Apartment Prices Research Articles

HOW DO DIFFERENT NOISE POLLUTION SOURCES AFFECT APARTMENT PRICES?

The paper analysed the impact of different noise sources on the residential market. This research used the hedonic method in OLS, SAR and SEM models based on the data set containing geocoded 16,247 apartments in Poznan. Strong evidence was found that noise is negatively linked with apartment prices. The apartment prices were the most significantly influenced by railway noise – an increase of 1 dB above 55 dB causes a 1.79% decrease in the value of an apartment. For other noise sources, aviation noise had the most significant impact with a 0.59% decrease in value per dB, tram noise with a 0.32% decrease and road noise with a 0.12% decrease. The influence of different noise levels on apartments is not constant and does not assume a linear relationship. For road noise, noise below 60 dB and tram noise below 65 dB were statistically insignificant. This may indicate that these noise levels are acceptable and are compensated by better access to public roads or urban transport.

Integration and Cointegration of Apartment Prices on the Primary and Secondary Market in Szczecin in the Years 2006-2022

Abstract The objective of the paper is to verify hypotheses regarding integration and cointegration (relation) of mean apartment prices on the primary and secondary market in Szczecin. Both transaction prices as well as offer prices of apartments were investigated. The analysis period encompasses the years of 2006 – 2022 (quarterly data). An ADF test was employed to examine the integration of time series, taking into consideration a deterministic component in the form of a quadratic function. Only the time series of mean offer prices and transaction prices on the primary market proved to be integrated in the first degree. The time series of mean offer prices and transaction prices on the secondary market were not integrated, they occurred to be trend stationary series. A two-step Engle-Granger test was employed to analyze the cointegration of time series, which confirmed the cointegration of mean offer prices and transaction prices on the primary market. The relations between individual price types were examined with the use of a procedure which entailed analyzing (with an ADF test) difference stationarity between prices. From the empirical studies it arises that, in Szczecin, transaction and offer prices on the primary market follow one another. On the secondary market, offer and transaction prices are trend stationary and they converge. On the other hand, prices on the primary market diverge from prices on the secondary market (the primary market diverges from the secondary market). This concerns both offer prices as well as transaction prices.

숙박시설 실거래가격에 미치는 영향 연구

This study investigated the factors influencing hospitality property prices in the context of COVID-19. Previous research primarily focused on luxury hotels, and it lacks a comprehensive analysis of the entire hospitality sector. The study analyzed transaction data of 586 hospitality properties nationwide from 2018 to 2021. We model all the regional economic, physical, locational, and market characteristics. We use pooled OLS specification with all the interacting variables with COVID-19 period (2020 and 2021) dummy variables.
 We found that both the residential areas dummy and annual growth of the apartment price index showed positive signs during the COVID-19 period. This study shows that hospitality properties were sold during the period as a residential alternative, such as conversion to officetel.

Down payment requirements and house prices: Quasi‐experiment evidence from Shanghai

AbstractUsing the regression discontinuity design, a quasi‐experiment approach, this paper establishes a causal relationship between the down payment requirement and house prices by exploiting a unique institutional background in Shanghai. In the unique setting, the required minimal down payment ratio jumps at the Inner Ring, a circular elevated highway, from 50% to 70% for a large group of buyers. With transaction level data from the largest real estate broker in Shanghai, we find that a lower required down payment ratio increases the apartment price by 138.8 thousand RMB, around 3.71% of the average transaction price.

Green Space and Apartment Prices: Exploring the Effects of the Green Space Ratio and Visual Greenery

Urban green spaces provide various social, economic, health, aesthetic, environmental, and ecological benefits. This study aimed to investigate the influence of green spaces on apartment prices, with a particular emphasis on visual greenery and the proportion of green spaces. Hedonic pricing models have often been used to assess the impact of green spaces on housing prices. Herein, 16 variables were considered as factors affecting housing prices and divided into housing, neighborhood, and green space characteristics. The findings indicate that the presence of green spaces enhanced the value of apartment complexes. Moreover, both visual greenery and the proportion of green spaces within apartment complexes influenced housing prices. Additional analysis demonstrated the impact of green space characteristics within Seoul apartment complexes on housing price changes from 2016 to 2022, finding that higher green space proportions and visual greenery led to approximately 20% higher price increases, and structural equation modeling revealed that the proportion of green spaces within apartment complexes, directly and indirectly, influenced housing prices through visual greenery. Overall, this study emphasized the importance of ensuring well-managed green spaces within and around apartment complexes.

재건축부담금 산정 방식의 개선을 위한 합리적 방안에 관한 연구

The purpose of this study is to examine how excessive the reconstruction levy is in the reconstruction maintenance project, what the cause of such excessive charge is calculated, and to study the solution. The development levy and reconstruction levy of the development levy system, which became the basis of the reconstruction levy system, are very similar in purpose, nature, and calculation method, but the difference in actual burden is excessive, raising concerns about equity and balance. In this regard, this study examines the contents of unreasonable charges through literature analysis and case analysis of reconstruction project areas in Seoul and Gyeonggi-do, where reconstruction charges are scheduled to be imposed recently, and proposes solutions from various aspects. The reconstruction levy is generally high regardless of regional factors, such as Seocho, Gangnam, and non-Gangnam areas, complex size, and apartment price level. It needs to be rationally improved. In order to reasonably calculate the reconstruction levy, various measures were proposed to ensure objectivity and fairness by matching the calculation standards and market price reflection rate of the housing price at the start and end of the reconstruction, and by calculating through a specialized institution. In the current real estate market, which is suffering from serious side effects due to the lack of supply of newly built apartments, we hope to achieve the goals of stabilizing real estate prices, sound development of the national economy, and social integration through institutional improvements.

서울시 지역별 주택가격 결정요인

This study conducted empirical analysis using the hedonic price model to examine regional differences in the determinants of housing prices among the representative examples of decoupling phenomena in inter-regional housing price movements, namely, “Gangnam 3-gu”, “Nodogang”, and “Mayongseong”, from 2017 to 2022.
 In the analysis results, the Gangnam 3-gu emerged as the region with the highest housing prices. It was observed that the longer the elapsed years, the more the anticipation of reconstruction was reflected in housing prices for older apartments. Additionally, a strong preference for convenient living facilities and an excellent educational environment was evident, indicating a dual focus on residential convenience and investment purposes.
 On the other hand, the Nodogang region had the lowest housing prices. The elapsed years were found to be significant only beyond a certain generational threshold. Preference for living convenience facilities showed contrasting results compared to the Gangnam 3-gu. Furthermore, the importance of proximity to subway stations was highlighted, indicating anticipation for reconstruction alongside the significance of convenient public transportation in this region.
 The Mayongseong region fell into the category of higher housing prices. It consisted relatively of newly constructed apartments, and the preference leaned more towards a pleasant residential environment rather than anticipation for reconstruction. This area exhibited a lower connectivity to subway stations, and there was a preference for a close proximity to local markets. In terms of pursuing residential convenience, it displayed similarities to the Gangnam 3-gu.

Determinants of apartment price volatility in Vietnam: a comparison between Hanoi and Ho Chi Minh City

Purpose This paper aims to explore factors influencing apartment price volatility in the two biggest cities in Vietnam, Hanoi and Ho Chi Minh City. Design/methodology/approach The study uses the supply and demand approach and provides a literature review of previous studies to develop four main hypotheses using four determinants of apartment price volatility in Vietnam: gross domestic product (GDP), inflation rate, lending interest rate and construction cost. Subsequently, the Vector Error Correction Model (VECM) is used to analyze a monthly data sample of 117. Findings The research highlights the important role of construction costs in apartment price volatility in the two largest cities. Moreover, there are significant differences in how all four determinants affect apartment price volatility in the two cities. In addition, there is a long-run relationship between the determinants and apartment price volatility in both Hanoi and Ho Chi Minh City. Research limitations/implications Limitations related to data transparency of the real estate industry in Vietnam lead to three main limitations of this paper, including: this paper only collects a sample of 117 valid monthly observations; apartment price volatility is calculated by changes in the apartment price index instead of apartment price standard deviation; and this paper is limited by only four determinants, those being GDP, inflation rate, lending interest rate and construction cost. Practical implications The study provides evidence of differences in how the above determinants affect apartment price volatility in Hanoi and Ho Chi Minh City, which helps investors and policymakers to make informed decisions relating to the real estate market in the two biggest cities in Vietnam. Social implications This paper makes several recommendations to policymakers and investors in Vietnam to ensure a stable real estate market, contributing to the stability of the national economy. Originality/value This paper provides a new approach using VECM to analyze both long-run and short-run relationships between macroeconomic and sectoral independent variables and apartment price volatility in the two biggest cities in Vietnam.

Prediction of Apartment Price Considering Socio Economic and Crime Rates Factors in DKI Jakarta

Investing in real estate properties in Indonesia is highly lucrative due to their consistent appreciation in value. Amongst the various property types, apartments are particularly favored for investment in limited land space. However, determining the value of apartments is often subjective and lacks quantitative measures. To address this issue, this study develops prediction models to predict rental prices and asset value based on apartment specifications, socio-economic factors, and crime rates. Machine learning models employed include Random Forest, Decision Tree, and Gradient Boosting Machine. The findings show Gradient Boosting Machine exhibits the highest accuracy in predicting apartment rental and sale prices, achieving R² values of 0.9230 and 0.8460, respectively. The study also highlights the significant influence of socio-economic factors and crime rates on the performance of the models, contributing between 0.09 and 0.22 with an average of 0.14, as indicated by the improved R² values. This study demonstrate that these models can be valuable tools for real estate investors and professionals seeking quantitative measures to determine the value of apartments. By incorporating apartment specifications, socio-economic factors, and crime rates, the models can provide objective insights into the potential rental income and asset value of apartments.

A Study on the Machine Learning-Based Apartment Price Index

A Study on the Machine Learning-Based Apartment Price Index

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.

What Influenced Hanoi’s Apartment Price Bubble between 2010 and 2021?

This study focused on testing the existence of an apartment price bubble in Hanoi (Vietnam) and on determining the factors that affected it in the period between 2010 and 2021. Using the fundamental factor approach, the authors applied VAR regression using time series data. Specifically, we used the ADF unit test to test the stationarity of the variables based on the following criteria: AIC (Akaike information criterion); LR (likelihood ratio); FPE (final prediction error); HQ (Hanan–Quinn information criterion); and Schwarz (SC) to find the optimal lag (Lag) for the model. We also applied the Granger causality test to determine the correlation between the economic variables that appeared in the model with the PR index. We present the results of the research model through the push–response function and the variance decomposition to consider and evaluate the impact of the PR index shock on itself and the other variables. The literature in this field includes many studies that are similar to this one; however, no research has been conducted that has focused on analysing whether variables, such as per capita income and the urbanisation rate, influence the formation of real estate bubbles. This focus is especially relevant in Hanoi, which is an important part of the Vietnamese real estate market. Through this study, we aimed to fill this gap and to contribute to the references on the Hanoi real estate market and its influencing factors.

Determination of house and apartment prices in the Juiz De Fora region, Minas Gerais, through intelligent algorithms

Estate agents value properties, suggesting a price based on their experience and market data, whereas engineers evaluate properties, performing calculations, structural assessments, and property conservation. Nevertheless, specialisedtechnology combining the technical expertise of both professionals can be created to define a valuation range for a specific property. This study presents the application of intelligent classification algorithms, namely, k-nearest neighbour (kNN), artificial neural network multilayer perceptron (MLP), and support vector machine (SVM) algorithms, and teaching-learning-based optimisation (TLBO) as the input selector. The algorithm developed in this study can be used in three neighbourhoods of the city of Juiz de Fora, Minas Gerais state, Brazil, to classify the price of a property in ranges varying from A to F with 75-millisecond classification speed and 82% accuracy. The system can serve to guide and assist estate agents and engineers in their assessment, thus facilitating the work of these professionals by incorporating the techniques of both into its structure since the study was carried out with real properties

Land and housing price increases due to metro effect: An empirical analysis of Santiago, Chile, 2008–2019

This research investigates whether properties near metro lines in Santiago, Chile, are sold for higher prices. To achieve this, a multiple regression analysis is carried out, considering fixed and temporal effects, comparing data within and outside a 1 km buffer zone around each metro station. The study focuses on three variables: (1) capitalization by landowners, (2) capitalization by apartment prices, and (3) a "land incidence" ratio, which is the proportion of land cost-to-apartment prices and acts as a proxy for residential density. The results indicate a positive correlation between metro proximity and capitalization by landowners and apartment prices, at 5.1% and 9.2%, respectively. However, the land incidence shows a negative correlation of − 11.8%, which suggests that apartment prices and residential densities increase more than land costs near metro stations. An approximate estimation also finds 23.5% higher premium to developers within the metro buffer zone, based on apartment prices, density, land cost, and average building cost. These findings support previous studies that show the positive impact of the metro on land and housing values. The study sees the influence of the metro in transforming Santiago's residential densities and private profits.

서울 동남권과 경기 남부지역 신도시 아파트 가격변동 특성 비교 연구

Recognizing the importance of the housing market in the Seoul metropolitan area, this study conducted a time series study based on the theory of the housing submarket in the southern part of Gyeonggi-do apartment markets in the metropolitan area in terms of diversity in research. The main research results derived from the empirical analysis results are as follows. First, through the Grandeur causality test, the sale price of apartments in the southeastern part of Seoul was confirmed as the cause variable, and the sale price of apartments in Bundang, Pangyo, Gwanggyo, and Dongtan was confirmed as the result variable, and the causal relationship existed. Second, through the results of shock response analysis and variance decomposition analysis, it was found that apartment sales prices in Bundang, Pangyo, Gwanggyo, and Dongtan were greatly affected by the southeastern region of Seoul. Third, the results of impact response analysis and distributed decomposition analysis showed that the four regions of Bundang, Pangyo, Gwanggyo, and Dongtan had a mutual influence relationship. The research results showed how the housing market in the Seoul metropolitan area changed until 2022 after the long-term recession after the global financial crisis in late 2008, and the significance of the study was found in that it confirmed the relationship between regions in southern Gyeonggi Province.

신축 아파트 가격 결정요인

A study analyzed the impact of housing characteristics on apartment prices in the Gangnam districts of Seoul using transaction data from 2018-2022. The study found that certain housing characteristics such as the number of floors, reputation of construction companies, number of parking spaces, and elapsed years had a positive effect on price. On the other hand, factors like the number of households, dedicated area size, and floor area ratio had a negative effect. Apartments with a view of the Han River had the highest price premium among environmental characteristics. Additionally, the transaction year had a strong positive effect on price, indicating a continuous increase in apartment prices during the studied period.

Analysis of the Degree of Reflection of the Apartment Sale Price and Publicly Announced Apartment Price for the Value of the Han River View

Analysis of the Degree of Reflection of the Apartment Sale Price and Publicly Announced Apartment Price for the Value of the Han River View

The Effect of Public Rental Housing on the Price of Neighborhood Apartments - Focusing on LH Happy House in Seoul-

The Effect of Public Rental Housing on the Price of Neighborhood Apartments - Focusing on LH Happy House in Seoul-

PARTISANSHIP, REGULATION, AND VOLATILITY IN SOUTH KOREA'S HOUSING MARKET

South Korea's mortgage loan policy, which represents a regulatory policy for stabilizing the housing market, has changed significantly in accordance with changes in presidency and partisanship. Which partisan government's housing policy has been most effective in stabilizing the market? To answer this question, we discuss the different partisanships of Korea's conservative and progressive parties, their policy preferences, and the previous governments’ key housing policies. Thereafter, we concretize the research methodology and examine the housing volatility witnessed since 1987, using the Markov‐switching regression. The results reveal that the market was highly unstable when the progressive government actively promoted regulation in favor of “governmentalist” partisanship. Moreover, the policy was mostly effective in lowering apartment prices in Seoul, which was the regulation's primary target all along. Based on these findings, we conclude with some policy implications of the study. Given that governmentalist partisanship heightens volatility in the housing market, housing market policies should be designed to hedge the negative externalities of partisanship.

Does district heating affect residential property prices? Case study of an urban area in South Korea

This article investigates whether the residential heating method affects property prices in an urban area of South Korea. Sales data for 1760 apartments in Seocho District, Seoul, the country's capital, are employed. The property price equation is estimated, including the heating method as one attribute. The gap between the price of properties with a district heating system (DHS) and those with other heating methods, such as an individual heating system or central heating system, accounted for about 21.7% of apartment prices. Specifically, apartments with a DHS were more expensive than apartments with other heating methods.