Prediction of the real estate industry economics based on LSTM model

Zhang, Taoen; Fang, Baojun

doi:10.1088/1742-6596/1802/4/042095

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…During network training, Dropout would set an inactivation probability for neurons on each layer of the network. Based on it, the network automatically eliminates the neurons to simplify the network structure and avoid overfitting [18][19][20][21][22][23]. For the gradient vanishing problem, the BN algorithm is introduced.…”

Section: Basic Structurementioning

confidence: 99%

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Yang

Jia

Liu

2022

Security and Communication Networks

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In view of the financial risks faced by listed enterprises, how to accurately predict the risks is an important work. However, the traditional LSTM financial diagnosis model has the disadvantage of low accuracy; the specific reason is that the LSTM model has the problems of overfitting and gradient disappearance in risk diagnosis. Therefore, Dropout is adopted to solve the overfitting problem in the process of premodel prediction, and the BN algorithm is used to solve the gradient disappearance problem in the process of iteration. In order to verify the feasibility of above improvements, the financial data of China’s A-share listed enterprises from 2017 to 2020 are taken as samples to analyze the financial data of listed enterprises through single-step dimension and multistep dimension. The experimental results show that under the analysis of two dimensions, the financial prediction accuracy of the improved LSTM for T-2∼T-3 years can reach 83.96% and 91.19%, respectively, which indicates that through the above improvements, the model can be improved and has certain reference value.

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Section: Basic Structurementioning

confidence: 99%

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Yang

Jia

Liu

2022

Security and Communication Networks

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“…A joint selfattention mechanism-based deep learning model for predicting house prices was proposed by authors in [12]. In [13] authors developed long short-term memory (LSTM) based model for predicting real estate prices. In [14] author propose a hybrid model for predicting real estate prices using PSO and MLR.…”

Section: Related Workmentioning

confidence: 99%

Recommendation of Regression Models for Real Estate Price Prediction using Multi-Criteria Decision Making

Kumar

2023

JCOMSS

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Accurate prediction of real estate prices is an essential task for establishing real estate policies. Even though various regression models for real estate price prediction have been developed so far, selecting the most suitable regression model is a challenging task since the performance of different regression models varies for different accuracy measures. This paper aims to recommend the most suitable regression model for real estate price prediction, considering various performance measures altogether using multi-criteria decision making (MCDM). The evaluation of regression models involves a number of competing accuracy measures; hence, choosing the best regression model for predicting real estate price is modeled as the MCDM problem in the proposed approach. An experimental study is designed using 22 regression models, three MCDM methods, six performance measures, and three real estate price datasets to validate the proposed approach. Experimental outcomes show that Gradient Boosting, Random Forest, and Ridge Regression are recommended as the best regression models based on MCDM ranking. The results of the experimental study show that the proposed MCDM-based strategy can be utilized effectively in real estate industries to choose the best regression model for predicting real estate prices by optimizing several competing accuracy measures.

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“…American housing loan interest rate is an important factor affecting the real estate market [10]. Zhang and Fang scholars use the time series prediction model to predict and study the real estate price based on multiple factors such as the average annual household income, the average real estate price, and the urban population [11]. The results show that the highest correlation with house prices is family income.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Grid Division and Cluster Analysis of Decision Graph in Real Estate Marketing Using Data Mining

2022

Mobile Information Systems

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China’s economy has been in a state of rapid development in recent years. People’s lifestyles and quality of life have also undergone earth-shaking changes. More people put forward requirements for the living environment and living environment, which promotes the rapid rise of the real estate industry. The state has promulgated several macrocontrol measures. Real estate companies analyze the needs of various customer groups, affordable housing unit prices, and family populations, and formulate real estate marketing strategies to improve real estate sales. Therefore, this paper practices data mining technology to precisely analyze and locate the target customers. The cluster analyzes the mapping of the real estate marketing bureau. The cluster further divides the adaptive network and selects the data of a real estate company in China for research. It also analyzes the relationship between the family population and the affordable housing unit price and comprehensively improves the accuracy of real estate marketing. The results show that the acceptable housing unit price for families in this area is 9800 yuan. Study its data characteristics to judge the relationship between the population of other families. The analysis and research of data mining technology in the context of real estate can formulate more accurate marketing strategies. Moreover, it can reduce the investment amount of real estate companies in marketing.

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Prediction of the real estate industry economics based on LSTM model

Cited by 3 publications

References 3 publications

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Recommendation of Regression Models for Real Estate Price Prediction using Multi-Criteria Decision Making

Adaptive Grid Division and Cluster Analysis of Decision Graph in Real Estate Marketing Using Data Mining

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