Abstract
In this paper, the author first analyzes the major factors affecting housing prices with Spearman correlation coefficient, selects significant factors influencing general housing prices, and conducts a combined analysis algorithm. Then, the author establishes a multiple linear regression model for housing price prediction and applies the data set of real estate prices in Boston to test the method. Through the data analysis and test in this paper, it can be summarized that the multiple linear regression model can effectively predict and analyze the housing price to some extent, while the algorithm can still be improved through more advanced machine learning methods.
Highlights
Ere is a well-known saying about the appraisal of real estate by Li Ka-Shing, the most famous property tycoon in Hong Kong: “ ree major factors are determining the price of a property, the first one is location, the second one is location, and the third one is still location.” His word does not seem to make much sense from a statistical research perspective
It is generally believed by academia that correctly predicting the special price for a specific real estate is impractical since it involves plenty of factors exerting influence on the eventual cost
Scientific Programming intelligence have been widely adopted in many aspects. Are they utilized in evaluating the price and value and they are applied to figure out potential future applications and would-be challenges [4]. e comprehensive adoption of machine learning and artificial intelligence in the property industry has generally transformed this experience-driven industry with great arbitrage opportunities to an intelligent and data-driven enterprise [5]
Summary
With the development of technology, high-dimensional data have been widely adopted in various fields, including economics, finance, and engineering. Spearman correlation coefficient has been a nonparametric rank statistic It was initially designed as a measure of the strength of the association between two variables. The Spearman correlation coefficient uses the rank of variables to compute the correlation between different variables. Irdly, the Spearman correlation coefficient can process discrete data practically It will not be affected by dimension, which means it can precisely measure the correlation between different dimensions. Erefore, the correlation of variables does not necessarily mean the linear relationship between variables It even cannot indicate a direct functional relation. In this sense, the Spearman correlation coefficient can better depict the correlation between variables when there exists nonlinearity [16]
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