Financial risk analysis of real estate bubble based on machine learning and factor analysis model

2020 ◽  
pp. 1-12
Author(s):  
Chengyuan Zhang ◽  
Mingliang Li ◽  
Yongqiang Li
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Real Estate ◽  
Financial Risk ◽  
Analysis Model ◽  
The Real Estate ◽  
Real Estate Price ◽  
Price Bubbles ◽  
Real Estate Bubble ◽  
Price Bubble

The regional real estate price bubble regulation policy is an external factor for the real estate industry. The effect of real estate regulation is difficult to determine, which is a typical problem of uncertain system analysis and forecasting, and the gray Bayesian network forecasting model is to solve the forecasting problem of economic system subject to external regulation. Based on machine learning and factor analysis models, this paper constructs a real estate bubble financial risk analysis model based on machine learning and factor analysis models. Moreover, starting from the real estate price bubble, which is a hot and difficult issue of the social economy, this paper discusses the causes of the formation of real estate price bubbles and the mechanism of the formation of real estate price bubbles, looks for the importance of policy regulation of real estate price bubbles, and clarifies the functional game model of policy regulation of real estate price bubbles. In addition, this paper uses examples to study the model constructed in this paper. The results show that the model constructed in this paper has a certain effect.

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 Price Bubble In The Real Estate Market - Behavioral Aspects

2014 ◽  
Vol 22 (1) ◽  
pp. 77-90 ◽  
Author(s):  
Justyna Brzezicka ◽  
Radosław Wisniewski
Keyword(s):  
Real Estate ◽  
Real Estate Market ◽  
Herd Behavior ◽  
Negative Consequences ◽  
The Real ◽  
The Real Estate ◽  
Price Bubbles ◽  
Price Bubble ◽  
The Real Estate Market ◽  
Behavioral Aspects

Abstract The article pertains to the topic of speculative price bubbles which arise in the real estate market. The individual parts of the article deal with the connection between the price bubble in the American real estate market and the global economic crisis, defining the concept of a price bubble with regard to the behaviors of market participants, providing a description of the environment generating price bubbles, and systematizing the reasons behind the formation of price bubbles. The analysis of behavioral aspects accompanying the existence of a price bubble is a key issue. The assumed considerations indicate that the housing price bubble could not exist in the real estate market (REM) if its formation was not accompanied by behavioral aspects. These aspects include, among others, giving in to temptations and emotions, limited rationalism, herd behavior, and seeking to make profits in a short amount of time at the expense of long-term negative consequences. The nature of these deliberations is theoretical.

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 The price bubble morphology

2013 ◽  
Vol 13 (1) ◽  
pp. 76-94 ◽  
Author(s):  
Waldemar Tyc
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Economic System ◽  
Institutional Environment ◽  
Real Estate Market ◽  
Economic Systems ◽  
The Real Estate ◽  
Price Bubbles ◽  
Price Bubble ◽  
The Real Estate Market

Abstract The article presents a discourse on the mechanism by which price bubbles emerge and burst. For idealization purposes the author assumes that even though price bubbles emerge in various markets, their morphology differs from market to market, be it the hi-tech stock (or, more generally, the stock market), the real estate market (where land is of fixed supply) or the housing market. The sources of their diversification lie in the type and weight of the causes of their appearance, the differences between their causative and functional determinants and the market feedbacks. Any interpretation of the nomological diversification of price bubbles (in the sense of their categorisation) requires looking at the system pragmatics and the market in which they emerge. Thus the designations of economic systems and the specifics of markets constitute both the economic and the institutional environment of their origin. They also constitute the necessary context for their understanding and interpretation, as price bubbles rise and collapse within specific functional structures of an economic system.

scholarly journals Evaluation of factors leading to formation of price-bubbles in the real estate market of Lithuania

2015 ◽  
Vol 16 (4) ◽  
pp. 345-352
Author(s):  
Audrius Dzikevičius ◽  
Lukas Kazlauskas ◽  
Šarūnas Bruzgė
Keyword(s):  
Regression Analysis ◽  
Real Estate ◽  
Business Cycles ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Price Bubbles ◽  
Correlation And Regression Analysis ◽  
Price Bubble ◽  
The Real Estate Market

Recently, real estate market has been discussed more frequently in the framework of economic analysis. The global economic crisis of 2008 has demonstrated the severity of financial shock that can be caused by inconsiderate investments in the real estate market. The present article analyses business cycles and the phenomenon of a price-bubble in that context. Drawing on the analysis of reference literature we identify the main reasons that can lead to fluctuations of prices in the real estate market. Finally, drawing on correlation and regression analysis we determine which factors have the strongest influence on the Lithuanian real estate market.

scholarly journals International Real Estate Review

2016 ◽  
Vol 19 (2) ◽  
pp. 249-264
Author(s):  
Steve P. Fraser ◽  
◽  
Marcus T. Allen ◽  
Keyword(s):  
Real Estate ◽  
Income Tax ◽  
Residential Development ◽  
Development Project ◽  
Golf Course ◽  
Golf Club ◽  
Development Projects ◽  
The Real Estate ◽  
Real Estate Price ◽  
Price Premiums

Considerable prior research confirms the existence of real estate price premiums associated with golf course amenities in residential development projects. This study examines a unique residential development project in which membership in a golf club is appurtenant to the real estate: ownership of certain (but not all) dwellings in the project includes deeded membership in the project¡¦s golf club. In this development project, golf memberships can only be obtained or disposed of by acquiring or selling the associated dwelling, respectively. The results of this analysis indicates that price premiums associated with appurtenant golf memberships, after controlling for golf course view and other relevant property characteristics, are significantly positive. Furthermore, the results indicate that the magnitude of the price premium for appurtenant golf memberships varies across dwelling types (detached vs. attached) in this project. These findings may be important for housing developers, consumers, lenders, appraisers, and property and income tax authorities.

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