Housing Price Prediction Based on Multiple Linear Regression

Zhang, Qingqi

doi:10.1155/2021/7678931

Cited by 26 publications

(15 citation statements)

References 16 publications

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“…Since the sample companies from 2012 to 2015 are not the same, and the short time is only four years, the four-year sample is aggregated as cross-sectional data for regression analysis. Table 5 shows the multiple regression [24][25][26][27][28][29] results of the impact of carbon information disclosure on corporate value. It can be seen from Table 5 that the overall fitting degree is 0.336, indicating that the selection of explanatory variables in the model is reasonable.…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

Impact of Carbon Information on Enterprise Value: Analysis Model Design Based on Big Data

Kong

Zhao

2022

Scientific Programming

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Investors pay more and more attention to the issue of climate change information disclosure of listed companies. Whether the company can effectively deal with the impact of climate change on its own operations and make reasonable information disclosure will undoubtedly affect the value of the enterprise. Aiming at this, an analysis model based on big data is designed for the impact of carbon information on enterprise value. This study selects the sample stocks of the Shanghai Stock Exchange responsibility index every year as the research sample, counts the carbon information through the social responsibility reports of listed companies, and uses the index method to score the carbon information of each company to measure the quality of carbon information disclosure. Firstly, it analyzes the situation of carbon information disclosure in China through descriptive statistics and then constructs relevant models to conduct an empirical analysis on the impact of carbon information disclosure on enterprise value.

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Section: Multiple Linear Regression Analysismentioning

confidence: 99%

Impact of Carbon Information on Enterprise Value: Analysis Model Design Based on Big Data

Kong

Zhao

2022

Scientific Programming

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“…The study showed that the price has a strong linear correlation with GDP, the total population in a certain area and some other factors. So, the conclusion was that it is reliable to predict the house price by linear regression model [5]. Dang and Yang studied the house prices in Tangshan and divided the factors into demand factors and supply factors.…”

Section: Related Reasearchmentioning

confidence: 99%

Research on the Impact of the Rent in Shanghai Based on Multiple Linear Regression Model

Zhang¹

2023

HSET

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In today's era, renting has become one of the most important ways of living for young people. Therefore, finding the main influencing factors of rent has important guiding value for both tenants and the government. By establishing a multiple linear regression model, this paper analyzes about 2,900 rental data collected from Shanghai, and obtains the relationship between Shanghai housing rent and some main influencing factors. Some variables are processed to be incorporated into a multiple linear regression model. In the process of establishing the model, the variables that have a significant impact on rent are screened out by significance testing. The model shows that the zone where the house is located and the type of lease have a greater impact on rent, while other factors such as the size of the house have less impact on rent. These results can help tenants make better choice about which to rent and help the government to regulate rental prices.

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“…People prefer houses that are close to these places for convenience and shorter commuting times. Overall, these factors, including house features, environmental conditions, and transportation accessibility, play a significant role in determining the prices of real estate properties (Zhang, 2021). Ghosalkar et al (2018) researched the importance of studying the cost of land due to its constantly changing prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Real Estate Prices in Romania: A Lag Optimized Linear Approach

Chirilus

2023

Baltic Journal of Real Estate Economics and Construction Management

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This study focuses on the real estate market in Romania and presents a forecasting model for predicting future real estate prices. The model, based on multiple linear regression, provides a comprehensive understanding of the market and enables real estate analysts to devise more efficient investment strategies. By enhancing investment efficiency, the model contributes to the overall efficiency of financial markets and supports sustained economic benefits for stakeholders. Although limited to a specific timeframe and apartment auction markets in Romania, future research can expand the model’s scope, improve accuracy through diverse data sets, and explore key factors for enhanced performance. The study’s contribution lies in its valuable insights for real estate analysts, enhancing investment efficiency, and fostering sustained economic benefits for stakeholders.

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Housing Price Prediction Based on Multiple Linear Regression

Cited by 26 publications

References 16 publications

Impact of Carbon Information on Enterprise Value: Analysis Model Design Based on Big Data

Impact of Carbon Information on Enterprise Value: Analysis Model Design Based on Big Data

Research on the Impact of the Rent in Shanghai Based on Multiple Linear Regression Model

Forecasting Real Estate Prices in Romania: A Lag Optimized Linear Approach

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