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.

Design of Real Estate Price Index System Based on Hedonic Price Method

2014 ◽  
Vol 488-489 ◽  
pp. 1463-1466
Author(s):  
Yun Du ◽  
Hui Qin Sun ◽  
Su Ying Zhang ◽  
Qiang Tian
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Real Estate Market ◽  
Hedonic Price ◽  
Hedonic Price Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Price Series ◽  
Research Level

Urban real estate price index (hereinafter referred to as UREPI) is a basic data of the real estate market, its accuracy is very important for enterprises, consumers and housing management department. In view of current research level here in China and popular models, the UREPI system is compiled based on the Hedonic price method because of its advantages such as calculation simple and sample easily etc. Compiled by Eviews the system has three main stages: the data standardization, the benchmark model establishment and the application of two periods chained update method to update price series. UREPI system is combined with the real deal, so it can be used to analysis the market accurately. The results completely meet the design requirements.

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 Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 616
Author(s):  
Sang-Hyang Lee ◽  
Jae-Hwan Kim ◽  
Jun-Ho Huh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Real Estate ◽  
Big Data Analysis ◽  
Real Estate Market ◽  
Land Price ◽  
The Real ◽  
The Real Estate ◽  
Land Prices ◽  
The Real Estate Market

In real estate, there are various variables for the forecasting of future land prices, in addition to the macro and micro perspectives used in the current research. Examples of such variables are the economic growth rate, unemployment rate, regional development and important locations, and transportation. Therefore, in this paper, data on real estate and national price fluctuation rates were used to predict the ways in which future land prices will fluctuate, and macro and micro perspective variables were actively utilized in order to conduct land analysis based on Big Data analysis. We sought to understand what kinds of variables directly affect the fluctuation of the land, and to use this for future land price analysis. In addition to the two variables mentioned above, the factor of the landscape was also confirmed to be closely related to the real estate market. Therefore, in order to check the correlation between the landscape and the real estate market, we will examine the factors which change the land price in the landscape district, and then discuss how the landscape and real estate can interact. As a result, re-explaining the previous contents, the future land price is predicted by actively utilizing macro and micro variables in real estate land price prediction. Through this method, we want to increase the accuracy of the real estate market, which is difficult to predict, and we hope that it will be useful in the real estate market in the future.

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.

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