[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123989-en":3,"doc-seo-123989-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123989,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models","Housing affordability in Shanghai is increasingly critical for young residents as property prices rise and many households turn to renting. This study predicts rental prices for Shanghai lane houses using five traditional machine learning models—multiple linear regression, ridge regression, lasso regression, decision tree, and random forest—then compares them with a Large Language Model approach using ChatGPT. The experiments use a public dataset of about 2,609 rental transactions from 2021. Random forest achieves the best traditional performance, while ChatGPT in the 10-shot setting shows strong potential, improving R-squared beyond traditional methods. Evaluation relies on MSE, MAE, and R-squared, indicating that integrating LLMs may enhance predictive accuracy.","Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and  \nLarge Language Models  \narXiv :2405 . 17505v1 [ cs .LG] 26 May 2024  \nTingting Chen  \nDepartment of Data Science and Big Data Technology Shanghai International Studies University Shanghai, China [18790061936@163.com](18790061936@163.com)  \nShijing Si⋆ Department of Data Science and Big Data Technology Shanghai International Studies University Shanghai, China [shijing.si@shisu.edu.cn](shijing.si@shisu.edu.cn)  \nAbstract—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.  \nIndex Terms—Rental price prediction, Multiple linear regression, Ridge regression, Lasso regression, Decision tree, Random forest, ChatGPT, Large Language Models, Machine learning  \nI. INTRODUCTION  \nHousing plays a vital role in our lives, particularly in cities like Shanghai where high housing prices present significant challenges for tenants. The considerable variation in rental prices for lane houses adds complexity to housing choices, especially with the evolving demands for living environments driven by rapid urbanization.  \nUnderstanding the factors influencing rental prices encompasses several dimensions. Firstly, the scarcity of land resources and housing due to urbanization  \n⋆ Corresponding author: [shijing.si@shisu.edu.cn](shijing.si@shisu.edu.cn)  \nsignificantly contributes to high rental prices of lane houses[1] . Urban development constraints and limited mobility of lane houses create ample opportunity for rental price escalation. Secondly, changing demands for living environments play a crucial role. While traditional lane houses hold cultural significance, issues like limited space, short duration of use, and outdated facilities gradually diminish their appeal[2] .  \nMoreover, social and policy factors also influence rental prices. Land use restrictions in Shanghai’s core areas limit the number of lane houses, leading to higher rental prices and relative disadvantages for residents. Government policies on rental and transportation further impact rental prices. For instance, public transportation development and property rental regulations significantly shape urban residents’ residential choices[3] .  \nStudying rental price determinants is valuable for tenants, facilitating more accurate rental estimates and ensuring market stability. Additionally, it contributes to urbanization, social progress, and improved quality of life. For residents in lane houses, understanding rental prices expands housing options and enhances quality of life.  \nTo address this, we propose employing a combination of traditional machine learning methods and advanced Large Language Models (LLM) to predict la","cbCaicm90KNPQAlI","https://ap.wps.com/l/cbCaicm90KNPQAlI","pdf",979322,1,13,"English","en",105,"# Introduction\n## Housing demand and rental price complexity in Shanghai\n## Factors influencing lane house rental prices\n## Proposed approach and main contributions\n# Related Work\n## Current research status on rental houses\n## Machine learning methods","[{\"question\":\"Which models are used to predict lane house rental prices in Shanghai?\",\"answer\":\"The study uses multiple linear regression, ridge regression, lasso regression, decision tree, and random forest, and also evaluates a Large Language Model approach with ChatGPT.\"},{\"question\":\"How is ChatGPT evaluated for rental price prediction?\",\"answer\":\"ChatGPT is tested under multiple shot settings—0-shot, 1-shot, 5-shot, and 10-shot—by prompting it to predict rental prices and comparing outputs.\"},{\"question\":\"How do the models perform according to the paper’s metrics?\",\"answer\":\"Random forest performs best among traditional machine learning methods, while the ChatGPT approach—especially in the 10-shot scenario—shows promising results with R-squared that surpass traditional methods, measured using MSE, MAE, and R-squared.\"}]","Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models | 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models are used to predict lane house rental prices in Shanghai?","Question",{"text":75,"@type":76},"The study uses multiple linear regression, ridge regression, lasso regression, decision tree, and random forest, and also evaluates a Large Language Model approach with ChatGPT.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is ChatGPT evaluated for rental price prediction?",{"text":80,"@type":76},"ChatGPT is tested under multiple shot settings—0-shot, 1-shot, 5-shot, and 10-shot—by prompting it to predict rental prices and comparing outputs.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the models perform according to the paper’s metrics?",{"text":84,"@type":76},"Random forest performs best among traditional machine learning methods, while the ChatGPT approach—especially in the 10-shot scenario—shows promising results with R-squared that surpass traditional methods, measured using MSE, MAE, and 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