1. Valuations User
Feedback
Algorithms Team

2. What We Do
 Extract features from property data (#
beds, # baths, sqft, past sales etc)
 Use a very complex algorithm to estimate
price using these features
 Give users the option to register their
feedback

3. What We Do
 Extract features from property data (#
beds, # baths, sqft etc)
 Use a very complex algorithm to estimate
price using these features
 Give users the option to register their
feedback

5. Useful Feedback

6. Useless Junk

7. Goals
 Auto correction of the estimate based on
the feedback?
 Checking for obvious mistakes?
 Facilitating easy review of the feedbacks?
 Discovering new useful features?

8. Goals
 Auto correction of the estimate based on
the feedback? No
 Checking for obvious mistakes?
 Facilitating easy review of the feedbacks?
 Discovering new useful features?

9. Goals
 Auto correction of the estimate based on
the feedback?
 Checking for obvious mistakes? Yes
 Facilitating easy review of the feedbacks?
 Discovering new useful features?

10. Goals
 Auto correction of the estimate based on
the feedback?
 Checking for obvious mistakes?
 Facilitating easy review of the feedbacks?
Yes
 Discovering new useful features?

11. Goals
 Auto correction of the estimate based on
the feedback?
 Checking for obvious mistakes?
 Facilitating easy review of the feedbacks?
 Discovering new useful features? Perhaps

12. Unsorted Feedback
http://analyticsnn2.sv2.trulia.com:8111/feedback-2012-05-21.html

13. After Sorting

14. After Sorting

15. What is the Sorting Based on?
 How detailed is the replay?
 How far off are we?
 Did a registered user submit the
feedback?
 Normalization/ regularization

16. Distribution by Geographical
Scope
Global Average: 1.2178

17. Text Clustering
 Use Apache Mahout to cluster feedback
text
 Discover terms that most frequently occur
together in feedback texts