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The Predictability of House Prices: "Human Against Machine"

Author

Listed:
  • Kristoffer B. Birkeland

    (Norwegian University of Science and Technology)

  • Allan D. D'Silva

    (Norwegian University of Science and Technology)

  • Roland Füss

    (University of St.Gallen)

  • Are Oust

    (Norwegian University of Science and Technology)

Abstract

We develop an automated valuation model (AVM) for the residential real estate market by leveraging stacked generalization and a comparable market analysis. Specifically, we combine four novel ensemble learning methods with a repeat sales method and tailor the data selection for each value estimate. We calibrate and evaluate the model for the residential real estate market in Oslo by producing out-of-sample estimates for the value of 1,979 dwellings sold in the first quarter of 2018. Our novel approach of using stacked generalization achieves a median absolute percentage error of 5.4%, and more than 96% of the dwellings are estimated within 20% of their actual sales price. A comparison of the valuation accuracy of our AVM to that of the local estate agents in Oslo generally demonstrates its viability as a valuation tool. However, in stable market phases, the machine falls short of human capability.

Suggested Citation

  • Kristoffer B. Birkeland & Allan D. D'Silva & Roland Füss & Are Oust, 2021. "The Predictability of House Prices: "Human Against Machine"," International Real Estate Review, Global Social Science Institute, vol. 24(2), pages 139-183.
  • Handle: RePEc:ire:issued:v:24:n:02:2021:p:139-183
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    References listed on IDEAS

    as
    1. Kostas Tsatsaronis & Haibin Zhu, 2004. "What drives housing price dynamics: cross-country evidence," BIS Quarterly Review, Bank for International Settlements, March.
    2. Agostino Valier, 2020. "Who performs better? AVMs vs hedonic models," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 38(3), pages 213-225, March.
    3. Are Oust & Simen N. Hansen & Tobias R. Pettrem, 2020. "Combining Property Price Predictions from Repeat Sales and Spatially Enhanced Hedonic Regressions," The Journal of Real Estate Finance and Economics, Springer, vol. 61(2), pages 183-207, August.
    4. Jim Clayton & David Geltner & Stanley W. Hamilton, 2001. "Smoothing in Commercial Property Valuations: Evidence from Individual Appraisals," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 29(3), pages 337-360, March.
    5. Karl E. Case & Robert J. Shiller, 1987. "Prices of single-family homes since 1970: new indexes for four cities," New England Economic Review, Federal Reserve Bank of Boston, issue Sep, pages 45-56.
    6. Northcraft, Gregory B. & Neale, Margaret A., 1987. "Experts, amateurs, and real estate: An anchoring-and-adjustment perspective on property pricing decisions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 39(1), pages 84-97, February.
    7. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
    8. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
    9. Fisher, Jeffrey D & Geltner, David M & Webb, R Brian, 1994. "Value Indices of Commercial Real Estate: A Comparison of Index Construction Methods," The Journal of Real Estate Finance and Economics, Springer, vol. 9(2), pages 137-164, September.
    10. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    11. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    12. Crone, Theodore M. & Voith, Richard P., 1992. "Estimating house price appreciation: A comparison of methods," Journal of Housing Economics, Elsevier, vol. 2(4), pages 324-338, December.
    13. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    14. David Geltner, 2015. "Real Estate Price Indices and Price Dynamics: An Overview from an Investments Perspective," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 615-633, December.
    15. Gatzlaff Dean H. & Ling David C., 1994. "Measuring Changes in Local House Prices: An Empirical Investigation of Alternative Methodologies," Journal of Urban Economics, Elsevier, vol. 35(2), pages 221-244, March.
    16. Quan, Daniel C & Quigley, John M, 1991. "Price Formation and the Appraisal Function in Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 4(2), pages 127-146, June.
    17. Meese, Richard A & Wallace, Nancy E, 1997. "The Construction of Residential Housing Price Indices: A Comparison of Repeat-Sales, Hedonic-Regression and Hybrid Approaches," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 51-73, Jan.-Marc.
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    More about this item

    Keywords

    AVMs; Housing Market; Machine Learning; Repeat Sales Approach; XGBoost.;
    All these keywords.

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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