A Novel Architecture and Machine Learning Algorithm for Real Estate

The real estate industry is a hot topic and the factors of a house which affect the investment benefit is worth of research. This paper designs a novel machine learning assisted real estate industry investment guidance (MLRIG) architecture and a machine learning algorithm, aiming at researching the factors and their weight respectively of a house which have influence on its investment value. The MLRIG architecture is composed of 4 stages: Data collection, Data discretization, Data Mining Process and Factors weight output; the proposed machine learning algorithm, called QSFL-LR (Quantum-inspired Shuffled Frog Leaping Logistic Regression), combines Quantum-inspired Shuffled Frog algorithm with Logistic Regression to select the factors of a house which affect the investment value before data training, then output the weight of the factors respectively. Experiment shows the proposed QSFL-LR algorithm has better performance in accuracy and precision compared with traditional Logistic Regression, proving the superiority of QSFL-LR. The experiment also shows MLRIG architecture can guide both business companies and individuals to reduce investment risk in real estate industry.