Factors Of Housing Price Research Articles

Macroeconomic factors of housing prices in Russia

The main household decisions are related to the consumer and housing market, while housing decisions are related to mortgages. Moreover, both markets are significantly influenced by the macroeconomic environment. The paper aims to study the structural relationships between the macroeconomic environment, consumer and housing markets. How do these aspects relate to each other in Russia? To analyze the associations, the Bayesian vector autoregression methodology with sign restrictions is used. The obtained results show that the housing market strongly depends on fluctuations in the consumer market, whereas the inverse dependence is not so strongly manifested. At the same time, the impact of mortgage shocks and monetary conditions has a greater impact on the volume indicators of the consumer and housing markets than on prices. The dynamics of housing prices is more related to the supply of housing, while consumer prices are related to the ruble exchange rate and supply and demand shocks. The impact e of oil prices on the dynamics of both markets is analyzed. Oil prices have the most significant impact on household consumption, mainly through the demand channel. One way of functioning of this channel are the fluctuations of real disposable incomes in response to fluctuations in oil prices. The constructed model built demonstrates acceptable predictive characteristics, outperfoming the best univariate time series models on certain horizons.

Analysis and Evaluation of Housing Price Factors Using Mathematical Modeling

In recent years, the real estate industry has achieved significant progress, driving the development of related sectors and playing a crucial role in economic growth. However, rapid real estate market expansion has led to challenges, particularly concerning housing prices, which have drawn widespread societal attention. This article explores the theories of housing prices, analyzes factors influencing them, and conducts an empirical investigation of the impact of representative factors on ordinary residential prices. Using regression analysis and the entropy weight method, a mathematical model was developed to examine how various factors affect housing prices.

Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen

This paper aims to analyze the influence mechanism of built environment factors on passenger flow by predicting the passenger flow of Shenzhen rail transit in the morning peak hour. Based on the classification of built environment factors into socio-economic variables, built environment variables, and station characteristics variables, eight lines and one hundred sixty-six stations in Shenzhen Railway Transportation are taken as research objects. Based on the automatic fare collection (AFC) system data and the POI data of AMAP, the multiple regression model (OLS) and the geographically weighted regression (GWR) model based on the least squares method are established, respectively. The results show that the average house price is significantly negatively correlated with passenger flow. The GWR model considering the house price factor has a high prediction accuracy, revealing the spatial characteristics of the built-up environment in the administrative districts of Shenzhen, which has shifted from the industrial structure in the east to the commercial and residential structure in the west. This paper provides a theoretical basis for the synergistic planning of house price regulation and rail transportation in Shenzhen, which helps to develop effective management and planning strategies.

Research on the influencing factors of housing price based on multiple linear regression and random forest

This paper aims to use multiple linear regression model and random forest models to analyze and study the factors affecting the housing price in Boston. The multiple linear regression model describes the relationship between multiple independent variables and one dependent variable through linear equations, and the random forest improves the accuracy and robustness by constructing multiple decision trees and combining their prediction results. To deal with complex nonlinear relationships and high dimensional data. Housing price is an important index to reflect the level and condition of economic and social development of a region, so it is of theoretical value and practical significance to explore its influencing factors and ways and degrees. Multiple factors are selected to analyze the weight and importance of each influencing factor, so as to help the government and decision makers to formulate more accurate policies, promote the stable development of the market, and provide scientific decision-making support for real estate developers, investors and ordinary buyers. In this study, the random forest model based on decision tree was used to clean, select and reduce the acquired housing price data, and to find out the main factors affecting housing price from the perspective of information gain, so as to obtain a relatively complete mathematical model and provide a reference scheme for future research by scholars.

Investigation of Influential Factors of Housing Price

The fluctuation of housing prices and its influencing factors are important research topics in economics. Under the current background of rapid globalization and urbanization, understanding the driving factors of housing prices is not only an important contribution to academic research but also has important guiding significance for policymakers and individual home buyers. This paper explores the key factors affecting housing prices, focusing on the impact of variables such as living space, land space, housing landscape, housing conditions, housing quality, and housing construction year on housing prices. This study uses multiple regression analysis to analyze the US housing price data set. The results show that living space, housing landscape, housing quality, and housing construction year have statistically significant effects on housing prices at a significance level of 1%. This study provides an effective reference to real estate developers, policymakers, and individual home buyers, which can help them understand market demand more deeply, make decisions that are more in line with market trends, and promote the dynamic and functional development of the real estate market.

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 the influencing factors of housing prices in Beijing

Research on the influencing factors of housing prices in Beijing

Research on Influencing Factors of the Real Estate Market in Beijing

As a large-scale real estate industry, the real estate industry plays an important role in regulating the world economy. As a pillar industry in my countrys development, it plays an important role in promoting my country's GDP growth. Consumption and investment demand for real estate also have a significant impact on overall consumer demand. Many scholars at home and abroad have conducted relevant research on real estate demand factors. This article uses annual data from the China Bureau of Statistics and Beijing, selects Beijings GDP, per capita disposable income of all residents, etc. as explanatory variables, and uses the commercial housing sales area sarea as the explained variable. A multiple regression model was established to analyze the influencing factors of Beijing's real estate market demand. The results show that policy, income, population and housing price factors have a significant impact on demand. Among them, GDP, population change, and policy dummy variable factors have a positive impact, while housing prices and per capita income have a negative impact. Based on the conclusion, this article puts forward suggestions for narrowing the income gap, further curbing real estate speculation, maintaining a balance between supply and demand, paying attention to policy regulation, and using precise and powerful policies to regulate the entire industry, which has served as a reference for the development of the real estate industry.

Analyzing the spatial determinants of housing prices in Dammam, Saudi Arabia: an AHP approach

Purpose This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM). Design/methodology/approach The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices. Findings The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18. Originality/value This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.

Study on the China's real interest rate after including housing price factor into CPI.

The Chinese economy has undergone a long-term transition reform, but there is still a planned economy characteristic in the financial sector, which is financial repression. Due to the existence of financial repression, China's actual interest rate level should be lower than the Consumer Price Index (CPI). However, based on official China's interest rates and CPI, over half of the years China's actual interest rate remained higher than CPI by our calculation from 1999 to 2022. This is inconsistent with the financial repression that exists in China, and the main reason is the calculation methods of China's CPI. China's CPI measurement system originated from the planned economy era, which did not fully consider the rise in housing purchase prices, so the current CPI measurement system can be more realistically presented by taking the rise in housing prices into consider. The core idea of this study is to mining relevant official statistical data and calculate the proportion of Chinese residents' expenditure on purchasing houses to their total expenditure. By taking the proportion of house purchases as the weight of house price factor, and taking the proportion of other consumption as the weight of official CPI, the Generalized CPI (GCPI) is formulated. The GCPI is then compared with market interest rates to determine the actual interest rate situation in China over the past 20 years. This study has found that if GCPI is used as a measure, China's real interest rates have been negative for most years since 1999. Chinese residents have suffered the negative effects of financial repression over the past 20 years, and their property income cannot keep up with the actual losses caused by inflation. Therefore, it is believed that China's CPI calculation method should be adjusted to take into account the rise in housing prices, so China's actual inflation level could be more accurately reflected. In view of the above, deepening interest rate marketization reform and expand channels for financial investment are the future development goals of China's financial system.

Research on Influencing Factors of Second-Hand Housing Price in Haidian District, Beijing

The housing issue is an economic issue as well as an issue of people's livelihood, especially in megacities like Beijing, where housing prices in the second-hand housing market fluctuate greatly, so its influencing factors have become the focus of attention. Studying the influencing factors of housing prices in the second-hand housing market can provide reference for the government to formulate appropriate real estate regulation policies and predict the development trend of housing prices in the future. This article collects nearly 3,000 groups of public second-hand housing individual transaction data from the Lianjia website in the domestic second-hand housing market. The time span of the data is from June 2020 to June 2021. After data cleaning and descriptive statistical analysis of the acquired data, we set the total price of the house as the explained variable, and the number of rooms, the area of the house, the decoration, the floor, and the unit price of the house are used as the explanatory variables. We establish a multiple linear regression model, then test and optimize it. The results are as follows: in the second-hand housing market of Haidian District, if the second-hand house has a large construction area, a large number of rooms, a high unit price and has been renovated, the total price of the house will be high.

Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price

Profound insights have been gained into which characteristics determine housing prices. These characteristics reflect two different aspects: those which are correlated with the dwelling itself and those which are correlated with the location and the surrounding area. However, few studies precisely looked at the disparities and heterogeneity in these effects across neighborhoods with varied conditions. Also, there lacks studies focusing on the moderate-density cases where housing markets have drawn concerns recently. This study aims to fill this research gap by analyzing these disparities across neighborhoods with different economic and racial/ethnic conditions. Through machine learning approaches, we compare the disparities in the impacts of housing status, public transit services, and surrounding environment factors under seven conditions. Results indicate that the heterogeneity in economic conditions could be more significant than racial/ethnical conditions. Through comparison analysis, we call policymakers to need to adopt differentiated perspectives on housing price analysis, and future studies should consider the disparities in the impacts across neighborhoods.

Analysis On the Influencing Factors of Housing Price in China

In order to recognize and analyze the influencing factors of housing price, based on the previous literature, this paper mainly examined 3 major factors: Supply Factors, Demand factors, and Government Policy Intervention in Chinese Housing system. It was indicated that, for supply factors, development investment in the real estate market has a considerable impact on the supply and price of real estate. While for the Demand respect, we further divided it into two factors including population and level of urbanization. The results showed that although total population has no clear relation with the housing price, population aging was a positive relation with the real estate. Also, the level of urbanization only has positive relations with some parts of China. Government policy intervention is unique in China, therefore, the results of this paper could provide some useful information for the investors and governors in analyzing Monetary policy, Housing credit system, Land system, and Taxation system. Generally speaking, our paper generates a better reasoning of Chinese Housing Price.

Discussion on the Influencing Factors of Housing Prices in Shandong Province

Aiming at the influencing factors of commercial housing prices in Anhui, based on the relevant data of the 2000~2020 Shandong Statistical Yearbook, three explanatory variables of actual GDP, actual per capita disposable income of urban residents and total population were selected, and a multiple linear regression model was established by Eviews software, and econometric test methods such as multicollinearity, heteroscedasticity and autocorrelation were tested and the model was modified. The results show that the per capita disposable income and total number of urban residents have a positive impact on the price of commercial housing in Shandong Province. Based on the research conclusions, it is proposed to provide a reference for the stable development of the commercial real estate industry in Shandong Province.

Real estate price forecasting based on fundamental analysis

Real estate economy is an important pillar of China's economy, where the real estate prices are the key factors influencing the economic development of real estate. Based on the analysis of real estate price changes, this study digs into the elements of real estate economic fluctuations and predict the factors of housing prices. Based on the evaluations, it will have a positive role for the government, enterprises, individuals to formulate regulation measures, adjust the investment strategy. According to the analysis, real estate prices have varying dramatically owing to various factors, e.g., policies, population and family size, consumer psychological needs, war, social and environmental factors. It is demonstrated by estimation that the prices of real estate can be controlled in a reasonable range by making scientific and reasonable real estate market regulation policy, so as to ensure the security of real estate finance. These results shed light on guiding further exploration of real restate regulation.

A bibliometric analysis of six decades of academic research on housing prices

PurposeThis paper aims to provide a comprehensive bibliometric study of housing prices according to the articles collected by the Web of Science (WOS).Design/methodology/approachThis paper studies 4,125 research papers on housing prices in the core collection database of WOS. Using VOSviewer, this paper makes a bibliometric and visual analysis of the housing prices research from 1960 to 2020 and probes into the housing prices research from five aspects: time, international cooperation, institutions author cooperation and research focuses.FindingsKeywords such as influencing factors of housing prices, analysis of supply and demand, policy and housing prices and regional cities appear frequently, which indicates the main direction of housing price research literature. Recent common keywords include regression analysis and house price forecast. Countries, like the USA started early in the study of housing prices, and the means and methods in the field of housing price research are mature, leading the forefront of housing price research. Compared with the USA and other Western developed countries, the housing price research in developing countries needs to use innovative research methods and put more effort on sustainability. Research shows that housing price is closely related to economy, and keyword cluster analysis shows that gross domestic product, interest rate, currency and other keywords related to economy are of high-frequency.Research limitations/implicationsThis paper only uses articles from one database (WOS), which does not represent all research papers published worldwide. Some studies have been published for a long time, and the reference value to the research focuses and future research might be limited. There are many kinds of journals included in the study with different publishing frequencies, time ranges and numbers of papers. These may have some influence on the research results.Originality/valueThe main theoretical contribution of this paper is to supplement the current academic research on housing prices. This paper reveals the key points of housing prices research and possible research problems that need attention. We can know from the future research direction and practice which can offer insights for future innovative direction.

An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices

Accurately forecasting housing prices will enable investors to attain profits, and it can provide information to stakeholders that housing prices in the community are falling, stabilizing, or rising. Previous studies on housing price forecasting mostly used hedonic pricing and weighted regression methods, which led to the lack of consideration of the nonlinear relationship model and its explanatory power. Furthermore, the attribute data of housing price forecasts are a heterogeneous study, and they are difficult to forecast accurately. Therefore, this study proposes an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices; that is, this study proposes an EW-KSOM algorithm to cluster the collected data and then applies random forest, extra tree, multilayer perception, and support vector regression to forecast the house prices of full, district, and apartment complex data. In the experimental comparison, we compare the performance of the proposed enhanced weighted kernel self-organizing map with the listing clustering methods. The results show that the best forecast algorithm is the combined EW-KSOM and random forest under the root mean square error and root-relative square error, and the proposed method can effectively improve the forecast capability of housing prices and understand the influencing factors of housing prices in full and important districts. Furthermore, we obtain that the top five key factors influencing house prices are transferred land area, house age, building transfer total area, population percentage, and the total number of floors. Lastly, the research results can provide references for investors and related organizations.

Evaluation Model of Tourism-Driven Real Estate Economic Growth Point Based on AHP

The economy of real estate is intimately connected to people’s quality of life, living standards, and living situations, all of which are extremely important in economic life. In order to better understand the impact of tourism-driven real estate on economic growth, based on AHP, this paper establishes an evaluation model of tourism-driven real estate economic growth. Based on the influencing factors of tourism real estate development, the evaluation model is built in this study of the tourism real estate development model by AHP and determines the optimal model according to the evaluation results. Through the application of the AHP model, the effect of real estate regulation policy is evaluated. The housing prices are affected not only by the influencing factors of housing prices in the pressure indicators, but also by real estate control policies. The model can effectively assess the economic growth point of real estate, propose real estate-specific guiding alternatives for economic growth and development, and ensure that the real estate market develops in a healthy and orderly manner.

Understanding Housing Prices Using Geographic Big Data: A Case Study in Shenzhen

Understanding the spatial pattern of urban house prices and its association with the built environment is of great significance to housing policymaking and urban planning. However, many studies on the influencing factors of urban housing prices conduct qualitative analyses using statistical data and manual survey data. In addition, traditional housing price models are mostly linear models that cannot explain the distribution of housing prices in urban areas. In this paper, we propose using geographic big data and zonal nonlinear feature machine learning models to understand housing prices. First, the housing price influencing factor system is built based on the hedonic pricing model and geographic big data, and it includes commercial development, transportation, infrastructure, location, education, environment, and residents’ consumption level. Second, a spatial exploratory analysis framework for house price data was constructed using Moran’s I tools and geographic detectors. Finally, the XGBoost model is developed to assess the importance of the variables influencing housing prices, and the zonal nonlinear feature model is built to predict housing prices based on spatial exploration results. Taking Shenzhen as an example, this paper explored the distribution law of housing prices, analyzed the influencing factors of housing prices, and compared the different housing price models. The results show that the zonal nonlinear feature model has higher accuracy than the linear model and the global model.

Empirical study on the influencing factors of housing price –––Based on cross-section data of 31 provinces and cities in China

Using Eviews software, this paper makes an empirical analysis on the two kinds of factors that affect the housing price of 31 key cities in China: internal direct factors and external indirect factors, in order to reveal the reasons for the high housing price. A multivariate linear regression model was established in which the explanatory variables were land purchase price and residents’ disposable income, and the explanatory variables were the average selling price of residential commercial housing. The research shows that the land purchase price has a greater positive impact on the average selling price of residential commercial housing, compared with residents’ disposable income although also a positive impact but less impact.