Predicting the Real Estate Price Index Using Machine Learning Methods and Time Series Analysis Model

2018 ◽  
Vol 26 (1) ◽  
pp. 107-133 ◽  
Author(s):  
Seong-Wan Bae ◽  
◽  
Jung-Suk Yu ◽  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Estate ◽  
Time Series Analysis ◽  
Price Index ◽  
Analysis Model ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Machine Learning Methods

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.

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.

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 EXPLORING THE IMPACT OF REAL ESTATE POLICY ON REAL ESTATE TRADING USING THE TIME SERIES ANALYSIS

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1281-1287
Author(s):  
S. W. Shao ◽  
X. Huang ◽  
L. X. Xiao ◽  
H. Liu
Keyword(s):  
Time Series ◽  
Real Estate ◽  
Time Series Analysis ◽  
Housing Prices ◽  
The Real ◽  
The Real Estate ◽  
Series Analysis ◽  
Registration Data ◽  
The People ◽  
The Impact

Abstract. Housing price is a major issue affecting people's lives, but also closely related to the interests of the people themselves. Housing prices are affected by various factors, such as economic factors, population size factors, social factors, national policy factors, the internal factors of real estate and environmental factors. With the deepening of urbanization and the agglomeration of urban population in China, housing prices have been further accelerated. The Chinese government has also introduced a series of policies to limit real estate transactions and affect property prices. This paper also aims to explore a time series analysis method to analyse the impact of real estate policies on real estate prices. Firstly, the article searches for policy factors related to real estate through government official channels such as state, Prefecture and city, and analyses key words related to policy by means of natural language processing. Then, the real estate registration volume, transaction volume and transaction house price data which are arranged into time series are modelled using ARIMA time series model, and the data are processed according to scatter plot, autocorrelation function and partial autocorrelation function graph of the model to identify its stationarity. Finally, the LPPL (logarithmic periodic power) model and MPGA (multi-population genetic algorithm) are used to fit and detect turning points of real estate registration data, and the time series detection algorithm is used to obtain the inflection time nodes of the sequence, and then the relationship between real estate policy and real estate transactions is analysed. Taking the real estate registration data in Wuhan as an example, this paper validates the above time series analysis method. The results show that some real estate policies (such as purchase restriction policy, public rental policy, etc.) have a certain impact on real estate transactions in a short time. Part of the real estate policy (such as graduate security, settlement policy, etc.) does not have a significant impact on real estate transactions. To sum up, the government's brutal blockade of macro-control of the housing market cannot fundamentally solve the housing difficulties of the people, but also standardize the real estate market trading mechanism, innovate the market trading mode, so as to promote the long-term development of the housing market.

The real estate industry in Turkey: a time series analysis

2018 ◽  
pp. 1-13 ◽  
Author(s):  
Dervis Kirikkaleli ◽  
Seyed Alireza Athari ◽  
Hasan Murat Ertugrul
Keyword(s):  
Time Series ◽  
Real Estate ◽  
Time Series Analysis ◽  
Real Estate Industry ◽  
The Real ◽  
The Real Estate ◽  
Series Analysis

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 Machine Learning Methods for Improved Understanding of a Pumping Test in Heterogeneous Aquifers

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1342 ◽  
Author(s):  
Yong Fan ◽  
Litang Hu ◽  
Hongliang Wang ◽  
Xin Liu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Analytical Solutions ◽  
Pumping Test ◽  
Learning Methods ◽  
Pumping Tests ◽  
Machine Learning Methods ◽  
Series Analysis ◽  
Pumping Wells

Pumping tests are very important means for investigating aquifer properties; however, interpreting the data using common analytical solutions become invalid in complex aquifer systems. The paper aims to explore the potential of machine learning methods in retrieving the pumping tests information in a field site in the Democratic Republic of Congo. A newly planned mining site with a pumping test of three pumping wells and 28 observation wells over one month was chosen to analyze the significance of machine learning methods in the pumping test analysis. Widely used machine learning methods, including correlation, cluster, time-series analysis, artificial neural network (ANN), support vector machine (SVR), random forest (RF) method, and linear regression, are all used in this study. Correlation and cluster analyses among wells provide visual pictures of possible hydraulic connections. The pathway with the best permeability ranges from the depth of 250 m to 350 m. Time-series analysis perfectly captured changes of drawdowns within the three pumping wells. The RF method is found to have the higher accuracy and the lower sensitivity to model parameters than ANN and SVR methods. The coupling of the linear regressive model and analytical solutions is applied to estimate hydraulic conductivities. The results found that ML methods can significantly and effectively improve our understanding of pumping tests by revealing inherent information hidden in those tests.

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