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Space-time approach to commercial property prices valuation

Author

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  • Beatriz Larraz Iribas

    (Statistics - UCLM - Universidad de Castilla-La Mancha = University of Castilla-La Mancha)

  • Jose Maria Montero Lorenzo

    (Statistics - UCLM - Universidad de Castilla-La Mancha = University of Castilla-La Mancha)

Abstract

There exists three ways of approaching real estate prices: the cost approach, the market data approach and the income capitalization approach. In this article, we propose an improvement of the market data approach that takes into account the spatial component. In particular, we propose a modified market data approach based on interpolation, being the structure of the spatial correlation between the prices of properties the main factor to obtain the weights. Interpolation methods have been widely used for estimating real estate prices, but they do not take into account the structure of their spatial dependence. Although this drawback is overcome by kriged estimation, in the case of the prices of commercial properties they do not provide good estimates because the scarceness of the market information. This is why auxiliary information is needed and cokriging methods are used to obtain estimates that are more accurate. The aim of this paper is the comparison of cokriged estimation of premises prices in two different temporal moments in the emblematic old part of Toledo city (Spain), using housing prices as an auxiliary random function due to their strong correlation with the main one. Cokriging, kriging and inverse distance weighting results are compared.

Suggested Citation

  • Beatriz Larraz Iribas & Jose Maria Montero Lorenzo, 2011. "Space-time approach to commercial property prices valuation," Post-Print hal-00712371, HAL.
    • Handle: RePEc:hal:journl:hal-00712371
    DOI: 10.1080/00036846.2011.581212
    Note: View the original document on HAL open archive server: https://hal.science/hal-00712371
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    Cited by:

    1. Thomas M. Fullerton & Arturo Bujanda, 2018. "Commercial property values in a border metropolitan economy," Asia-Pacific Journal of Regional Science, Springer, vol. 2(2), pages 337-360, August.
    2. Fernandes, Guilherme Barreto & Artes, Rinaldo, 2016. "Spatial dependence in credit risk and its improvement in credit scoring," European Journal of Operational Research, Elsevier, vol. 249(2), pages 517-524.
    3. Jorge Chica-Olmo & Rafael Cano-Guervos & Mario Chica-Rivas, 2019. "Estimation of Housing Price Variations Using Spatio-Temporal Data," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    4. Fernandes, Guilherme Barreto & Artes , Rinaldo, 2013. "Spatial correlation in credit risk and its improvement in credit scoring," Insper Working Papers wpe_321, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.

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