Study of the real estate price wealth effect in Dalian and its policy implication

2015 ◽  
Author(s):  
L. P. Han ◽  
X. Y. Wang ◽  
G. G. Yang
Keyword(s):  
Real Estate ◽  
Policy Implication ◽  
Wealth Effect ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

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 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.

scholarly journals Capital Gain Expectations And Efficiency In The Real Estate Markets

2011 ◽  
Vol 6 (4) ◽  
Author(s):  
Shady Kholdy ◽  
Ahmad Sohrabian
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Housing Prices ◽  
Housing Price ◽  
Capital Gain ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Economic Fundamentals

Capital gain expectation is known to be an important determinant of housing price hikes during the real estate booms. Empirically, however, specifying the way expectations about current and future economic variables are formed is a dilemma. Although it is reasonable to assume that economic fundamentals have a significant effect on the investors’ expectation about future gains, a number of housing market analysts claim that expectations of housing prices are extrapolative. This study attempts to investigate the mechanism by which investors’ capital gain expectations and psychology are shaped. The results suggest that housing prices are predictable with respect to capital gain expectations only when these expectations are formed by extrapolation of past price appreciations. Considering the large number of empirical evidence on housing market anomaly with respect to capital gain expectations, the results suggest that the extrapolative expectations can better explain the real estate price behavior than expectations that are formed by economic fundamentals.

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 Prosperity or Real Estate Bubble? Exuberance Probability Index of Real Housing Prices in Chile

2021 ◽  
Vol 9 (3) ◽  
pp. 51
Author(s):  
Byron J. Idrovo-Aguirre ◽  
Francisco J. Lozano ◽  
Javier E. Contreras-Reyes
Keyword(s):  
Real Estate ◽  
Financial Market ◽  
Housing Prices ◽  
Housing Price ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Mortgage Interest ◽  
Real Estate Bubble ◽  
Probability Index

In this paper, we approached the concept of real estate bubble, analyzing the risk its bursting could generate for the Chilean financial market. Specifically, we analyzed the relationship between real housing prices, the economic activity index, and mortgage interest rates denominated in inflation-linked units from 1994 to 2020. The analysis was based on a second order Markov switching model with the predetermined variables mentioned later, whose parameters were obtained through the expectation–maximization algorithm. Then, we built a probability index as early warning indicator for potential imbalances in the real estate price that could put financial market stability at risk. The indicator is important to evaluate economic policy calibrations in time. A main finding was that the real housing price had a non-linear relationship with economic activity and the mortgage interest rate. Therefore, the evolution of the real estate price has been consistent with fundamental macroeconomic variables, even under a high growth regime, with increases above 12% per year. About 92% of housing price variability derived from changing macrofinancial conditions, suggesting a low margin of speculative behavior.

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.

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