Post on 14-Aug-2015
Listtoclose.us How much will a house get bid up before selling?
Peter Anthony Insight Data Science Fellow
Predicting final sale price of house
Listed at $1.0 M June 1st
Closed at $1.1 M July 1st
ΔP, Δt
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%
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
Segmenting regression by region
Cal
iforn
ia
Bay
Are
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Los
Ang
eles
Inla
nd E
mpi
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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
Demo
Peter Anthony PhD Biophysics
Stanford University
F
U
‡
G
Extension
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
Norm
aliz
ed c
oeff
icie
nt
1st 2nd 3rd
Nearest neighbor
1st order 2nd order 3rd order
Relative magnitudes of fit coefficients
Location, location, location
Days on market is harder to predict
R2 = 0.09