 분석하였다. 인과관계분석결과 물가지수와 서울가격지수가 6대광역시가격지수에 그랜저 코즈하지만 역의 관계는 성립하지 않는다. 충격반응분석결과 전국지수가 서울지수의 충격에 의해 정(+)의 영향을 받지만 수도권지수에 의해서는 부(-)의 영향을 받는다. 모든 아파트 가격지수들의 상승이 단기적으로 물가를 하락시키는 역헤징 현상도 나타났다. 분산분해분석결과 서울, 수도권, 6대광역시 아파트 가격은 전국아파트 가격변동에 의해 가장 크게 영향을 받지만, 수도권아파트가격은 자신의 가격변동보다 전국과 서울의 가격변동에 의해 더 큰 영향을 받는다. 6대광역시지수에 대한 서울아파트 가격변동의 영향은 상대적으로 작은 것으로 나타났다. Using a VAR approach, this study examines the relationship and dynamic interactions among the housing price indexes in whole country, seoul, capital area and 6 metropolitan city, and additionally consumer price index and 4 housing price indexes. The results from the empirical analysis are as follows: first, price index and seoul housing price index Granger cause 6 metropolitan housing price index, but not vise-versa. Second, the impulse response tests show that the whole country housing price is positively affected by the shock to the seoul housing price, but negatively affected by the shock to the capital housing price. One standard deviation shocks in whole country housing price decreases the 0.022 unit of price index after 3 months. Third, variance decomposition shows that seoul, capital area and 6 metropolitan housing price are explained most by whole country housing price index. The capital housing price index is explained by whole country and seoul housing price rather than itself variation. The variation of seoul housing price has a little impact on the 6 metropolitan price.

주택가격지수의 변화 패턴 분석

이형욱(Lee, Hyung Wook),이호병(Lee, Ho Byung) 한국부동산학회 2011 不動産學報 Vol.47 No.-

1. CONTENTS (1) RESEARCH OBJECTIVES The purpose of this paper is to analyze housing price index predictabilities by using ARIMA and artificial neural network models in Korean metropolitan areas. The data of housing price index were collected for the seven metropolitan areas such as Seoul, Incheon, Daejeon, Daegu, Ulsan, Busan, and Gwangju, and categorized into groups with similar changing patterns by cluster analysis. The data of housing price index from January 1986 to May 2008 were used to analyze the changing patterns of housing price index for the metropolitan areas. (2) RESEARCH METHOD In this study, housing price index predictabilities were analyzed by using the analytic techniques such as ARIMA, artificial neural network model, cluster analysis and ANOVA. (3) RESEARCH RESULTS The result of this empirical study showed that there were the three changing pattern grour:e of housing price index - 1) Seoul and Incheon, 2) Daejeon, Daegu, Ulsan and Busan, and 3) Gwangju. 2. RESULTS The results of this paper can be summarized as follows. First, the result of the cluster analysis showed that there were the three changing pattern groups of housing price index-1) Seoul and Incheon, 2) Daejeon Daegu, Ulsan and Busan, and 3) Gwangju. The first group showed the highest rising pattern in housing price index, while the third one showed the lowest rising pattern. Second, artificial neural network model was more excellent than ARIMA model in a stable pattern of housing price index, while the former model was worse than the latter in a steep pattern of housing price index.

소비심리와 주택매매가격이 경매낙찰가율에 미치는 영향

전해정(Chun, Hae Jung) 한국주거환경학회 2018 주거환경(한국주거환경학회논문집) Vol.16 No.3

The purpose of this study is to analyze the effect of consumer sentiment and housing sales price on the housing auction price ratio. This study was analyzed using panel vector error correction model(VECM). The independent variable is the housing auction price ratio and the dependent variables are the housing consumer sentiment index and housing sales price. The temporal range is from July 2011 to May 2017, and the spatial ranges are Seoul, Gyeonggi-do and Incheon. As a result of variance decomposition analysis, the explanatory power of the housing auction price ratio was the largest, and the housing consumer sentiment index and the housing sales price showed the highest explanatory power. As a result of impulse response analysis, the housing auction price ratio showed a positive (+) response to the housing consumer sentiment index. The housing auction price ratio showed a positive (+) response to the housing sales price, and the housing sales price showed a positive (+) response to the housing auction price ratio. Housing sales price and consumer sentiment index showed a positive(+) response on each other. The housing sales market and the auction market were found to be coupled. Especially, it was found that consumer sentiment had a great influence on the housing market and aution market.

전국과 서울주택시장간의 매매와 전세가격의 정보이전 메커니즘을 통한 전략적 접근에 관한 연구

김광수(Kim, Kwang-Soo),문규현(Moon, Gyu-Hyen) 대한경영학회 2011 大韓經營學會誌 Vol.24 No.6

주택가격지수 가격변동 특성비교 연구

황관석 ( Hwang Gwanseok ),김지혜 ( Kim Jeehye ),권건우 ( Kwon Geonwoo ) 한국부동산분석학회 2024 不動産學硏究 Vol.30 No.1

이 연구는 국가 승인통계인 한국부동산원의 주택가격지수를 중심으로 주택가격지수의 현형과 특성, 각 지수의 가격변동 특성을 비교 · 분석하고 시사점을 도출하였다. 주택가격지수는 이용자료에 따라 조사가격기반 지수와 실거래 기반 지수로 구분할 수 있다. 조사기반 지수는 대표성을 확보할 수 있고 시의성이 높으나 낮은 변동성, 평활화 효과가 발생할 수 있으며, 실거래 기반지수는 체감력이 높으나, 표본의 대표성, 시의성 측면에서 한계가 존재한다. 주택가격지수별 가격변동 특성을 비교 분석한 결과, 한국부동산원 지수, KB 지수는 실거래지수 및 부동산114지수와 비교하여 상대적으로 가격 변동성이 작고, 후행하는 특성을 보였다. 주택시장의 순환국면에 따라서도 확장기에는 가격변동성이 크고, 실거래 반영비율이 높지만, 수축기에는 가격변동성이 낮고 실거래 반영비율이 낮은 하방경직성의 특성이 나타났다. 특히, 승인통계인 한국부동산원 지수가 다른 지수와 비교하여 평활화 효과가 큰 것으로 나타나 실거래 포착능력을 보다 확대할 수 있도록 지수를 개선할 필요가 있다. This study compared and analyzed the current form and characteristics of the housing price index and the price fluctuation characteristics of each index, focusing on the Korea Real Estate Agency's housing price index, which is a nationally approved statistics, and derived implications. Housing price indices can be divided into survey price-based indices and actual transaction-based indices depending on the data used. Survey-based indices can secure representativeness and have high timeliness, but low volatility and smoothing effects may occur, while real transaction-based indices have high perceived power, but there are limitations in terms of representativeness and timeliness of the sample. As a result of comparing and analyzing the price fluctuation characteristics of each housing price index, the Korea Real Estate Agency index and the KB index showed relatively small price volatility and lagging characteristics compared to the actual transaction index and the real estate 114 index. Depending on the cyclical phase of the housing market, during the expansion period, price volatility is large and the actual transaction reflection ratio is high, but during the contraction period, price volatility is low and the actual transaction reflection ratio is low, showing characteristics of downward rigidity. In particular, there is a problem with the Korea Real Estate Board index, which is an approved statistic, having a large smoothing effect compared to other indices, so there is a need to improve the index to further expand the ability to capture actual transactions.

공동주택 실거래가지수 산정에 관한 연구

이창무,김진유,이상영 대한국토·도시계획학회 2005 國土計劃 Vol.40 No.4

Recently, real transaction price change has become more important in Korea. In fact, real transaction price had never been surveyed before the government enacted the Housing Real Transaction Price Report Regulation for capital gain tax in 2004. Therefore, almost all previous studies analyzed the housing market by examining the asking price. However, the asking price is generally different from the transaction price and the study results based on it can distort housing policy. If the asking price change does not exactly mirror the real housing transaction price change, housing policy based on it can distort housing market through inappropriate reaction. Therefore, in order to understand the real change of housing market and establish a more effective housing policy, it is necessary to analyze not only asking price but also transaction price. This study investigates asking price change and real transaction price change with 51 week(12 month) data reported by realtors. The results show the differences between two price indices. First, the transaction price fluctuates more widely than asking price. Especially, the price gap increases when the real transaction price index decreases. Second, the real transaction price change precedes asking price change. In conclusion, this study implicates that housing policy should be established according to the real transaction price change rather than asking price change.

제2기 수도권신도시 및 주변지역 아파트가격지수 추정

송의현,김경민 한국감정원 2019 부동산분석 Vol.5 No.2

This paper suggests housing price index methodology to examine changes in housing prices of areas narrower than the area scope of the existing housing price index, and analyzes changes in apartment transaction prices as well as Jeonse prices of the 2nd new towns in Seoul Metropolitan area by utilizing this methodology. We control the characteristics of individual transactions, using a hedonic price model, and only price changes are indexed over a period of time. This allows to create a housing price index for areas smaller in scale than cities, counties, and districts, which are units for the existing indices. The areas of analysis comprises Seongnam Pangyo, Suwon Gwanggyo, Hwaseong Dongtan, and their nearby residential areas including Seongnam Bundang, Yongin Suji, Yongin Jukjeon, Yongin Guseong, Yongin Dongbaek, Suwon Yeongtong, Hwaseong Byeongjeom, Osan Segyo, and the old city center of Osan. The results of analysis are as follows. First, changes of price in the individual residential areas varied considerably. Second, even during the so-called period of housing price explosion after 2017, there were areas where price changes were not significant or even price decreased depending on the residential areas. Third, jeonse prices rose in all the areas during periods of falling interest rates. Fourth, the rise of jeonse prices followed the rise of the transaction prices when housing market recovered in 2013. Finally, even among apartment complexes supplied to the same area during the same period, there is a price gap of as much as 30~40% depending on their locations. 본 연구에서는 기존의 주택가격지수들의 작성범위보다 좁은 지역의 주택가격변화를 살피기 위한 지수작성방법을 제시하고, 이를 이용하여 제2기 수도권신도시 및 인근지역의 아파트 매매가와 전세가의 변화를 분석하였다. 특성가격함수를 이용하여 개별거래사례의 특성을 통제하였으며, 시점에 따른 가격변화 만을 지수화 하였다. 이를 통해 기존 지수들의 최소 작성단위인 시군구보다 더 작은 지역의 지수를 작성할 수 있었다. 분석지역은 수도권 남부 제2기 신도시인 성남판교, 수원광교, 화성동탄 및 인근 주거권역인 성남분당, 용인수지, 용인죽전, 용인구성, 용인동백, 수원영통, 화성병점, 오산세교, 오산구도심이다. 분석 결과는 다음과 같다. 첫째, 개별 주거권역의 가격변화가 매우 상이했다. 둘째, 2017년 이후 이른바 주택가격 폭등기라 일컬어지는 기간에도 주거권역에 따라 가격변화가 크지 않거나 오히려 떨어진 지역도 존재했다. 셋째, 금리하락기에 전체 권역에서 전세가격 상승이 관찰되었다. 넷째, 2013년 주택시장 회복기에서 전세가격 상승이 매매가격 상승이 뒤따랐다. 다섯째, 동일한 시기 동일한 지역에 공급된 아파트 단지라 하더라도 입지에 따라 많게는 30~40%의 가격격차가 존재함을 확인할 수 있었다.