  Housing has emerged as a crucial concern among young individuals residing in
major cities, including Shanghai. Given the unprecedented surge in property
prices in this metropolis, young people have increasingly resorted to the
rental market to address their housing needs. This study utilizes five
traditional machine learning methods: multiple linear regression (MLR), ridge
regression (RR), lasso regression (LR), decision tree (DT), and random forest
(RF), along with a Large Language Model (LLM) approach using ChatGPT, for
predicting the rental prices of lane houses in Shanghai. It applies these
methods to examine a public data sample of about 2,609 lane house rental
transactions in 2021 in Shanghai, and then compares the results of these
methods. In terms of predictive power, RF has achieved the best performance
among the traditional methods. However, the LLM approach, particularly in the
10-shot scenario, shows promising results that surpass traditional methods in
terms of R-Squared value. The three performance metrics: mean squared error
(MSE), mean absolute error (MAE), and R-Squared, are used to evaluate the
models. Our conclusion is that while traditional machine learning models offer
robust techniques for rental price prediction, the integration of LLM such as
ChatGPT holds significant potential for enhancing predictive accuracy.
