Price Recommendation Engine for Airbnb · 2017 SAS Analytics Day Price Recommendation Engine for...
Transcript of Price Recommendation Engine for Airbnb · 2017 SAS Analytics Day Price Recommendation Engine for...
2017
SAS Analytics Day
Price Recommendation Engine for Airbnb
Praneeth Guggilla, Student, Oklahoma State University
Snigdha Gutha, Student, Oklahoma State University
Objective
• Understanding the factors influencing occupancy rate of a property.
• Analyze how price determines occupancy rate.
Data Extraction
• Extracted information related to host, price and availability of the New York listings.
• Extracted 614,128 customer reviews and ratings of all New York listings.
*Source: http://www.airbnb.com/
Data Cleaning
• Using latitude and longitude we classified all locations into five major neighborhoods(Manhattan, Bronx, Staten Island, Queens and Brooklyn).
• Appropriate transformations are applied to reduce skewness and Kurtosis.
Source: http://www.insideairbnb.com/
Percentage of Low Occupancy Listings
High Occupancy Listings = 12,531Low Occupancy Listings = 11,328Overall listings = 23,859Total Variables = 65
Text Mining
*Reference: Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS by
Dr.Goutam Chakraborty, Murali Pagolu, Satish Garla
Variable Selection
• Variable clustering is used to select the continuous variables.• All the categorical variables and text topics are used
Regression Model
Occupancy Model Price Model
• significant predictors – Price and room type
• Important predictors – Security deposit,
cleanliness review, neighborhood location,
and extra person fee
Significant Predictors – Security deposit and room type
Important predictors – Property type and neighborhood
Occupancy Model Text TopicsNot included
Text TopicsIncluded
Misclassification rate 39.84% 38.6%
Price Model Text TopicsNot included
Text TopicsIncluded
Adjusted R Square 68.74% 69.78%
Insights & Conclusions
Insights
• Manhattan - security deposit for high and low occupancy rate listings differed by 25%
• Bronx - average price for high and low occupancy rate listings differed by $18
• Shared apartment service had 12% higher occupancy rate than other room types
• Flexible pricing was more effective than strict and moderate pricing policy
Conclusions & Future Scope
• Price is major determining factor
• Cleanliness and reviews by other customers are other driving factors.
• Perform sentiment analysis on the text reviews
• Build optimization model to come up with optimal prices.
2017
SAS Analytics Day
Contact
Name : Praneeth GuggillaOrganization : Oklahoma State UniversityContact No: 405-780-5330Email : [email protected]: https://www.linkedin.com/in/praneethguggilla
Name : Snigdha GuthaOrganization : Oklahoma State UniversityContact No: 405-780-5330Email : [email protected]: https://www.linkedin.com/in/snigdhagutha