Some Constitutional Law, Competition Law and Economic Psychology Issues Inherent in Some Real Estate Market Mechanisms

Nwogugu, Michael I. C.

doi:10.1007/978-3-030-71415-4_3

Cited by 2 publications

(2 citation statements)

References 106 publications

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“…Lee et al [33] constructed their real estate index prediction model by using three distinct machine learning models, compared their findings, and discovered that the model based on the random forest offered the most accurate forecasts. Using concepts from common law, Nwogugu examined the constitutional law, competition law, and economic psychology that drive the real estate market mechanism [34]. Kang et al [35] constructed a short-term prediction model of apartment prices utilizing machine learning technologies, and then basing it on the frequency with which keywords were searched for in news items.…”

Section: Background Of the Studymentioning

confidence: 99%

Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia

et al. 2022

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The housing market is a crucial economic indicator to which the government must pay special attention because of its impact on the lives of freshly minted city inhabitants. As a guide for government regulation, individual property purchases, third-party evaluation, and understanding how housing prices are distributed geographically may be of great practical use. Therefore, much research has been conducted on how to arrive at a more accurate and efficient way of calculating housing prices in the current market. The goal of this study was to use the artificial neural network (ANN) technique to correctly identify real estate prices. The novelty of the proposed research is to build a prediction model based on ANN for predicting future house prices in Saudi Arabia. The dataset was collected from Aqar in four main Saudi Arabian cities: Riyadh, Jeddah, Dammam, and Al-Khobar. The results showed that the experimental and predicted values were very close. The results of the proposed system were compared with different existing prediction systems, and the developed model achieved high performance. This forecasting system can also help increase investment in the real estate sector. The ANN model could appropriately estimate the housing prices currently available on the market, according to the findings of the assessments of the model. Thus, this study provides a suitable decision support or adaptive suggestion approach for estimating the ideal sales prices of residential properties. This solution is urgently required by both investors and the general population as a whole.

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Section: Background Of the Studymentioning

confidence: 99%

Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia

et al. 2022

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“…Lee et al used three machine learning models to build the real estate index prediction model, compared the performance of the three models, and concluded that the real estate index prediction model based on the random forest has high accuracy [8]. Based on the principles of common law, Nwogugu discussed the constitutional, competition law, and economic psychology inherent in the real estate market mechanism [9]. Kang et al constructed a short-term prediction model of apartment prices based on news article keyword search frequency and machine learning technology and used relevant data for training and testing.…”

Section: Related Workmentioning

confidence: 99%

Research on Prediction and Analysis of Real Estate Market Based on the Multiple Linear Regression Model

Liu

2022

Scientific Programming

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Since the housing reform in the 1990s, China’s real estate market has expanded and developed rapidly. It has quickly become the pillar of China’s national economy and made a great contribution to China’s economic growth. However, China’s real estate market started late and its development is not perfect, and house prices show obvious volatility. Accurate prediction of house prices is conducive to the government issuing appropriate regulatory policies, helping investors formulate correct investment strategies, and guiding the healthy and long-term development of the real estate market. Based on the statistical data of the real estate market, this study analyzes the influencing factors of the real estate price, establishes a multiple linear regression model, uses the least square method to solve the unknown parameters of the model, and finally constructs a house price prediction model to predict and analyze the real estate market. The results show that the maximum error of the real estate price prediction model is no more than 8%. The above results show that the house price prediction model has high accuracy and can predict house prices accurately and efficiently.

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Some Constitutional Law, Competition Law and Economic Psychology Issues Inherent in Some Real Estate Market Mechanisms

Cited by 2 publications

References 106 publications

Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia

Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia

Research on Prediction and Analysis of Real Estate Market Based on the Multiple Linear Regression Model

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