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Automated valuation modelling: a specification exercise

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  • Rainer Schulz
  • Martin Wersing
  • Axel Werwatz

Abstract

Market value predictions for residential properties are important for investment decisions and the risk management of households, banks and real estate developers. The increased access to market data has spurred the development and application of Automated Valuation Models (AVMs), which can provide appraisals at low cost. We discuss the stages involved when developing an AVM. By reflecting on our experience with md*immo, an AVM from Berlin, Germany, our paper contributes to an area that has not received much attention in the academic literature. In addition to discussing the main stages of AVM development, we examine empirically the statistical model development and validation step. We find that automated outlier removal is important and that a log model performs best, but only if it accounts for the retransformation problem and heteroscedasticity.

Suggested Citation

  • Rainer Schulz & Martin Wersing & Axel Werwatz, 2014. "Automated valuation modelling: a specification exercise," Journal of Property Research, Taylor & Francis Journals, vol. 31(2), pages 131-153, June.
    • Handle: RePEc:taf:jpropr:v:31:y:2014:i:2:p:131-153
    DOI: 10.1080/09599916.2013.846930
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    1. Anglin, Paul M & Gencay, Ramazan, 1996. "Semiparametric Estimation of a Hedonic Price Function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 633-648, Nov.-Dec..
    2. van Garderen, Kees Jan, 2001. "Optimal prediction in loglinear models," Journal of Econometrics, Elsevier, vol. 104(1), pages 119-140, August.
    3. Shiller, Robert J & Weiss, Allan N, 1999. "Evaluating Real Estate Valuation Systems," The Journal of Real Estate Finance and Economics, Springer, vol. 18(2), pages 147-161, March.
    4. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053.
    5. William N. Goetzmann & Liang Peng, 2002. "The Bias of the RSR Estimator and the Accuracy of Some Alternatives," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 30(1), pages 13-39.
    6. R. Carter Hill & J. R. Knight & C. F. Sirmans, 1997. "Estimating Capital Asset Price Indexes," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 226-233, May.
    7. Yatchew,Adonis, 2003. "Semiparametric Regression for the Applied Econometrician," Cambridge Books, Cambridge University Press, number 9780521812832.
    8. Case, Bradford & Quigley, John M, 1991. "The Dynamics of Real Estate Prices," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 50-58, February.
    9. Palmquist, Raymond B, 1980. "Alternative Techniques for Developing Real Estate Price Indexes," The Review of Economics and Statistics, MIT Press, vol. 62(3), pages 442-448, August.
    10. Goetzmann, William Nelson, 1992. "The Accuracy of Real Estate Indices: Repeat Sale Estimators," The Journal of Real Estate Finance and Economics, Springer, vol. 5(1), pages 5-53, March.
    11. Bradford Case & John Clapp & Robin Dubin & Mauricio Rodriguez, 2004. "Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 167-191, September.
    12. Yen Goh & Greg Costello & Greg Schwann, 2012. "Accuracy and Robustness of House Price Index Methods," Housing Studies, Taylor & Francis Journals, vol. 27(5), pages 643-666.
    13. Harry Haupt & Joachim Schnurbus & Rolf Tschernig, 2010. "On nonparametric estimation of a hedonic price function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 894-901.
    14. Bin, Okmyung, 2004. "A prediction comparison of housing sales prices by parametric versus semi-parametric regressions," Journal of Housing Economics, Elsevier, vol. 13(1), pages 68-84, March.
    15. Brown, Bryan W & Mariano, Roberto S, 1984. "Residual-Based Procedures for Prediction and Estimation in a Nonlinear Simultaneous System," Econometrica, Econometric Society, vol. 52(2), pages 321-343, March.
    16. Thomas G. Thibodeau, 2003. "Marking Single-Family Property Values to Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(1), pages 1-22, March.
    17. John M. Clapp, 2004. "A Semiparametric Method for Estimating Local House Price Indices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(1), pages 127-160, March.
    18. Gencay, Ramazan & Xian, Yang, 1996. "A forecast comparison of residential housing prices by parametric versus semiparametric conditional mean estimators," Economics Letters, Elsevier, vol. 52(2), pages 129-135, August.
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    Cited by:

    1. 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".
    2. Demetris Demetriou, 2017. "A spatially based artificial neural network mass valuation model for land consolidation," Environment and Planning B, , vol. 44(5), pages 864-883, September.
    3. Miriam Steurer & Robert Hill, 2019. "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers 2019-02, University of Graz, Department of Economics.
    4. Elena Bykowa & Maria Skachkova & Ivan Raguzin & Irina Dyachkova & Maxim Boltov, 2022. "Automation of Negative Infrastructural Externalities Assessment Methods to Determine the Cost of Land Resources Based on the Development of a “Thin Client” Model," Sustainability, MDPI, vol. 14(15), pages 1-29, July.
    5. 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.
    6. Robert J. Hill & Norbert Pfeifer & Miriam Steurer, 2020. "The Airbnb Rent-Premium and the Crowding-Out of Long-Term Rentals," Graz Economics Papers 2020-06, University of Graz, Department of Economics.
    7. Jannet C. Bencure & Nitin K. Tripathi & Hiroyuki Miyazaki & Sarawut Ninsawat & Sohee Minsun Kim, 2019. "Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches," Sustainability, MDPI, vol. 11(13), pages 1-17, July.

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    More about this item

    JEL classification:

    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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