Influence Factors And The Forecast Of The Rental Housing Rent Based On Multiple Regression

In recent years,more and more university graduates choose to go to other cities to develop their careers and lives.For these people,buying a house is an almost unrealizable dream,which will gradually make renting a mainstream.However,the rents of most of the homes are priced by the owners.So,how does the general public judge whether the rent of a house is reasonable? How do I get a listing that fits my criteria? How can a rental website recommend a specific listing to users based on their requirements?This article addresses the above issues as follows:(1)This article crawled about 130,000 rental housing information of Zhejiang Province on the Anjuke website and the per capita GDP,fixed investmen,etc.of Zhejiang cities in recent years The data is modeled based on multivariate linear regression model and multi-level regression analysis.The factors that affect rents of rental houses are analyzed,and then the rents of houses are predicted based on the characteristics of the house types and surrounding facilities.(2)This article uses the characteristics of city,house name,house code,and latitude and longitude of the house as the label of the house.It uses units,lease methods,educational facilities,etc.are the characteristics of the property.According to the user's desire for the property,a feature setting is given,and the similarity degree of each house data to the feature setting is calculated to construct a recommendation list.(3)Construct the objective function,optimize the recommendation list based on multi-objective optimization,and filter out multiple recommendation lists that meet the objective function to form a recommendation class table set.