[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120737-en":3,"doc-seo-120737-105":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},120737,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Predicting Listing Prices in Dynamic Short Term Rental Markets Using Machine Learning Models","This research develops a machine learning approach to predict listing prices in dynamic short-term rental markets, using Airbnb-style listings as the proving ground. The study targets frequent price fluctuations driven by demand, seasonality, and related market conditions, where accurate forecasts support both hosts’ revenue optimization and travelers’ booking decisions. A methodical modeling pipeline is built with feature engineering enhanced by sentiment analysis to analyze periodic price changes and estimate Austin, Texas listing prices, while also identifying key influencing factors.","PREDICTING LISTING PRICES IN DYNAMIC SHORT TERM RENTAL MARKETS USING MACHINE LEARNING MODELS  \nSAM CHAPMAN, SEIFEY MOHAMMAD AND KIMBERLY VILLEGAS  \nTABLE OF CONTENTS  \n1. INTRODUCTION.......................................................................................................................2  \n1. 1 BACKGROUND.................................................................................................................. 2  \n1.2 OBJECTIVES.......................................................................................................................3  \n2. RELATED WORKS................................................................................................................... 3  \n2. 1 LINEAR REGRESSION...................................................................................................... 3  \n2.2 RANDOM FOREST............................................................................................................ 5  \n2.3 EXTREME GRADIENT BOOSTING (XGBOOST) .......................................................... 7  \n3. DATA DESCRIPTION............................................................................................................... 7  \n3. 1 LISTINGS............................................................................................................................ 8  \n3.2 CALENDAR........................................................................................................................ 9  \n3.3 REVIEWS.......................................................................................................................... 10  \n4. DATA SCIENCE PIPELINE................................................................................................... 10  \n4. 1 DATA WRANGLING........................................................................................................ 10  \n4. 1. 1 PREPROCESSING LISTINGS....................................................................................... 11  \n4. 1.2 PREPROCESSING CALENDAR................................................................................... 11  \n4. 1.3 PREPROCESSING REVIEWS....................................................................................... 13  \n4.2 DATA EXPLORATION..................................................................................................... 15  \n4.2. 1 EXPLORING LISTINGS................................................................................................ 15  \n4.2.2 EXPLORING CALENDAR............................................................................................20  \n4.2.3 EXPLORING REVIEWS................................................................................................22  \n4.3 DATA MODELING........................................................................................................... 24  \n5. EXPERIMENTS....................................................................................................................... 25  \n5. 1 SETUP................................................................................................................................ 25  \n5. 1. 1 FEATURE SELECTION.................................................................................................25  \n5.2 EXPERIMENTAL RESULTS............................................................................................29  \n5.3 CROSS-VALIDATION RESULTS................................................................................... 30  \n5.4 SHAP ANALYSIS............................................................................................................. 31  \n6. CONCLUSION......................................................................................................................... 32  \n6. 1 IMPACT............................................................................................................................. 33  \n6.2 FUTURE WORK..............................................","cbCaibYmd5cRw0Ey","https://ap.wps.com/l/cbCaibYmd5cRw0Ey","pdf",1809095,1,40,"English","en",105,"# Introduction\n## Background\n## Objectives\n# Related Works\n## Linear Regression\n## Random Forest\n## Extreme Gradient Boosting (XGBoost)\n# Data Description\n## Listings\n## Calendar\n## Reviews\n# Data Science Pipeline\n## Data Wrangling\n### Preprocessing Listings\n### Preprocessing Calendar\n### Preprocessing Reviews\n## Data Exploration\n### Exploring Listings\n### Exploring Calendar\n### Exploring Reviews\n## Data Modeling\n# Experiments\n## Setup\n### Feature Selection\n## Experimental Results\n## Cross-Validation Results\n## Shap Analysis\n# Conclusion\n## Impact\n## Future Work\n# References\n# Appendix","[{\"question\":\"What problem does the project address in dynamic short-term rental markets?\",\"answer\":\"It addresses the challenge of predicting frequently fluctuating listing prices in a dynamic short-term rental market, motivated by Airbnb-style price volatility.\"},{\"question\":\"How does the project improve feature engineering for price prediction?\",\"answer\":\"It incorporates sentiment analysis into feature engineering to better capture signals that may influence price changes over time.\"},{\"question\":\"What is the main geographic focus and goal of the study?\",\"answer\":\"The study focuses on predicting Airbnb rental prices specifically in Austin, Texas, aiming to construct an accurate machine learning model and identify key drivers of price.\"}]","Predicting Listing Prices in Dynamic Short Term Rental Markets Using Machine Learning Models | 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