Presentation Title

How much should I charge for my AirBnB? - Pricing AirBnB Using Descriptive and Predictive Data Mining

Faculty Mentor

Dr. Sonya Zhang

Start Date

23-11-2019 10:30 AM

End Date

23-11-2019 10:45 AM

Location

Markstein 301

Session

oral 2

Type of Presentation

Oral Talk

Subject Area

interdisciplinary

Abstract

Our research study intends to identify the key factors affecting the listing price of Airbnb using descriptive and predictive data mining. As AirBnB rises as a new form of tourist accommodation, many travelers enjoy staying in AirBnB housing due to their low costs, convenient locations, household amenities, and personal services. However, the owner of the property, or the host, may not have the best knowledge at setting the right prices for their property. A rough scan of local neighborhood prices may not be enough or accurate, for this we have to consider the differentiation. Our study is a multi-step process: First, we extracted and cleaned the AirBnB dataset which contains about 31,000 listings in Los Angeles County. Second, we performed exploratory data analysis and identified groups of similar listings using visualization and clustering analysis. Third, we developed a predictive model for the listing price based upon a collection of features such as type of property, number of rooms, location, amenities, reviews, etc. External factors include median rental prices grouped by zip code and the number of bedrooms from Zillow, and median hotel prices grouped by zip code from Yelp. For the predictive model, we applied the feature selection and evaluated the model performance.

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Nov 23rd, 10:30 AM Nov 23rd, 10:45 AM

How much should I charge for my AirBnB? - Pricing AirBnB Using Descriptive and Predictive Data Mining

Markstein 301

Our research study intends to identify the key factors affecting the listing price of Airbnb using descriptive and predictive data mining. As AirBnB rises as a new form of tourist accommodation, many travelers enjoy staying in AirBnB housing due to their low costs, convenient locations, household amenities, and personal services. However, the owner of the property, or the host, may not have the best knowledge at setting the right prices for their property. A rough scan of local neighborhood prices may not be enough or accurate, for this we have to consider the differentiation. Our study is a multi-step process: First, we extracted and cleaned the AirBnB dataset which contains about 31,000 listings in Los Angeles County. Second, we performed exploratory data analysis and identified groups of similar listings using visualization and clustering analysis. Third, we developed a predictive model for the listing price based upon a collection of features such as type of property, number of rooms, location, amenities, reviews, etc. External factors include median rental prices grouped by zip code and the number of bedrooms from Zillow, and median hotel prices grouped by zip code from Yelp. For the predictive model, we applied the feature selection and evaluated the model performance.