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Housing price prediction: parametric versus semi-parametric spatial hedonic models

Author

Listed:
  • José-María Montero

    (University of Castilla-La Mancha)

  • Román Mínguez

    (University of Castilla-La Mancha)

  • Gema Fernández-Avilés

    (University of Castilla-La Mancha)

Abstract

House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.

Suggested Citation

  • José-María Montero & Román Mínguez & Gema Fernández-Avilés, 2018. "Housing price prediction: parametric versus semi-parametric spatial hedonic models," Journal of Geographical Systems, Springer, vol. 20(1), pages 27-55, January.
    • Handle: RePEc:kap:jgeosy:v:20:y:2018:i:1:d:10.1007_s10109-017-0257-y
    DOI: 10.1007/s10109-017-0257-y
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    2. Rodrigo García Arancibia & Pamela Llop & Mariel Lovatto, 2023. "Nonparametric prediction for univariate spatial data: Methods and applications," Papers in Regional Science, Wiley Blackwell, vol. 102(3), pages 635-672, June.
    3. Katarzyna Kopczewska & Mateusz Kopyt & Piotr Ćwiakowski, 2021. "Spatial Interactions in Business and Housing Location Models," Land, MDPI, vol. 10(12), pages 1-25, December.
    4. Ziwei Hu & Hotaka Kobori & Brent Swallow & Feng Qiu, 2022. "Willingness to pay for multiple dimensions of green open space: Applying a spatial hedonic approach," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 70(3), pages 179-201, September.
    5. Runqiu Liu & Chao Yu & Canmian Liu & Jian Jiang & Jing Xu, 2018. "Impacts of Haze on Housing Prices: An Empirical Analysis Based on Data from Chengdu (China)," IJERPH, MDPI, vol. 15(6), pages 1-21, June.
    6. Å aszkiewicz, Edyta & Heyman, Axel & Chen, Xianwen & Cimburova, Zofie & Nowell, Megan & Barton, David N, 2022. "Valuing access to urban greenspace using non-linear distance decay in hedonic property pricing," Ecosystem Services, Elsevier, vol. 53(C).
    7. Minmeng Tang & Deb Niemeier, 2021. "How Does Air Pollution Influence Housing Prices in the Bay Area?," IJERPH, MDPI, vol. 18(22), pages 1-13, November.

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    More about this item

    Keywords

    Housing prices; Semi-parametric spatial hedonic models; Generalized additive models; Penalized splines; Mixed models;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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