[1L3-GS-13] AI application: Social application (1)

[1L3-GS-13-03] Predicting inquiry from potential renters using property listing information

- Prediction accuracy and applicability to business -

〇Takeshi SO¹, Yuta ARAI² (1. Daito Trust Construction Co.,Lytd., 2. Reitaku University AI/business Research Center)

Keywords:Realestate, Rental housing, Logistic regression, xgboost, RandomForest

In this study, We deduced how accurate the number of inquiries from potential tenants for housing available for rent can be predicted based on the attributes of the housing, using multiple statistical methods, and compared the results. The purpose of this study is to show these results as case studies.

Confusion matrices were calculated based on the results deduced with three methods – the classical logistic regression, RandomForest, and XGBoost – and prediction accuracies were verified. The results showed that the accuracy of XGBoost was the highest, followed by that of logistic regression.

It is sometimes desirable to use logistic regression, which is easy to interpret from the perspective of application to business, because the differences in accuracy among the statistical methods are not significant. It is thus important in business to take into account the accuracy, ease of interpretation, and research structure and select the most appropriate statistical method.