1. Trulia Estimates v2.0

3. Motivation
• Trulia Estimates launched in 2011
• Public records snowball has evolved since then, but the valuation
algorithm has not
• Valuations already have a lot of visibility (valuation heatmaps etc)
and we are planning to give them even more visibility in the near
future (valuations history)
• Brilliant Basics – Improve estimates before surfacing them
everywhere

4. Us v/s Competition
0 5 10 15
Trulia
Estimates
Zestimate
Median Error %
Trulia
Estimates
Zestimate

5. Our Work
• Location specific and temporal features
• Crime Safety
• School Proximity
• Stats and Trends
• New Geoscopes
• Solve the problem of geographic boundaries
• Model Learning Improvements
• Explicit modeling of location hierarchies
• Better learned parameters
• Better feature representation and normalization

6. New Features
8.97
8.78
8.82
8.84
8.65
8.7
8.75
8.8
8.85
8.9
8.95
9
Baseline Add
CrimeScore
only
Add
SchoolScore
only
Add avg
ppsqft/ hood
only
Improvement by Individual Features
Median Error
Percentage

7. New Geoscopes

8. New Geoscopes

9. New Geoscopes
 After the initial pass
 Coverage improved by 1.67% ~ 1.15million properties throughout the
nation
 330 more counties valued
 For San Mateo, median error goes from 8.97% to 8.85%

10. Model Learning Improvements
 Each geography is different. Static set of model parameters not
always ideal
 Using cross validation to learn parameters for each location model
from data
 Median error % improves from 8.97 to 8.69 (~3% relative improvement)
 Hierarchical Modeling
 Explicitly model Location Hierarchies to get smoother estimates using
higher level information

11. What’s Next?
 Spend more time optimizing new features – Optimization is
everything!
 Add price trends data to the hedonic model and simplify our learning
process
 Make per model parameter optimization scalable
 Incorporate hierarchical models into the existing mix