Identifying Berlin’s land value map using adaptive weights smoothing

Abstract

We use adaptive weights smoothing (AWS) of Polzehl and Spokoiny (J R Stat Soc Ser B 62:335–354, 2000; Ann Stat 31:30–57, 2003; Probab Theory Relat Fields 135:335–362, 2006) to estimate a map of land values for Berlin, Germany. Our data are prices of undeveloped land that was transacted between 1996 and 2009. Even though the observed land price is an indicator of the respective land value, it is influenced by transaction noise. The iterative AWS applies piecewise constant regression to reduce this noise and tests at each location for constancy at the margin. If not rejected, further observations are included in the local regression. The estimated land value map conforms overall well with expert-based land values. Our application suggests that the transparent AWS could prove a useful tool for researchers and real estate practitioners alike.