Apartment Market Research Articles

Analysis of Momentum Effects in the Seoul Apartment Market

Analysis of Momentum Effects in the Seoul Apartment Market

분양원가를 공개하면 아파트 가격이 하락하는가?

This study analyzed whether the disclosure of apartment construction cost actually decreased apartment prices using the synthetic control method (SCM) while taking into account the sudden disclosure of apartment construction cost of Segok 2 District 4 (Gangnam Hanyang Sujain) by SH Corporation in February 2022 as a natural experiment. In the analysis, the treatment group was Set to the 85㎡ exclusive private area type of the Gangnam Hanyang Sujain, and the control group was Set to the apartment complexes located within a radius of 2km (#17), 3km (#33), 4km (#46), and 2-4km (#30) centered on the Gangnam Hanyang Sujaini, respectively. The results of the analysis show that apartment prices do not decrease in response to the disclosure of construction cost, in cases where the apartment market price was already established. This result is consistent with various robustness checks. This suggests that the policy of disclosing construction cost to stabilize apartment prices may not align with its purpose.

시계열모형에 의한 서울 아파트시장 가격예측 분석

The purpose of this study is to make a model to forecasting apartment trade price and household rent price in apartment housing market using ARIMA model. On the basis of those models, I tried to forecast the fluctuations of short-term apartment trade price and household rent price in Seoul apartment market. To analyze the ARIMA model, quarterly data during 2011 1/4∼2023 4/4 are used for identification, estimation, diagnosis, and prediction of the ARIMA model. Using ARIMA model, the outcome ARIMA(1,1,0) model is applied to Sepol apartment trade price in the rate of apartment trade price forecasting model, and ARIMA(1,1,1) model is applied to Seoul apartment household rent price in the rate of apartment rent price. According to the forecast results of Seoul apartment trade price and apartment household rent price in the ARIMA model, looking at the quarterly rate of change in Seoul apartment sales prices, it appears that they will continue to go up the 2024 1/4, and will continue to rise but without significant fluctuations. Looking at the quarterly trend of Seoul apartment rental prices on the 2024 1/4 and continue until the 2024 4/4, and the upward trend will be larger than Seoul apt sale price. While the government's housing policy is sluggish, Seoul apartment sales prices and Jeonse prices are expected to be affected by a decrease in apartment supply and interest rates due to a decrease in permits and occupancy this year.

Modeling the Impact of Rent Growth Caps On Washington State’s Apartment Market

Modeling the Impact of Rent Growth Caps On Washington State’s Apartment Market

ARIMA 모형을 이용한 전국 아파트시장 가격 예측

The purpose of this study is to make a model to forecasting apartment trade price and household rent price in apartment housing market using ARIMA model. On the basis of those models, I tried to forecast the fluctuations of short-term apartment trade price and household rent price in national apartment market.
 To analyze the ARIMA model, quarterly data during 2011 1/4∼2023 3/4 are used for identification, estimation, diagnosis, and prediction of the ARIMA model. Using ARIMA model, the outcome ARIMA(2,1,1) model is applied to nationwide apartment trade price in the rate of apartment trade price forecasting model, and ARIMA(1,1,1) model is applied to nationwide apartment household rent price in the rate of apartment rent price.
 According to the forecast results of nationwide apartment trade price and apartment household rent price in the ARIMA model, looking at the quarterly rate of change in national apartment sales prices, it appears that they will continue to fall in the 2023 4/4, but will turn to an upward trend starting in the 2024 3/4, and will continue to rise slightly but without significant fluctuations. Looking at the quarterly trend of national apartment rental prices, the downward trend is expected to turn into an upward trend in the 2023 4/4 and continue until the 2024 4/4, but the upward trend will not be large.
 While the government's housing policy is sluggish, national apartment sales prices and Jeonse prices are expected to be affected by a decrease in apartment supply and interest rates due to a decrease in permits and occupancy next year.

Semiparametric M-quantile regression with measurement error in spatial covariates: an application to housing price modelling

Abstract Spatial data are becoming increasingly accessible to urban scientists, but these data are often prone to measurement error. Motivated by the analysis of the Milan (Italy) apartment market heterogeneity, we propose a semiparametric approach to adjust for the presence of measurement error in the covariates when estimating M-quantile regression. The M-quantile approach helps explain the heterogeneity across individual units, preserving robustness and efficiency in the estimates. The model’s parameters are estimated within a penalised likelihood framework and an analytical expression is proposed to estimate standard errors. Asymptotic properties of estimates are also provided.

Blinder-Oaxaca 분해법을 활용한 서울시 아파트가격지수 작성에 관한 연구

We establish a house price index for Seoul based on a Fisher Ideal aggregation of a two-way Blinder-Oaxaca (BO) decomposition, the FIBO index. Applying the BO decomposition to the apartment market decomposes the intercity differences in the mean house prices into two parts: (i) differences that are due to the differences in housing characteristics across areas (endowment effects) and (ii) differences that arise from the interarea differences in implicit prices of those characteristics (coefficient effects). Using actual transaction data for apartments in Seoul from 2017 to 2022, the FIBO index is constructed based on the estimated coefficient effects. Robustness tests show that our method is far less influenced by changes in sample sizes and selection of the reference. We also show that the FIBO index more accurately reflects the actual movement and fluctuation of apartment prices compared to existing indices, which suggests that the FIBO index could serve as a good complementary house price index.

The asymmetric impact of macroeconomic variables on Islamic bank home financing in Indonesia

Purpose – The study investigates the performance of non-performing financing (NPF) in the housing sector concerning the asymmetric influence of macroeconomic conditions. Methodology – The method of the study adopts nonlinear autoregressive distributed lag (NARDL), while the data of the study ranges from 2014m6 to 2022m2.Finding – The findings reveal an asymmetric relationship between macroeconomic variables and NPF is present both in the short and long run. In addition, the COVID-19 has worsened the NPF of Islamic banks' home financing in some sectors in the short run, while only such financing in the flat and apartment market has suffered during the pandemic in the long run.Implication – The implication of the study suggests that the Islamic banking industry and financial authority need robust risk management and financial policies, respectively, to achieve and maintain the stability of Islamic banks in home financing activities. Originality – The study is original in measuring the impact of macroeconomic influences on Islamic banks' home financing from a nonlinear viewpoint.

Market Preferences of Different Operators of Long-Term Rental Apartments in a Fuzzy Environment

The long-term rental apartment market in China is steadily growing to be a trillion-dollar sector, but excessive market growth can lead to several issues. Due to the high demand for long-term rentals, many investors will enter the market. Nevertheless, without direction and supervision, it is simple to overdevelop the supply of long-term rentals, which would have negative effects on the real estate market. Long-term rental apartments involve a variety of companies, and it is vital that we drive their beneficial growth. To provide other operators with a comparison to find flaws, enhance improvements, and prevent irrationally increasing the stock, this paper uses the Pythagorean fuzzy decision-making method to identify the most well-liked long-term rental apartment operators and the most significant rental needs of tenants in the market environment. The results of the study show that real estate developers’ flats are the most popular among the four major operators, and that C4: Providing emotional value, C7: Ability to resist risk and C8: Ability to prevent social incidents are aspects that tenants value more than others. The results of the study provide real estate operators with directions for optimization, provide other operators with criteria for improvement, prevent blind increases in rental stock and provide operators with a healthy competitive environment, which is of great significance to the healthy development of long-term rental apartments in China.

Extrapolative time-series modelling of house prices: a case study from Sydney, Australia

PurposeHouse price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.Design/methodology/approachWe specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.FindingsMultiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.Research limitations/implicationsWe demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.Originality/valueTo the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

3기 신도시 개발발표가 주변 아파트 가격에 미치는 영향에 관한 연구

This study analyzed the effect of the announcement of new town development in Goyang Changneung district on the prices of surrounding apartments using the difference in difference model based on the Hedonic model. Research that affected the existing development of new cities in the Seoul metropolitan area was limited to the land market. Therefore, for the first time, this study analyzes the Changneung district in Goyang to analyze the impact of the new city on the surrounding apartment market. it was analyzed that the announcement had a negative effect on the price increase in the area near the Changneung district in Goyang, Seoul. On the contrary, it was analyzed that apartments located in Goyang-si had a positive effect of increasing prices unlike the Seoul influence area. Through the analysis results, it was suggested that the influence of the presentation had different effects on apartment sales prices in Seoul and Goyang, respectively.

시기별 아파트 질적 특성요인의 향상에 따른 가격효과 분석

In the domestic apartment market, the quality enhancement and differentiated strategies of construction companies have been implemented since the 2000s. In many ways, the quality characteristics of apartment buildings that have been improved continuously provided a variety of choices in the criteria for housing buyers to purchase. This study empirically analyzed the effect of improving the quality characteristics of apartments by period among the determinants of housing prices on apartments in Gangnam-gu, Seoul through the Hedonic price model. The analysis showed that all the variables of qualitative characteristics, which are the core variables of this study, had a positive effect on apartment prices. It was confirmed that some of the variables corresponding to the qualitative characteristic factors showed higher estimation coefficient values than other variables, which had a greater impact on apartment price formation. In addition, it can be seen that a significant portion of the price effect due to aging, which has been measured as a quadratic function in many previous studies, is the result of overlapping effects due to the improvement of the qualitative characteristics of apartments.

서울 주택구매력지수의 변화가 지방거점도시 주택시장에 미치는 영향에 관한 연구

This study attempted to derive the effect of changes in the Seoul Housing Purchasing Power Index on apartment prices in Jeonju, one of the representative non-metropolitan base cities, and to understand its meaning. The analysis method used VECM in consideration of the characteristics of the variables measured in time series. The summary and implications of the results of this study are as follows.
 First, the change in housing purchasing power in Seoul was found to be one of the main variables affecting apartment prices in Jeonju, a local hub city, and in particular, it was found to have a significant impact on the lag behind. In other words, if housing prices in the Seoul metropolitan area soar and housing investors' ability to invest in the Seoul metropolitan housing market decreases, it is interpreted that they are interested in the apartment market in local hub cities with relatively high price advantages over some time difference. Second, it was found that the change in the population had the greatest effect on apartment prices in Jeonju. Finally, unlike research results on housing prices in Seoul and the metropolitan area, changes in major macroeconomic environments such as total currency, economic growth, and consumer price index did not significantly affect housing prices in Jeonju. This result is judged to be because the level of housing prices is very low compared to the metropolitan area, and the merit as an investment good or an inflation hedge is not high.

GSADF 검정 및 Probit 모형을 이용한 시도별 주택가격거품에 대한 연구

This study estimated the date and duration of the bubble and analyzed what factors were important during bubble episodes in apartment market in Korea from October 2003 to January 2021. Main results were as follows. First, more than two bubble episode appeared in all regions and the negative bubble episode appeared more than once in all regions. It was found that the negative bubble episode generally lasted shorter than that of the positive bubble episode. Second, as a result of the marginal effect analysis it was found that housing sales prices generally increased the probability of bubble and household loan growth and interest rates lowered the probability of bubble. These results differed by regions, with housing sales prices having a greater impact on the probability of bubble occurrence in the non-metropolitan, and household loan growth having a greater impact on the metropolitan.

PSY 검정을 활용한 지역별 아파트가격 거품과 원인에 대한 분석

This study examines the timing of the boom and bust of bubbles in South Korea’s apartment market by region using the test developed by Phillips et al. (2015a, 2015b). The study also identifies the factors causing housing bubbles using ordinary least squares (OLS) and probit models. The sample covers the period from January 2000 to June 2022, comprising 270 monthly observations. The main results of this study are as follows. First, the generalized sup Augmented Dickey–Fuller test results indicate that housing bubbles partially existed in all five regions. Second, the bubble date stamping shows some differences across regions. Third, OLS results indicate that although housing bubbles are inversely related to the loan-to-deposit ratio (LDR), interest rate (IR), and stock index (SI), they are positively associated with the producer price index (PPI), the exchange rate of US dollar to Korean won (ER), and the total amount of interim payment in the construction sector (TIP). Fourth, probit results suggest that the probability of housing bubbles is influenced by PPI, LDR, IR, SI, ER, and TIP in order of magnitude.

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.

The identification of spatial bubbles in a real estate market

There is great interest in identifying and modelling real estate market bubbles over time. However, the spatial dimension of those bubbles gets less attention. While building on the theory of real estate market equilibrium and formation of market bubbles, this research focuses on the spatial bubbles of the market value of apartments inside a city. This approach adds another dimension to the phenomena of real estate market bubbles. The scientific problem of this study is whether the spatial differentiation of real estate encourages the formation of spatial bubbles in real estate market. The object of research is the spatial bubbles in real estate market. The aim of research is to identify the impact of spatial bubbles on real estate market value. Because of the nature of real estate market, the supply and demand of real estate does not match, therefore real estate almost never reaches the equilibrium market value and bubbles appear. The main idea behind this research is that the supply and demand equilibrium model can be applied not only to the changes over time but also across space inside a city. The spatial bubbles appear because of unexplained variance. The empirical research focuses on unexplained variance across space of market value of homogeneous apartments in two major cities in Lithuania. The prominent variance is eliminated through careful selection of projects and control variables such as floor and size of an apartment, distance to civic points and location. Up to 39% of unexplained variance of apartment market value across space was found when looking at 3D diagrams of residualised apartment market value of the selected cities. The main reasons behind formation of those spatial bubbles should be addressed by further research for deeper understanding of the phenomenon of spatial bubble formation.

High-class apartment selection factors in Ho Chi Minh City

This paper aims to find out the current state of the high-class apartment market. Qualitative research is done to adjust and supplement the observed variables for the scale. Quantitative research is conducted through a survey of customers who have bought a high-class apartment or are intending to buy an apartment to build a research model and test the scale. After collecting data, the study conducted the descriptive statistical analysis, testing the reliability of scale. Then, the Exploratory Factor Analysis (EFA) was performed to group the related factors into groups of factors that are independent. The research results showed that there are eight main groups of factors with 31 related observational variables that affected customers' decisions to choose high-class apartments.

Predlog za izboljšavo množičnega vrednotenja nepremičnin v Sloveniji na podlagi pristopa generaliziranih aditivnih modelov

The present paper discusses the heterogeneity of the apartment market. For this purpose, we have developed the model for the mass valuation of apartments in the Republic of Slovenia. The construction of the mass valuation model is based on the generalised additive model approach. In this paper, the development of the model is presented. In the experimental part, the analysis of the results of the two models is performed. The dependent variable (the price of an apartment) is distributed according to the Gaussian and the gamma distributions. Particular attention has been paid to the impact of the transaction time on the apartments’ transaction value. The results of the model are also compared with the results of the mass valuation model in the Republic of Slovenia, which is carried out cyclically and iteratively, the results of which depend on the results (and mass valuation models) of previous cycles.

Trust in peers or in the institution? A decomposition analysis of Airbnb listings’ pricing

ABSTRACT The present paper examines whether Airbnb listing prices differ significantly between ‘casual’ hosts and hosts with multiple listings, regarded as professionals. To this effect the Airbnb apartment market in Athens (Greece) is used as a case study. Drawing on a sample of about 3.500 Airbnb apartments listed in May 2017, the study employs, first, typical hedonic price modelling to assess the determinants of listings’ price of both groups, and then it uses the Blinder–Oaxaca decomposition methodology to examine whether there is a price gap between casual and professional hosts, the characteristics that cause this price gap and their relative contribution to it. The research finds a statistically significant difference in the Airbnb pricing between casual and professional hosts, where about a half of the price gap is attributed to the ‘better’ listings that professionals manage (in terms of size, availability of amenities and locational attributes), and a half to the different reaction hosts have towards specific Airbnb characteristics. In particular, professionals value higher (i.e. place more trust on) the Superhost badge issued by Airbnb as an institution, whereas casual hosts place trust on (and price accordingly) the evaluations provided by the guests.