scholarly journals Empirical Testing of the Impact of Gender and Marital Status on the Price and Trend of Urban Real Estate – Evidence from Provincial Panel Data of China

2018 ◽  
Vol 10 (7) ◽  
pp. 38
Author(s):  
Min Tan ◽  
Yajie Bai
Keyword(s):  
Panel Data ◽  
Real Estate ◽  
Marital Status ◽  
Negative Impact ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Empirical Results ◽  
Real Estate Price ◽  
The Impact

This paper investigates the impact of demographic structure, especially gender and marital status, on the price of regional real estate. This paper utilizes controlled-heteroskedasticity fixed-effect model for the empirical tests based on a panel data set of 30 Chinese provinces from 2011 to 2015. Empirical results show that the gender ratio in the provincial panel data does have a significant negative impact on the regional real estate prices, which implies that when the number of women in a region increases, the real estate price in this region tends to rise. The impact of marital status on the real estate price is not significant according to empirical results.

Data Analysis on the Influencing Factors of the Real Estate Price

2021 ◽  
pp. 52-66
Author(s):  
Huang-Mei He ◽  
Yi Chen ◽  
Jia-Ying Xiao ◽  
Xue-Qing Chen ◽  
Zne-Jung Lee
Keyword(s):  
Data Analysis ◽  
Real Estate ◽  
Influencing Factors ◽  
Real Estate Market ◽  
Training Data ◽  
Market Demand ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

scholarly journals Analysis of Urban Residents' Sports Consumption Demand Based on Intercepted Regression Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qing Liu
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Estate Market ◽  
Survey Method ◽  
Real Estate Industry ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Real Estate Prices ◽  
The Impact

At this stage, broadening the consumer market, upgrading the consumption system and gradually establishing a consumption-led development concept are key factors in promoting high-quality economic development. At the same time, China's macro economy is also experiencing another test. The rapid development of China's real estate market in recent years has attracted a large number of investors, and real estate prices have produced irrational and substantial increases. Behind the boom of the real estate market is a social system crisis driven by profiteering and the growing seriousness of real estate financial bubble. So exploring the mechanism of the influence of real estate prices on the upgrading of residents' consumption is important for the current stage of China. Therefore, it is important to investigate the mechanism of real estate price impact on consumer upgrading for the coordinated development of real estate industry and national economy. In this paper, we analyze and examine the theory on the consumption improvement by the literature survey method. We also summarize the present research on the correlation and the influence mechanism of the real estate price and the consumption improvement and choose the index which reflects the present state of the real estate industry and the consumption of the inhabitant. Besides the input indicators that qualitatively manage the impact of housing prices on the improvement of residents' consumption, we first use the descriptive statistics method to understand the level of the Chinese real estate market and improve consumer spending. Based on this, the descriptive statistical method is applied to define the current state of China's real estate market and the level of improvement in consumption, and to define the standard for improving consumption in China. On the other hand, based on the spatial and spatial spillover points of view, we use spatial analysis framework combined with exploratory spatial data analysis and GIS to investigate spatial correlation between consumption structure and housing price, and accurately reflect the spatial clustering status of the index by drawing. Moran dispersion plot and Lisa cluster plot, then the spatial Darwinian model, are used to investigate the impact of real estate prices on the increase in occupant consumption from a macro perspective.

scholarly journals Transformacja w przestrzeni. Wpływ modelu deweloperskiego na urbanizację Polski po 1989 roku

2016 ◽  
pp. 177-192
Author(s):  
Łukasz Drozda
Keyword(s):  
Real Estate ◽  
Negative Impact ◽  
Living Environment ◽  
Housing Shortage ◽  
The Real ◽  
The Real Estate ◽  
Real Socialism ◽  
The Impact ◽  
Housing Estates

The article describes the impact, the meaning and the characteristics of the housing estates developed by the real estate companies in Poland after 1989 and presents possible solutions to eliminate the dysfunctions of the inhabited space, which can improve the functionality of inhabited environment. That model of housing is one of the most typical elements of the settlement network in Poland. Unknown under the real socialism when the housing development was based on public and industrial investment and the so-called socialized housing, in the later period supplemented to a greater extent by individual projects. Real estate housing has negative impact on the quality of the urbanized space and the national economy, which results in the creation of a low quality living environment. On the other hand, to some extent they solve the problem of the housing shortage caused by the ine'cient public policy in that field. The study is based on source literature and o'cial statistics on the level of housing production in the analysed period.

The role of project financing in the development of the real estate funding market in Russia

2021 ◽  
Vol 27 (4) ◽  
pp. 894-912
Author(s):  
Tat'yana A. RUBLEVA
Keyword(s):  
Real Estate ◽  
Systems Approach ◽  
Complex Structure ◽  
Real Estate Market ◽  
Digital Economy ◽  
Project Financing ◽  
The Real ◽  
The Real Estate ◽  
Object Oriented Design ◽  
The Impact

Subject. This article examines the impact of project financing on the development of the real estate funding market in the context of the transition to the digital economy. Objectives. The article aims to define the features of project financing in the property construction and its development prospects in the context of the transition to the digital economy. Methods. For the study, I used comparative and logical analyses, object-oriented design, and the systems approach. Results. The article defines the essence of project financing and its role in the development of the real estate funding market in the transition to the digital economy. It describes a number of features of project financing in construction and compares them with the features of project financing of innovative industrial projects. The article shows how to solve existing problems in this area and offers a use case diagram that helps develop a software product relevant to the real estate funding market. Conclusions and Relevance. The real estate funding market is a complex structure and it includes the synergy of the real estate market, banking market, and the financial market. Project financing is an integral part of the real estate funding market. It stimulates the development of quality consulting services in the market and produces key requirements for the profession of the next generation. The results of the study can be used to improve banking activities in project financing and when creating quality services of consulting companies in the real estate funding market.

scholarly journals Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1421
Author(s):  
Gergo Pinter ◽  
Amir Mosavi ◽  
Imre Felde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Model Performance ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Call Detail Records

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

Study on the Influence Factors of Real Estate Price Based on Regression Model — The Case of Jilin City

2011 ◽  
Vol 117-119 ◽  
pp. 1547-1551
Author(s):  
Xi Li Tan ◽  
Han Zhou ◽  
Ying Song Xu
Keyword(s):  
Real Estate ◽  
Regression Model ◽  
Influence Factors ◽  
Multiple Linear Regression Model ◽  
Factors Affecting ◽  
The Real Estate ◽  
Real Estate Price ◽  
Spss Software ◽  
Countermeasures And Suggestions ◽  
The Impact

Real estate price has been one of the hottest discussion topics, especially in recent years, it becomes the focus of attention. In this paper, we aim to study the impact of economic factors on real estate price. By multiple linear regression model and SPSS software, we analyze four economic indicators affecting the real estate price of Jilin city, and make some amendments and testings, the conclusions show the consumption level and housing construction area are important factors affecting the price trend. On this basis, we further to give the corresponding countermeasures and suggestions.

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