House Prices Research Articles

Determinants of Home Ownership Among the Urbanites in Melaka

Over the past few years, the real estate market has been unbalanced in terms of supply and demand when supply exceeds demand, especially in urban areas. Although there is a high demand for affordable housing, the issue of house dumping is still a public debate. The difficulty of owning a house has always been a public issue debated by the government and the private sector in finding a solution for all stakeholders. With the current economic situation, buying a house is almost impossible due to the drastic increase in house prices in urban areas. In reality, urban communities need affordable housing to overcome the problem of renting a house, where the cost of renting is increasing. This study aims to identify whether factors such as market price, socio-economics, and community well-being affect home ownership, especially in Melaka urban areas. Data and information were obtained from house renters in some selected housing estates. The data collected from questionnaires was analysed using Statistical Package for Social Science (SPSS) 23 and Microsoft Excel software. The regression analysis method was employed for this study. The study results found that the increase in house prices affects the residents' ownership in the city, even though the demand for houses is always there. Design factors, types, and comfortable spaces are still the choices of today's home buyers. Socio-economic development factors also affect the demand of urban communities that choose housing estates near the city because of easy access, available infrastructure, and stunning green surroundings. The community well-being factor is a determining factor in the urban community's choice of a community-friendly environment and guaranteed security as one of the factors of home ownership in the future. In general, this study also helps housing developers study marketing strategies by considering the current needs of urban communities, such as market prices, socio-economic development, and a community-friendly housing environment. This study suggests that the government should make more efforts to increase homeownership in the urban community by providing quality housing and creating a united society along with development.

House Price Prediction

House Price Prediction

The Impact of Ukrainian War Refugees on Rental Prices in Europe: A Panel Data Analysis

This study examines the impact of Ukrainian war refugees on actual rental prices in 27 European countries. Using panel data regression analysis for the period 2017Q1-2023Q1, the study found that inflation, house price growth, and interest rates were the primary drivers of rental price growth after February 2022. The study also showed that an inflow of Ukrainian refugees equal to 1% of the host country’s population translated into an increase in rental price growth of 0.2-0.3%, but this effect was not statistically significant at the 5% probability threshold. Auxiliary estimations revealed the statistical significance of Ukrainian migration when adjusting for the stringency of rent controls and size of the rental market.

User Cost of Housing Analysis of the German Real Estate Market

Background: This study looks at the development of the German real estate landscape, which recorded a continuous rise in prices between 2009 and 2022. These were due to a genuine excess demand, a shortage of supply and favorable financing conditions. In the course of 2022, mortgage rates then rose after a historically long period of low interest rates. Aim: This study aims in particular to assess whether the financial advantage of home ownership remains robust. The aim is to determine the relative attractiveness of home ownership compared to renting in all 401 German districts and independent cities. Methods: The owner-occupier costs are determined using the user cost of housing approach and compared with the current rental prices in the respective cities and districts. Various factors are taken into account, including the purchase price, financing costs, maintenance and rental prices. Results: The results of the analysis show that home ownership still offers a considerable cost advantage on average across Germany: In mid-2023, it is around 57% cheaper per square meter to invest than to rent. However, it is important to point out that this result does not take into account differences in the population’s ability to afford home ownership. Furthermore, the analysis raises questions about the sustainability of past house price growth and whether this increase is due to fundamental factors or low mortgage rates. In this sense, the study confirms that home ownership on the German real estate market remains economically favorable compared to renting, even with increased interest rates. It underlines how important it is to consider the long-term benefits for wealth accumulation when deciding between owning and renting. Practical relevance/social implications: The study is of practical relevance for people who rent an apartment or house in Germany and are considering whether it is sensible and economical for them to invest in property. The findings from the analysis are helpful for current purchasing decisions. Originality/value: It also offers original results and provides the reader with added value, as it captures and evaluates current real estate developments on the German market in a well-founded manner.

Comparative Analysis of Multiple – Linear Regression Algorithm with Random Forest Regression for Prediction of House Plot Prices

Humans basically have a basic need to have a place to live, which can be a house or shelter. Along with the rapid population growth in Indonesia, which continues to increase every year, many people do not have or have a decent place to live. Therefore, careful planning is needed so that every family can have a decent home. One very important aspect in planning investment in the form of property is predicting future house prices. One approach that can be used is to use a Random Forest and Multiple Linear Regression algorithm, which is an algorithm from Machine Learning. There are several factors that can influence the price of a house, including land area, building area, number of bedrooms, bathrooms and garage. In this research, multiple linear regression and random forest regression methods were chosen. The aim of this research is to find the best prediction results between the two methods. To achieve accurate predictions, research was carried out repeatedly by dividing the dataset into 80% for training and 20% for testing. The research results show that the random forest regression algorithm provides the best results, with an accuracy of 81.6%.

The Impact of Digitization on Housing Prices: Evidence from China

Issues: The housing prices (HP) in China has been rising over the past 20 years, which is too extremely high to afford for citizens. Also, high housing prices results in various problems, such as increasing financial risks, a shift from physical economy to virtual economy, innovation obstacles, crowding out residents’ consumption, among others. Objective: This study strives to investigate the impact of digitization on HP, with an aim to explore an effective way to tackle high housing prices issue in China. Theoretical Framework: Digitization can essentially effluence the HP through various channels, such as promoting the economic efficiency, improving the infrastructure, affecting the population agglomeration, and increasing the availability for residents to work remotely, etc., whereby it plays a substantial role in curbing the housing prices. Method: With a sample of 279 cities covering the period from 2011 to 2019, a dynamic panel method, General Method of Moment model (GMM) is adopted to run the empirical analysis. Results: The empirical results confirm an inverted U-shaped relationship between the HP and digitization, which indicate that digitization (DIG) acts as an impetus to increase HP at the first stage, but when the digitization surpasses the turning point, it may exsert a powerful force to lower housing prices. Implications: Improving the digitization is among the solutions to curb high housing prices, as long as the digitization exceeds the inflection point. Originality: This study first concludes digitization can be a considerable effluence on housing prices, providing a new insight into solving high housing prices.

Econometric Advances in the Estimation of Housing Price Determinants

This paper aims to help improve the estimations produced by researchers who rely on conventional housing market pricing models to determine housing prices. The widespread use of panel data in estimating housing prices is justified by the richness of cross-sectional regional or metropolitan data analysed over several periods. Unfortunately, panel data has slope coefficient heterogeneity and cross-sectional dependence, producing inconsistent and misleading estimates of the coefficients using the Ordinary Least Squares (OLS) estimator. Recent advances in econometrics address these panel data limitations, producing better estimates. We analysed the empirical application of these new estimators on housing market panel data, showing that the Fully Modified Ordinary Least Squares Augmented Mean Group (FMOLS-MG) estimator produces the best estimates of the long-term housing market equilibrium and that the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator produces the best estimates of the housing market's short-term dynamics. Adopting a trending methodology like Difference-in-Differences (DID) in housing market research to explain the effects of policy decisions on housing prices also has complications related to using the OLS estimator with fixed effects when the data has serial correlation. We show these problems can be overcome using the Feasible Generalised Least Squares estimator in a Seemingly Unrelated Regression Equations (FGLS-SURE) system. Recent econometric developments produce more accurate housing price determinant estimates than conventional econometric methods. These new methodologies can help researchers better estimate the effects of fundamental economic changes and policy decisions on housing prices, which can, in turn, support policymakers in implementing better housing policies.

Assessing Malaysia’s Effectiveness in Alleviating Home Buying Challenges for Young Malaysians

This study analyses the current status of home ownership among young Malaysians and assesses the effectiveness of the scheme in improving housing affordability among young Malaysians. The study examines the target population, income profile, and loan interest rates to provide a comprehensive overview of the current status of home ownership among young Malaysians. The findings show that housing market conditions, especially among the B40 income group, have been deteriorating since 2016, with house prices growing faster than income growth. This has significantly reduced housing affordability, making it difficult for many to realize their dream of home ownership. The continued growth in money supply and volatility in the overnight policy rate have further exacerbated affordability. Although the MFHS has provided substantial support by offering loans with no down payment of up to 110%, rising house prices and interest rates have undermined its effectiveness. Policy recommendations include adopting a long-term housing policy framework that takes into account demographic and economic trends, establishing a strong monitoring and evaluation mechanism, and adopting flexible loan terms. It is also recommended to work with private sector stakeholders to improve housing accessibility and affordability through innovative financing solutions and increasing the supply of affordable housing.

Determinants of housing prices: Serbian Cities’ perspective

This study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the inflation variable and housing prices, underlining real estate’s potential as an inflation hedge. Public service provision and infrastructural amenities also emerge as contributors to higher housing prices in urban areas, emphasizing the importance of comprehensive urban planning strategies. Our study contributes to the literature by providing specific quantitative evidence, advancing the understanding of urban housing market dynamics in developing countries. By offering nuanced insights into determinants of housing prices, our research informs policymakers and urban planners seeking to foster equitable and sustainable urban development strategies.

Optimal Portfolios for Large Investors in Housing Markets Under Stress Scenarios: A Worst-Case Approach

AbstractThe study focuses on constructing a mathematical housing market threatened by a major catastrophic event or crash. It incorporates the worst-case scenario portfolio optimization problem as introduced in Korn and Wilmott (Int J Theor Appl Finance 5(02):171–187, 2002) into housing markets. The standard stochastic models for housing markets assume a geometric Brownian motion and neglect sudden housing price falls during crash times. However, the size, timing, and frequency of crashes have to be included in such models. By incorporating the worst-case portfolio optimization problem into housing markets, this study introduces a methodology to construct portfolios for large investors that are robust and resilient to extreme housing market conditions. The worst-case portfolio optimization approach can be used in housing markets to incorporate stress scenarios, minimize potential losses, utilize mathematical techniques, and manage housing investment risk effectively. This study provides valuable insights for large investors seeking to construct housing portfolios prioritizing downside protection and minimizing losses in extreme housing market conditions. Utilizing numerical illustrations, it provides insights into portfolio construction, demonstrating the effectiveness of adjusting portfolios to mitigate downside risks during housing market crises. The results highlight dynamic portfolio management’s significance in safeguarding wealth when housing prices undergo significant fluctuations.

Recent progress in hukou reform and labor market integration in China: 1996–2022

To investigate the household registration system (hukou) reform in China, it is important to quantify the thresholds of obtaining hukou in urban areas at the city level. We build a new index to measure the likelihood of obtaining urban hukou for migrants in each destination city, termed the “settlement probability”, based on individual information of floating population from the population census and hukou policies of 332 cities from 1996 to 2022. We find that the average settlement probability has been steadily increasing since 1999, reaching 91% in 2022, and 15 provinces have achieved low thresholds. We further build a simple general spatial equilibrium model with the factor of settlement probability to investigate the impacts of hukou reform. The empirical analysis shows there is a mismatch between settlement probability and settlement intentions. Moreover, we find that higher settlement probability is significantly correlated with more migrants' inflow, lower migrants' wages and higher urban housing prices, especially in more developed cities. Therefore, hukou reform is crucial for enhancing the integration of China's labor market.

Multilevel modelling, location and property valuations: an application to the Italian residential market

PurposeThe use of statistical methods in the field of real estate appraisals presents a trade-off between the efficiency of the estimates (that would require the use of sophisticate econometric models) and the ease of the economic interpretation of the outcomes (that characterises the hedonic pricing models). This paper shows that multilevel modelling (MLM) can represent a suitable solution to this trade-off.Design/methodology/approachThis paper uses the so-called “multilevel modelling” (henceforth MLM). MLM can represent a further step forward in the use of statistical methods in property appraisals. MLM is easy to implement and the MLM estimates have a clear economic meaning. Furthermore, MLM provides more efficient estimates of hedonic prices (the prices of housing attributes) with respect to standard hedonic pricing models. Finally, MLM is particularly suitable for the housing market analysis, where the feature “location” plays a key role.FindingsFor the Italian context, characterised by many “benchmark locations” (small municipalities that share similar geographic, historic, and socioeconomic characteristics), the paper finds that multilevel modelling (MLM) is needed to correctly estimate the hedonic prices also in a micro-area.Practical implicationsMLM allows to further enhance the key role of “location”. Location is indeed used as the “grouping variable” in MLM, instead of being treated as a generic housing attribute in hedonic pricing models. When the benchmark locations are many, therefore, MLM represents a very effective compromise between the estimates’ efficiency and the ease of outcomes’ economic interpretation.Originality/valueUnlike the related literature that, basically, use MLM to investigate what are the main determinants (levels) of housing prices, this paper uses MLM to make more efficient the estimation of hedonic prices.

The research on factors influencing housing prices-take Beijing as an example

Housing price is not only related to the economic situation of individuals and families, but also has a profound impact on the whole social economy. For many families, their home is their biggest asset. A rise in house prices can increase household net assets. A fall in house prices could lead to a decline in assets. This paper aims to study the influencing factors of housing prices. The data in this paper take Beijing from May to July 2014 as an example. First of all, the article mentions some papers about housing prices, then the author uses scatter plot and linear regression to fix the model. In the results, it can be seen that sqft-living, sqft-above and view have a relatively high correlation with housing prices. Finally, the specific correlation data table is listed. The final conclusion gives the results of the research in this paper and the authors of the articles mentioned. Housing price is not only related to the economic situation of individuals and families, but also has a profound impact on the whole social economy.

Research on factors influencing housing prices in Beijing

This paper delves into the issue of soaring housing prices in Chinas real estate sector, with a particular focus on Beijing. Employing a multiple linear regression model, it conducts an empirical analysis to identify the factors influencing housing prices. Furthermore, it synthesizes scholarly perspectives to offer a comprehensive discussion on these influencing factors. The research highlights Beijings real estate development, residential investment, and residential sales area as pivotal determinants of housing prices. Building upon these findings, the study advocates for the implementation of effective measures such as land policy regulation and restrictions on real estate transactions to guide the real estate market development prudently and maintain housing prices within a rational range. Results show that real estate development residential investment has a significant positive impact on housing prices, while residential sales area has a significant negative impact. Ultimately, the research endeavors to furnish a scientific foundation for policy formulation and assess its impact, thereby fostering the healthy growth of the real estate market, ensuring stable economic progress, and enhancing the well-being of people.

Research on housing prices prediction based on multiple linear regression

With the steady development of social economy, commercial housing, as an important real estate, occupies a large proportion in family assets. According to the China Household Wealth Survey Report (2018) compiled by the Social China Economic Trends Institute, household net worth accounts for 70% of household wealth, including housing prices in Beijing and Shanghai. In higher cities, the proportion is as high as 80%. This paper analyzes the transaction data of about 10,000 second-hand houses in Beijing, constructs a multiple regression model with SPSS software, and obtains the dependent variable (housing price per unit area). The dataset used in this paper is fetched from the Kaggle website (Housing Price in Beijing). The results show that the relationship between the elevator, the floor situation, the decoration method, the administrative division and other independent variables. Also, it is shown that the correlation between the two is significant, so the model can be used. This paper provides reference for the actual transaction of second-hand housing in Beijing.

Testing the relationship between housing prices and inflation in the OECD countries

Testing the relationship between housing prices and inflation in the OECD countries

An adaptive trimming approach to Bayesian additive regression trees

A machine learning technique merging Bayesian method called Bayesian Additive Regression Trees (BART) provides a nonparametric Bayesian approach that further needs improved forecasting accuracy in the presence of outliers, especially when dealing with potential nonlinear relationships and complex interactions among the response and explanatory variables, which poses a major challenge in forecasting. This study proposes an adaptive trimmed regression method using BART, dubbed BART(Atr) to improve forecasting accuracy by identifying suspected outliers effectively and removing these outliers in the analysis. Through extensive simulations across various scenarios, the effectiveness of BART(Atr) is evaluated against three alternative methods: default BART, robust linear modeling with Huber’s loss function, and data-driven robust regression with Huber’s loss function. The simulation results consistently show BART(Atr) outperforming the other three methods. To demonstrate its practical application, BART(Atr) is applied to the well-known Boston Housing Price dataset, a standard regression analysis example. Furthermore, random attack templates are introduced on the dataset to assess BART(Atr)’s performance under such conditions.

Utilizing 31 Chinese province panel data models to investigate the factors influencing house prices

In the realm of data analysis, this research employs regression models to delve into the complexities of housing market dynamics. The construction sector of real estate development companies, the amount invested in real estate development, and the gross regional product have all been found to be important determinants of home prices. Interestingly, the most significant factor is the investment in real estate development, which has a significant impact on house prices. The analysis reveals a positive correlation between the gross regional product and investment amount in real estate development with housing prices, suggesting that as these economic indicators rise, so too do housing prices. Conversely, the author observes a negative correlation between the construction area of real estate development enterprises and housing prices, indicating that an increase in construction area is associated with a decrease in housing prices. These findings underscore the importance of considering these key factors when analyzing housing market trends, providing valuable insights for policymakers, investors, and researchers alike. By understanding these relationships, stakeholders can make more informed decisions to navigate the ever-evolving housing market landscape.

The research on factors influencing house value-take California as an example

Housing price is a popular and important topic in todays society. This article aims to find the factors that have impacts on the housing price. To find the relationships between factors, this article uses Multiple Linear Regression as the method to perform a significant analysis of factors. 1000 samples of Californias block groups in 1990 are selected for this research. Based on the assumption, this research chooses 8 explanatory variables for the analysis. Because of the relationships between explanatory variables, the article also adds interaction terms between latitude and longitude, and population and total bedrooms to solve the multicollinearity problem among explanatory variables. To optimize model analysis effectiveness, this research compares the significance, VIF value, and GVIF value of explanatory variables. The analysis result shows that the geographical location (Latitude and longitude), the housing median age, the total bedrooms, the population, and the median income make significant impacts on the housing value. Among these factors, the median income is the main factor.

Inheritance dynamics and housing price fluctuations: Evidence from the China household finance survey

The demographic shift towards an older population has a profound impact on the economy, particularly in the real estate sector. The evolving housing demand, shaped by changing family structures and financial circumstances, is influenced by inheritance considerations, which in turn affect investment decisions and market dynamics. This study uses data from the China Household Finance Survey (2013–2019) to explore the impact of aging on housing prices, with a focus on inheritance motives and the influence of male descendants. While ageing tends to decrease prices, inheritance motives, particularly those of male heirs, tend to offset this effect. The geographical distribution of this impact varies, with the level of education also playing a role. It is therefore vital to address demographic shifts and cultural norms in order to maintain stability in the housing market.