House Price Prediction Based On Deep Learning

TL;DR

This paper proposes a novel house price prediction method utilizing deep learning that combines visual and textual features to improve accuracy and assist consumers and policymakers.

Contribution

It introduces a new approach integrating mixed depth vision and text features for more effective house price prediction.

Findings

Improved prediction accuracy over traditional methods

Effective integration of visual and textual data

Potential to aid consumers and policymakers

Abstract

Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than "to live and work happily, to eat and dress comfortable". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features.