Study on Deleveraging and Downshifting of Real Estate Enterprises in the Context of Artificial Intelligence

Abstract

Abstract With the decrease in the heat of the real estate market, the structural problems of real estate enterprises have become increasingly prominent, which are mainly reflected in high inventory, high leverage and high cost. After analyzing the types and underlying factors of financial risks faced by real estate enterprises, this paper explains the “three red lines” policy of the deleveraging mechanism for real estate enterprises. Based on artificial intelligence technology, an intelligent financial data processing platform has been established, and PCA and K-Means algorithms are used to process financial data. The association rule mining was introduced to analyze the financial data. The deep meaning of the financial data was mined through the weighted Apriori algorithm. The deleveraging strategy of real estate enterprises was established in combination with the financial decision-support process. The application effect of Group W’s deleveraging strategy was analyzed by selecting it as the research object. In 2021, the current ratio was 0.03 times different from the industry average, the asset-liability ratio decreased to 75.69%, and the overall cash balance at the end of the period increased by 11.2 times. After 2018, the “three red lines” indicators were all met, and it successfully ranked among the green enterprises, with the highest cumulative excess rate of return reaching 5.46%. Based on intelligent financial data processing, it can provide decision-making support for real estate enterprises to optimize their structure and realize the deleveraging, downshifting, and efficiency increase of real estate enterprises.