Sales Price Index Research Articles

딥러닝을 활용한 서울시 주택 유형별 전세가격에 영향을 미치는 요인분석

1. CONTENTS
 (1) RESEARCH OBJECTIVES This study used deep learning DNN model to empirically analyze the factors affecting Chonsei prices of each housing type in Seoul.
 (2) RESEARCH METHOD The output variables are set as the apartment Chonsei price index, the single-family home Chonsei price index, and the townhouse Chonsei price index, and the input variables are the apartment sales price index, single-family home sales price index, townhouse sales price index, number of employed people, and corporate bond yield. housing construction permit records. The spatial scope was set to Seoul, and the temporal scope was set from January 2007 to October 2023.
 (3) RESEARCH FINDINGS Looking at the LIME(Local Interpretable Model-agnostic Explanations) of each Chonsei prices input variable by housing type, when the apartment sale price index is -0.66 or less, the single-family house sale price index is in the range of -0.66 to -0.38, and the townhouse sale price index is -0.59 or less. it was found to have a negative impact on the Chonsei price index for each housing type. When the corporate bond yield was in the range of -0.89 to -0.23 for apartments, -0.89 to -0.23 for single-family homes, and -0.89 to -0.23 for townhouses, it was found to have a positive (+) impact on the Chonsei prices index for each housing type.
 2. RESULTS
 The implications are that although there are differences by housing type, the Chonsei price appears to be greatly influenced by the sale price and interest rates. Therefore, the government must continuously monitor the Chonsei price and sale price by housing type to ensure housing stability for the working class and middle class and appropriate liquidity management.

개발 호재가 아파트 분양 프리미엄에 미치는 영향

1. CONTENTS
 (1) RESEARCH OBJECTIVES
 The purpose of this study is to examine how significant the construction of Samsung Electronics' Pyeongtaek Campus and the SRT Pyeongtaek-Jije Station, which are favorable developments in the Pyeongtaek area, affect the premium of apartment sales rights.
 (2) RESEARCH METHOD
 Hedonic price model was used for the factors affecting the actual transaction price, and multiple regression analysis was applied to find out the factors affecting the premium of the actual transaction right.
 (3) RESEARCH FINDINGS
 Model 1 (related to SRT Pyeongtaek Jije Station) empirical analysis showed that variables such as the number of floors, 2021 contract rankings, household parking, interest rates, exchange rates, and Pyeongtaek apartment sales price index had a positive (+) effect. The distance from the SRT Pyeongtaekji Station, a characteristic of favorable development, was found to have the second-largest influence on the dependent variable among the variables.
 Model 2 (related to Samsung Electronics' Pyeongtaek Campus) empirical analysis showed that the number of floors, 2021 contract ranking, exclusive area, number of parking lots by generation, interest rate, exchange rate, Pyeongtaek apartment sales price index had a positive (+) effect. Whether it was completed in 2018, the distance to Samsung Electronics' Pyeongtaek campus, the distance to the school, distance to commercial facilities, and the overall stock index were significant and had a negative (-) effect. The distance from Samsung Electronics' Pyeongtaek campus, a characteristic of favorable development, was found to have the greatest influence on the dependent variable among the variables.
 2. RESULTS
 The results of the study provide basic data that can be used to predict the real estate market when developing station areas around high-speed railway stations by grasping the impact of the opening of SRT Pyeongtaek Jije Station on the premium of the apartments around the area. This is expected to be useful data in grasping the degree of change in the value of local real estate assets and analyzing the development effect when constructing additional transportation facilities in the future

종합부동산세의 정책적 변화가 아파트 매매가격에 미치는 영향에 관한 연구

1. CONTENTS
 (1) RESEARCH OBJECTIVES
 This study is a study on the impact of policy changes in comprehensive real estate tax on apartment sales prices. The purpose of this study is to examine the effects of real estate characteristics, macroeconomic characteristics, and comprehensive real estate tax characteristics on apartment sales prices in the metropolitan area, Seoul, Gyeonggi-do, and Incheon. In particular, we want to know how policy changes in comprehensive real estate holding tax affect apartment sales prices through the characteristics of comprehensive real estate tax.
 (2) RESEARCH METHOD
 This research method was analyzed as a fixed effect model using the Hausman test.
 (3) RESEARCH FINDINGS
 This study has the following implications. First, it examines the impact of policy changes in comprehensive real estate holding tax in the metropolitan area, Seoul, Gyeonggi-do, and Incheon on apartment sales prices. This study can be said to have implications at the present time in terms of comprehensive real estate holding tax changes due to the real estate recession. Second, this study was constructed with panel data combining time series and cross-sectional data, and attempted to determine the relevance of the apartment sales price index by dividing it into real estate characteristics, macroeconomic characteristics, and comprehensive real estate holding tax characteristics. Third, at a time when changes to the comprehensive real estate holding tax are taking place, research on the impact of the comprehensive real estate holding tax on apartment sales prices will be important, and regional differences in the metropolitan area on the impact of the comprehensive real estate holding tax by characteristics will be examined. This will be seen as meaningful.

심리적 요인이 지역별 주택가격에 미치는 영향

In order to examine the influence of consumer sentiment on housing prices by region, this study conducted an empirical analysis using the VARX model, including apartment sales price index, consumer sentiment index, apartment construction permit performance, corporate bond yield rate, and consumer price index. As a result of the VARX model estimation, the change rate of the housing consumer sentiment index has a significant positive (+) effect on the change rate of apartment sales prices, the change rate of the corporate bond yield has a negative (-), and the change rate of the consumer price index has a positive (+) effect in all regions. Looking at the coefficient values, it was found that consumer sentiment, corporate bond yield, and consumer price index had a greater impact on the rate of change in apartment sales prices in Seoul than in other regions. As a result of the impulse response analysis, the change rate of apartment sales price showed a positive (+) response to the shock of the change rate of housing consumption sentiment in all regions. The change rate of apartment sales price showed a weakly negative (-) response to the shock of the apartment construction permit performance in Seoul, was converged with a positive (+) response in nationwide and in non-metropolitan areas. It was found that the impact of the change rate of housing consumption sentiment in Seoul has a greater impact on the change rate of apartment sales prices than in other regions.

중소형 오피스 빌딩의 거래가격에 영향을 미치는 요인에 관한 연구

This study conducted a demonstration of the price formation factors of small and medium-sized office buildings accessible to individual investors. The various factors that affect the price determination of real estate are fixed factors that the real estate itself has, and it is subdivided into regional conditions, traffic conditions, access conditions, and building conditions. In addition, the macro environment, regional environment, and trader environment that fluctuate according to the market environment to be focused on in this study were added. The extent to which these various factors affect the formation of the transaction price of small and medium-sized office buildings was analyzed. Let's take a look at the main analysis results of market environmental factors. As factors related to the macroenvironment, interest rates and CPI variables significantly affected the transaction prices of small and medium-sized buildings. In addition, changes in the apartment sales price index and publicly announced land prices had a statistical effect on the formation of the transaction price of small and medium-sized buildings. In addition, the transaction price of small and medium-sized buildings was formed higher in the case of corporate transactions than individual transactions.

국내 가계대출의 특징과 결정요인 분석

Purpose - Since COVID-19, the government's expansion of liquidity to stimulate the economy has resulted in an increase in private debt and an increase in asset prices of such as real estate and stocks. The recent sharp rise of the US Federal fund rate and tapering by the Fed have led to a fast rise in domestic interest rates, putting a heavy burden on the Korean economy, where the level of household debt is very high. Excessive household debt might have negative effects on the economy, such as shrinking consumption, economic recession, and deepening economic inequality. Therefore, now more than ever, it is necessary to identify the causes of the increase in household debt. Design/methodology/approach - Main methodology is regression analysis. Dependent variable is household loans from depository institutions. Independent variables are consumer price index, unemployment rate, household loan interest rate, housing sales price index, and composite stock price index. The sample periods are from 2017 to May 2022, comprising 72 months of data. The comparative analysis period before and after COVID-19 is from January 2017 to December 2019 for the pre-COVID-19 period, and from Jan 2020 to December 2022 for the post-COVID-19 period. Findings - Looking at the results of the regression analysis for the entire period, it was found that increases in the consumer price index, unemployment rate, and household loan interest rates decrease household loans, while increases in the housing sales price index increase household loans. Research implications or Originality - Household loans of depository institutions are mainly made up of high-credit and high-income borrowers with good repayment ability, so the risk of the financial system is low. As household loans are closely linked to the real estate market, the risk of household loan defaults may increase if real estate prices fall sharply.

아파트 매매가격, 전세가격 및 월세가격 간의 수익률 전이효과

In this paper, I analyze the spillover effects in apartment Sales, Chonsei, and Monthly Rent Markets. I measure the volatility spillover effect using the method of Diebold & Yilmaz. First, I find that spillover effects exist between the apartment sales price index, the Chonsei price index, and the monthly rent price index in Korea real estate markets. Second, I find that the apartment sales and Chonsei prices lead the rent price. Third, I confirm that the trading market leads the monthly rental market and the Chonsei market leads the monthly rental market. And I find that the importance of the trading market as the trading market leads the Chonsei market. Fourth, I find that the total spillover index declines in April 2020 after the outbreak of COVID-19. Fifth, as a result of the average value test, I confirm that the net spillover effect of the sale price and the Chonsei price increases after the outbreak of COVID-19, playing a leading role in the market. On the other hand, I find that the net spillover effect of the monthly rent price decreases further and the dependency becomes stronger.

주택조세강화가 수도권 아파트 가격에 미치는 영향에 관한 연구

In this study, the effect of the recently strengthened heavy taxation of capital gains tax and comprehensive real estate tax on the apartment market in the metropolitan area was analyzed using the vector error correction model. As a result of the shock response analysis, the volume of apartment sales transactions in the Seoul metropolitan area had the greatest impact on the apartment sales price index, followed by mortgage loans in the Seoul metropolitan area and GDP. On the other hand, it was found that there was no significant shock reaction to the heavy capital gains tax and the heavy comprehensive real estate tax. However, most parallaxes show negative (-) values, indicating that they negatively affect apartment sales prices in the Seoul metropolitan area. As a result, it can be seen that the apartment market in the metropolitan area is generally affected by changes in the housing tax system, such as heavy capital gains tax and heavy comprehensive real estate tax, along with housing finance, supply, and macroeconomic factors. As a result of this analysis, the housing-related tax strengthening policy can contribute to stabilizing the housing market by lowering housing prices during the rapid rise in housing prices. However, the tax strengthening policy during the period of falling housing prices due to the economic recession can cause a sharp downturn in the housing market.

시공사 브랜드가 아파트 분양가와 투자수익에 미치는 영향

Depending on the economic situation, real estate policies have been announced with the goal of stabilizing real estate or revitalizing the economy. Before implementing real estate policies, effective operation and evaluation of government policies are essential. Real estate exhibits different regional variations due to its unique location fixity, and as a consumer and investment good, it is a commodity that is affected in a complex way, such as liquidity, supply, macroeconomics, and psychology. Real estate policies vary by tax policies such as holding tax and capital gains tax, and housing finance regulations (LTV, DTI). From this point of view, there is a need for a tool that can analyze the comprehensive policy effects including various types of real estate policies of the government. In this study, the available variables among the macroeconomic variables of the real estate market macroeconomic model designed on the premise of the ripple path following the implementation of real estate policies and the apartment sales transaction price index in the metropolitan area and nationwide, which is a proxy for apartment houses, were set as variables and analyzed through multivariate analysis. Analyze dynamic variations between variables. The purpose of this study is to examine the effect of housing finance regulation on housing mortgage loans in the national and metropolitan areas among real estate policies and the effect of tax policies and mortgage loans on apartment house prices (apartment sale price index) among real estate policies. We would like to focus on analyzing the effectiveness of the policy.

시공사 브랜드가 아파트 분양가와 투자수익에 미치는 영향

Depending on the economic situation, real estate policies have been announced with the goal of stabilizing real estate or revitalizing the economy. Before implementing real estate policies, effective operation and evaluation of government policies are essential. Real estate exhibits different regional variations due to its unique location fixity, and as a consumer and investment good, it is a commodity that is affected in a complex way, such as liquidity, supply, macroeconomics, and psychology. Real estate policies vary by tax policies such as holding tax and capital gains tax, and housing finance regulations (LTV, DTI). From this point of view, there is a need for a tool that can analyze the comprehensive policy effects including various types of real estate policies of the government. In this study, the available variables among the macroeconomic variables of the real estate market macroeconomic model designed on the premise of the ripple path following the implementation of real estate policies and the apartment sales transaction price index in the metropolitan area and nationwide, which is a proxy for apartment houses, were set as variables and analyzed through multivariate analysis. Analyze dynamic variations between variables. The purpose of this study is to examine the effect of housing finance regulation on housing mortgage loans in the national and metropolitan areas among real estate policies and the effect of tax policies and mortgage loans on apartment house prices (apartment sale price index) among real estate policies. We would like to focus on analyzing the effectiveness of the policy.

아파트매매가격지수와 아파트전세가격지수 사이의 선형 및 비선형 인과관계 분석

The purpose of this study was to examine a linear and a nonlinear causality between apartment sale prices and apartment jeonse prices in Korea. The non-parametric method for the nonlinear causality test was adopted to avoid model specification errors. Our empirical results showed that a feedback relationship between two prices is observed on both linear and nonlinear tests. When we included interest rates, stock prices and exchange rates to control the macroeconomic conditions, there was the feedback relationship between the apartment sale price index and the apartment jeonse price index on the both tests. However, the nonlinear test with the bivariate BEKK-GARCH model demonstrated that the apartment jeonse price index Granger-caused the apartment sale price index. This implied that the changes in apartment jeonse prices has an ability to predict apartment sale prices. Our results suggested that investors and policy makers should consider fluctuations in apartment jeonse prices for investment decisions and for establishing the real estate policies.

Real estate listings and their usefulness for hedonic regressions

Real estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications. This paper asks whether it is valid to do so in the established research areas of (1) willingness to pay estimation, (2) automated valuations, and (3) price index construction. It therefore compares listings and transaction data and regression results derived from them. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. But estimates of implicit prices also differ. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. Listings data are not very useful to predict market values of individual houses either, as these predictions suffer from upward bias and large error variance. We find, however, that an ask price index complements a sale price index, as it is useful for nowcasting.

수도권 지역 주택가격 순환주기 변동의 영향요인 분석

The purpose of this study is to analyze the cycle variation of housing sales price in Seoul, Gyeonggi, and Incheon and the factors that influence them. For the purpose, the apartment sales price index, rent price index, 3-year corporate bond interest rate, currency volume, and consumer price index data from November 2003 to July 2020 were used to analyze HP filter and Bayesian VAR model. As a result of the analysis, the housing sales price were 4 cycle variations in Seoul and Gyeonggi and 3 cycle variations in Incheon. In Seoul, the expansion period lasted longer than the contraction period, but the contraction period was longer in Gyeonggi and Incheon. In addition, it was found that the rent price index, corporate bond yield, and currency volume had a significant influence on the cycle variation of housing sales price, and there were differences in influence factors by region. This suggests that the government's real estate policy needs to be subdivided and established by region rather than a consistent regulatory-oriented policy.

방송뉴스 감성지수와 서울시 주택매매가격의 상관 및 인과관계 분석*

The study intends to understand whether the real estate sentiment index, calculated using real estate-related broadcasting news, has a significant correlation or causal relationship with the apartment sales price index based on data from 2012 to 2018 in the Seoul Metropolitan Area. We find that the broadcasting sentiment index correlates positively with the apartment sales price index; an increase in the broadcasting sentiment index results in an increase in the sales price index in Seoul, which again has a significant impact on the increase in the broadcasting sentiment index. In addition, the broadcasting sentiment index indicates a significant causal relationship between one-month and five-month time difference with the sales price index. The broadcasting sentiment index has a significant impact on the fluctuations in the apartment sales price index. It suggests that the mechanism of the real estate market can be explained and predicted by utilizing text mining and unstructured big data. It also implies that artificial intelligence techniques such as machine learning can be applied to the field of real estate.

Information value of property description: A Machine learning approach

This paper employs machine learning to quantify the value of “soft” information contained in real estate property descriptions. Textual descriptions contain information that traditional hedonic attributes cannot capture. A one standard deviation increase in the uniqueness of a property based on this “soft” information leads to a 15% increase in property sale price in a hedonic price model and a 10% increase in a repeat sales price model. The effects in the hedonic model appear to arise through two channels: the unobserved quality of the housing unit, and the market power of the housing unit relative to competing properties. The effects in the repeat sales model appear to be driven entirely by the market power of the unit. Further, an annual hedonic price index ignoring our measure of unobserved quality overstates real estate prices by between 10% to 23% and mistimes the stabilization of housing prices following the Great Recession. Similar, but smaller effects, are observed for the repeat sales price index.

Comparative Study on the Prediction of the Apartment Sales Price Index

부동산에 편중되어 있는 가계의 자산구조로 인해 부동산 가격 변동은 국가경제에 큰 영향을 미치게 된다. 주택가격에 대한 정확한 예측은 주택가격의 불확실성을 감소시킬 뿐만 아니라 관련 정책의사결정에 도움을 줄 수 있다. 본 연구의 목적은 2006년 1월부터 2019년 11월까지의 서울 및 6대 광역시 아파트 매매 실거래 가격지수를 대상으로 계량 시계열 모형과 인공신경망 모형을 활용하여 주택가격을 예측하고, 높은 예측력을 지닌 예측모형을 구축하는 데에 있다. 분석결과를 통해 정책적 시사점을 도출하고자 한다. 본 연구에서는 기존의 계량시계열 모형 및 표준 LSTM 모형이외 양방향(Bidirectional) LSTM 및 GRU모형을 추가하여 제시된 모형의 예측력을 비교분석하여 높은 예측력을 지닌 인공신경망 모형을 제시하였다. 실증분석결과 지역에 따라 높은 예측력을 보이는 인공신경망 모형이 다르다는 것을 확인하였다. 지역별로 예측력 높은 인공신경망 모형 구축 후 변수의 추가·삭제와 같은 자유로운 조작을 통해 지역에 적합한 거시경제변수가 주택가격지수에 미치는 효과를 파악하여 원하는 정책효과를 거두는 것이 가능할 것이다.

Price competition between green and non green products under corporate social responsible firm

This article deals with a newsvendor inventory model in light of green product marketing of corporate social responsible firms. In this model, comparison between green and non green marketing is analyzed including subsidy and tax implementation by Government where the Government offers higher subsidy and lower tax to the green producer unlike the lower subsidy and higher tax to the non green producer. There is also price contest between green and non green producer as the demands of the products are dependent on sales price, carbon emission and corporate social responsibility index. Assuming the cost and profit parameters, an expected profit function of the systems is formulated and maximized analytically. Finally, numerical examples are illustrated to justify the proposed model.

A Study on the Dynamic Causal Relationship between Housing Price and Housing Trading Volume

The purpose of this study is to examine the characteristics of the housing market that differs by region by analyzing the correlation between the housing prices and the transaction volume of seven major cities including Seoul. For the analysis, apartment transaction volume and sales price index of Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan were used. The data was monthly data released by the Korea Appraisal Board and from January 2006 to December 2019. Analytical methods were performed by Granger causality tests, impulse response analysis and variance decomposition analysis. The empirical results of the study can be summarized as follows. First, it is possible to confirm that the sale price and the volume of the house are operating in a positive correlation, but differs depending on the characteristics of the housing market in the area. This suggests that, as suggested in the previous study, price fluctuations affect trade volume fluctuations and trade volume fluctuations have mutual causal effects on price fluctuations. However, only Daegu and Incheon are identified as two-way correlations. Second, an increase in sales prices leads to an increase in trading volume in the short term, while the increase in trading volume is not significant for fluctuations in sales price. Third, as a result of variance decomposition analysis, that the impact of price on the volume of trade is relatively higher than the impact on price on volume. In areas where trade prices and volume have a mutual influence on trade volume change, explanatory power increased from short to long term.

THE EXCHANGE RATE EFFECT ON HOUSING PRICE INDEX AND REIT INDEX RETURN RATES

Real estate is an important investment instrument, and homeownership has always been considered as a valuable investment tool in Turkey. Real Estate Investment Trusts (REITs) are one of the first real estate investment instruments that the Capital Market Board of Turkey (SPK) legally regulated. The exchange rate is an important factor that affects many industries including real estate, especially in emerging economies. This macroeconomic economic factor became more important for Turkish housing and real estate industries after the law issued in 2013 that enabled foreigners to purchase housing units in Turkey. The aim of this research is to examine the effect of exchange rates (USD/TL) on the return rates of REIT and housing sales price indices by using time series analysis covering the monthly period of January 2004-December 2016. VAR econometric model is used to test the effect of the exchange rates on the return rates of both indices, and Augmented Dickey-Fuller and Granger Causality Tests were also employed. The results of the study exhibited that while REIT index return rate is affected by exchange rates, there is no statistically significant effect of exchange rates on housing sales price return rates. This result may affect the perception of the investors about considering housing as an investment tool in Turkey and open a new discussion for alternative future retirement plans that may combine different real estate-based investment instruments.

Gross domestic product growth rate analyzing based on price indexes, import and export factors

Economic development could be presented by gross domestic product to show how different factors affect the development. Gross domestic product could be affected by different nonlinear factors in positive or negative way. Hence it is suitable to apply artificial intelligence techniques in order to track the gross domestic product variation in depend on the factors. AI techniques require only input and output data pairs in order to catch the output variations based on the input factors. Therefore in this study adaptive neuro fuzzy inference system was applied in order to select the most relevant factors for gross domestic product growth rate. These factors are whole sale price index, consumer price index in urban areas, consumer price index in rural areas, state per capita income, exports, import and industry income. Results shown that the whole sale price index has the highest relevance on the gross domestic product growth rate.