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

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Segmenting regression by region

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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

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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

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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