Identifying Berlin’s land value map using adaptive weights smoothing

Jens Kolbe, Rainer Schulz, Martin Wersing, Axel Werwatz (Corresponding Author)

    Research output: Contribution to journalArticle

    2 Citations (Scopus)
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    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.
    Original languageEnglish
    Pages (from-to)767-790
    Number of pages24
    JournalComputational Statistics
    Volume30
    Issue number3
    Early online date25 Feb 2015
    DOIs
    Publication statusPublished - Sep 2015

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    Smoothing
    Local Regression
    Alike
    Margin
    Field Theory
    Transactions
    Regression
    Estimate
    Land values
    Observation

    Keywords

    • land value
    • adaptive weight smoothing
    • spatial modelling

    Cite this

    Identifying Berlin’s land value map using adaptive weights smoothing. / Kolbe, Jens; Schulz, Rainer; Wersing, Martin; Werwatz, Axel (Corresponding Author).

    In: Computational Statistics, Vol. 30, No. 3, 09.2015, p. 767-790.

    Research output: Contribution to journalArticle

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