A demo of listtoclose.us.

1. Listtoclose.us
How much will a house get bid up before selling?
Peter Anthony
Insight Data Science Fellow

2. Predicting final sale price of house
Listed at $1.0 M
June 1st
Closed at $1.1 M
July 1st
ΔP, Δt

3. The data
• Database of ~150,000 properties in urban California
• Numerical features: home area, lot area, #bedrooms, #bathrooms,
year built, date of sale, lat/long
• Categorical features: ZIP code, home type, seller’s agent
• Median error assuming zero ΔP: 2.3%

4. A simple predictive model
• For each house, look at nearby houses that sold recently
• ΔP ~ 1 + List + ΔP1/List1 + List(ΔP1/List1) + ΔP2/List2 + …
• Gets sign of ΔP right 59% of the time
• When it does, median error reduced to 1.4%
r = 0.53
2
1
3

5. Segmenting regression by region
California
BayArea
LosAngeles
InlandEmpire
med. |ΔP|/List (%) 2.3 7.0 2.0 1.7
med. error in predicted sale price (%) 1.4 6.6 1.4 1.4
freq. sign correct (%) 59 87 60 60
Pearson’s r for ΔPpredicted vs. ΔP 0.53 0.42 0.43 0.14

6. Demo

7. Peter Anthony
PhD Biophysics
Stanford University
F
U
‡
G
Extension

9. Closer houses are more predictive
“Order”
ΔP ~ 1 + List 0
+ δP1 + δP2 + δP3 1
+ List(δP1 + δP2 + δP3) 2
+ δP1δP2 + δP1δP3 + δP2δP3 3
δPi = ΔPi/Listi
1.0
0.8
0.6
0.4
0.2
0.0
Normalizedcoefficient
1st 2nd 3rd
Nearest neighbor
1st order
2nd order
3rd order
Relative magnitudes of fit coefficients

10. Location, location, location

11. Days on market is harder to predict
R2 = 0.09