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Identifying Berlin's land value map using adaptive weights smoothing

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  • Kolbe, Jens
  • Schulz, Rainer
  • Wersing, Martin
  • Werwatz, Axel

Abstract

We use Adaptive Weights Smoothing (AWS) of Polzehl and Spokoiny (2000, 2003, 2006) to estimate a map of land values for Berlin, Germany. Our data are prices of undeveloped land that was transacted between 1996-2009. Even though the observed land price is an indicator of the respective land value, it is in uenced 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.

Suggested Citation

  • Kolbe, Jens & Schulz, Rainer & Wersing, Martin & Werwatz, Axel, 2014. "Identifying Berlin's land value map using adaptive weights smoothing," SFB 649 Discussion Papers 2015-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2015-003
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    References listed on IDEAS

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    1. Kevin A. Bryan & Pierre-Daniel G. Sarte, 2009. "Semiparametric estimation of land price gradients using large data sets," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 95(Win), pages 53-74.
    2. Gabriel Ahlfeldt & Wolfgang Maennig, 2010. "Impact of sports arenas on land values: evidence from Berlin," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 44(2), pages 205-227, April.
    3. Douglas B. Diamond, Jr., 1980. "The Relationship between Amenities and Urban Land Prices," Land Economics, University of Wisconsin Press, vol. 56(1), pages 21-32.
    4. Kennedy, Peter, 1983. "Logarithmic Dependent Variables and Prediction Bias," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 45(4), pages 389-392, November.
    5. J. Polzehl & V. G. Spokoiny, 2000. "Adaptive weights smoothing with applications to image restoration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 335-354.
    6. Raphael W. Bostic & Stanley D. Longhofer & Christian L. Redfearn, 2007. "Land Leverage: Decomposing Home Price Dynamics," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(2), pages 183-208, June.
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    Cited by:

    1. Helbing, Georg & Shen, Zhiwei & Odening, Martin & Ritter, Matthias, 2017. "Estimating Location Values of Agricultural Land," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 66(3), September.
    2. Fiebig, Ewelina Marta, 2021. "On data-driven choice of λ in nonparametric Gaussian regression via Propagation–Separation approach," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    3. Kolbe, Jens & Schulz, Rainer & Wersing, Martin & Werwatz, Axel, 2019. "Land value appraisal using statistical methods," FORLand Working Papers 07 (2019), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    4. Ahlfeldt, Gabriel M. & Heblich, Stephan & Seidel, Tobias, 2023. "Micro-geographic property price and rent indices," Regional Science and Urban Economics, Elsevier, vol. 98(C).
    5. Braun, Stefanie & Lee, Gabriel S., 2021. "The prices of residential land in German counties," Regional Science and Urban Economics, Elsevier, vol. 89(C).
    6. Stefan Seifert & Christoph Kahle & Silke Hüttel, 2021. "Price Dispersion in Farmland Markets: What Is the Role of Asymmetric Information?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(4), pages 1545-1568, August.
    7. Yun Hye Hwang & Ivan Kurniawan Nasution & Deepika Amonkar & Amy Hahs, 2020. "Urban Green Space Distribution Related to Land Values in Fast-Growing Megacities, Mumbai and Jakarta–Unexploited Opportunities to Increase Access to Greenery for the Poor," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    8. Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2019. "Bodenwertermittlung mit statistischen Methoden [Land value appraisal using statistical methods]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 5(1), pages 131-154, November.
    9. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.
    10. Kumhof, Michael & Tideman, Nicolaus & Hudson, Michael & Goodhart, Charles, 2021. "Post-Corona Balanced-Budget Super-Stimulus: The Case for Shifting Taxes onto Land," CEPR Discussion Papers 16652, C.E.P.R. Discussion Papers.
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    More about this item

    Keywords

    land value; adaptive weight smoothing; spatial modeling;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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