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Mass appraisal without statistical estimation: a simplified comparable sales approach based on a spatiotemporal matrix

Author

Listed:
  • Sonia Yousfi

    () (Université de Bourgogne)

  • Jean Dubé

    () (Université Laval)

  • Diègo Legros

    () (Université de Bourgogne)

  • Sotirios Thanos

    () (University of Manchester)

Abstract

For mass appraisal in real estate, the hedonic pricing method (HPM) tends to be most commonly used by academic researchers, while the comparable sales approach (CSA) is mostly preferred by professionals. This paper shows how CSA is a constrained version of a spatial autoregressive model, which can be implemented by simple matrix calculations. The CSA takes into account information on individual characteristics identifying similar complex goods, spatial proximity reflecting similar spatial amenities and temporal constraints by only selecting past sales. Using US transaction data from Lucas County, Ohio, we compare CSA to a-spatial HPM results and conduct an out-of-sample exercise to gauge the prediction performance of the two approaches. The findings suggest that CSA is a very useful tool for mass appraisal, especially when the number of independent variables available is limited.

Suggested Citation

  • Sonia Yousfi & Jean Dubé & Diègo Legros & Sotirios Thanos, 2020. "Mass appraisal without statistical estimation: a simplified comparable sales approach based on a spatiotemporal matrix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 349-365, April.
  • Handle: RePEc:spr:anresc:v:64:y:2020:i:2:d:10.1007_s00168-019-00959-2
    DOI: 10.1007/s00168-019-00959-2
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    References listed on IDEAS

    as
    1. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, September.
    2. Jean Dubé & Diègo Legros, 2013. "Dealing with spatial data pooled over time in statistical models," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 1-18, March.
    3. Luc Anselin & Nancy Lozano-Gracia, 2008. "Errors in variables and spatial effects in hedonic house price models of ambient air quality," Empirical Economics, Springer, vol. 34(1), pages 5-34, February.
    4. Claude Besner, 2002. "A Spatial Autoregressive Specification with a Comparable Sales Weighting Scheme," Journal of Real Estate Research, American Real Estate Society, vol. 24(2), pages 193-212.
    5. Thanos, Sotirios & Dubé, Jean & Legros, Diègo, 2016. "Putting time into space: the temporal coherence of spatial applications in the housing market," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 78-88.
    6. Can, Ayse, 1992. "Specification and estimation of hedonic housing price models," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 453-474, September.
    7. Francois Des Rosiers & Antonio Lagana & Marius Theriault, 2001. "Size and proximity effects of primary schools on surrounding house values," Journal of Property Research, Taylor & Francis Journals, vol. 18(2), pages 149-168, January.
    8. R. Kelley Pace & Otis W. Gilley, 1998. "Generalizing the OLS and Grid Estimators," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 26(2), pages 331-347, June.
    9. Torsten Hägerstrand, 1989. "Reflections On “What About People In Regional Science?”," Papers in Regional Science, Wiley Blackwell, vol. 66(1), pages 1-6, January.
    10. Hans R. Isakson, 1986. "The Nearest Neighbors Appraisal Technique: An Alternative to the Adjustment Grid Methods," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 14(2), pages 274-286, June.
    11. R. Kelley Pace & James P. LeSage, 2004. "Spatial Statistics and Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 147-148, September.
    12. 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..
    13. Thanos, Sotirios & Dubé, Jean & Legros, Diègo, 2016. "Putting time into space: the temporal coherence of spatial applications in the housing market," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 78-88.
    14. Devaux, Nicolas & Dubé, Jean & Apparicio, Philippe, 2017. "Anticipation and post-construction impact of a metro extension on residential values: The case of Laval (Canada), 1995–2013," Journal of Transport Geography, Elsevier, vol. 62(C), pages 8-19.
    15. Peter F. Colwell & Roger E. Cannaday & Chunchi Wu, 1983. "The Analytical Foundations of Adjustment Grid Methods," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 11(1), pages 11-29, March.
    16. Jean Dubé & Diègo Legros & Sotirios Thanos, 2018. "Past price ‘memory’ in the housing market: testing the performance of different spatio-temporal specifications," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(1), pages 118-138, January.
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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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