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

Amenities Text Topics

Modeling Approach

Note: Refer paper for detailed modeling approach

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 : praneeth.guggilla@okstate.eduLinkedIn: https://www.linkedin.com/in/praneethguggilla

Name : Snigdha GuthaOrganization : Oklahoma State UniversityContact No: 405-780-5330Email : snigdha.gutha@okstate.eduLinkedIn: https://www.linkedin.com/in/snigdhagutha