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Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich

Author

Listed:
  • Jakob A. Dambon

    (University of Zurich
    Lucerne University of Applied Sciences and Arts)

  • Stefan S. Fahrländer

    (Fahrländer Partner Raumentwicklung)

  • Saira Karlen

    (Fahrländer Partner Raumentwicklung)

  • Manuel Lehner

    (Fahrländer Partner Raumentwicklung)

  • Jaron Schlesinger

    (Fahrländer Partner Raumentwicklung)

  • Fabio Sigrist

    (Lucerne University of Applied Sciences and Arts)

  • Anna Zimmermann

    (Fahrländer Partner Raumentwicklung)

Abstract

This article examines the spatially varying effect of age on single-family house (SFH) prices. Age has been shown to be a key driver for house depreciation and is usually associated with a negative price effect. In practice, however, there exist deviations from this behavior which are referred to as vintage effects. We estimate a spatially varying coefficients (SVC) model to investigate the spatial structures of vintage effects on SFH pricing. For SFHs in the Canton of Zurich, Switzerland, we find substantial spatial variation in the age effect. In particular, we find a local, strong vintage effect primarily in urban areas compared to pure depreciative age effects in rural locations. Using cross validation, we assess the potential improvement in predictive performance by incorporating spatially varying vintage effects in hedonic models. We find a substantial improvement in out-of-sample predictive performance of SVC models over classical spatial hedonic models.

Suggested Citation

  • Jakob A. Dambon & Stefan S. Fahrländer & Saira Karlen & Manuel Lehner & Jaron Schlesinger & Fabio Sigrist & Anna Zimmermann, 2022. "Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-14, December.
    • Handle: RePEc:spr:sjecst:v:158:y:2022:i:1:d:10.1186_s41937-021-00080-2
    DOI: 10.1186/s41937-021-00080-2
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    References listed on IDEAS

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    1. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    2. David Wheeler & Catherine Calder, 2007. "An assessment of coefficient accuracy in linear regression models with spatially varying coefficients," Journal of Geographical Systems, Springer, vol. 9(2), pages 145-166, June.
    3. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    4. W. Brunauer & S. Lang & P. Wechselberger & S. Bienert, 2010. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," The Journal of Real Estate Finance and Economics, Springer, vol. 41(4), pages 390-411, November.
    5. David Wheeler & Lance Waller, 2009. "Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests," Journal of Geographical Systems, Springer, vol. 11(1), pages 1-22, March.
    6. Stephen Malpezzi & Larry Ozanne & Thomas G. Thibodeau, 1987. "Microeconomic Estimates of Housing Depreciation," Land Economics, University of Wisconsin Press, vol. 63(4), pages 372-385.
    7. Stefan Sebastian Fahrländer, 2006. "Semiparametric Construction of Spatial Generalized Hedonic Models for Private Properties," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 142(IV), pages 501-528, December.
    8. David C. Wheeler & Antonio Páez & Jamie Spinney & Lance A. Waller, 2014. "A Bayesian approach to hedonic price analysis," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 663-683, August.
    9. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    10. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    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. Kai Cao & Mi Diao & Bo Wu, 2019. "A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(1), pages 173-186, January.
    13. Clapp, John M. & Salavei, Katsiaryna, 2010. "Hedonic pricing with redevelopment options: A new approach to estimating depreciation effects," Journal of Urban Economics, Elsevier, vol. 67(3), pages 362-377, May.
    14. Clapp, John M. & Giaccotto, Carmelo, 1998. "Residential Hedonic Models: A Rational Expectations Approach to Age Effects," Journal of Urban Economics, Elsevier, vol. 44(3), pages 415-437, November.
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    Cited by:

    1. Fabienne Helfer & Volker Grossmann & Aderonke Osikominu, 2023. "How does immigration affect housing costs in Switzerland?," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-31, December.

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    More about this item

    Keywords

    Gaussian process; Spatial statistics; Real estate; Mass appraisal;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

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