The Panel Data Analysis about Determinants Affecting the Real Estate Holding Ratio of Firms

2019 ◽  
Vol 21 (3) ◽  
pp. 1217-1227
Author(s):  
Young Joo Yoon ◽  
Eun Joo Kim ◽  
Jeong Il Kim
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Real Estate ◽  
Panel Data Analysis ◽  
The Real ◽  
The Real Estate

scholarly journals The Real Estate Risk Premium:A Developed/Emerging Country Panel Data Analysis

2009 ◽  
Vol 35 (5) ◽  
pp. 118-132 ◽  
Author(s):  
John-John D’Argensio ◽  
Frédéric Laurin
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Real Estate ◽  
Panel Data Analysis ◽  
The Real ◽  
The Real Estate ◽  
Emerging Country

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 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.

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