This document discusses preliminary analysis on estimating house prices. It explores using tax assessments, location factors like school quality and transportation, and market trend data from recent comparable property sales. The author develops an initial formula using the tax assessed base value times a timing index based on recent sale prices. Estimates are generated for some sample areas, but error rates are still high, requiring more data and model improvements to provide reliable price range predictions.

1. Preliminary analysis report
Xianghui Dong

2. Can you estimate house price?
 NO
 Different type of buyers
 Different valuation and preference
 Small pool of serious buyer
 Timing
 YES
 At least an range
 Experienced Realtor can

3. Zestimate®
 Industry leading metric performance:
Within 20% of sale price in 79.7% of time

4. Zestimate® formula
Physical attributes
Tax assessments
Prior and current transactions
data on 110 million homes (March 2013)

5. Really all about 3 things
 Location
 Education – school district
 Transportation – distance to major sites
 Community
 House itself
 Size, design, condition…
 Timing
 Market trend
 Timing for seller and buyer

6. Can we model it?
Available data
 Lots of macroscopic data:
 Economy, Mortgage rates…
 Local and property data
 Diverse data sources and formats
 Data quality problems
 Main API available

7. Start from my local area

8. Focus
 Top priority: School district
 All great school districts
 Suburbs
 Price range: 400k ~ 700k
 Assumption:
 Potential buyers are similar and comparable

9. Recent Sales Data

10. Property Data for each sale

11. One data that rule them all
 Assessment on property value every 3 years
 sales approach
 cost approach
 land + improvement + adjustment

12. Not so perfect reality
Market Value Index
3 years period

13. My first formula
Sale price =
Base (Tax assessment) * Timing index
 Intrinsic value adjusted by market trend
 Tax assessment have them already:
 Location, land, Sqft, bedrooms, conditions
 Comparable sales
 Use more up to date comparable sales to adjust

14. Define “comparable”
 Sales in same area
 Previous 40 days
 Tax assessment within 15% difference
 60k ~ 100k for 400k ~ 700k
 More complex conditions didn’t improve

15. Moving average of timing index
 Timing index = sale price / tax assessment
 Simple moving average for comparable sales
 Limit to 0.8 ~ 1.4
 Most are foreclosures, trustee auction, investment buyer
 Zillow exclude foreclosures too
 Half price
 Auctioned with loan amount 15 years ago
 Tear down and Rebuild
 Not comparable to regular buyer, not too many

16. 20854, Potomac, MD
 Suburb and great school district

17. 20850, Rockville, MD
 City and good transportation

18. 46077, Zionsville, Indiana

19. Estimation error for Potomac
 Mean = 0.005710863
 sd = 0.1124423

20. Estimation error for Rockville

21. Estimation error for Zionsville

22. Need lots of improvement
The error is still too big
Follow the trend, not prediction
Compare to Zestimate
More data