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Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values

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  • Carranza, Juan Pablo
  • Piumetto, Mario Andrés
  • Lucca, Carlos María
  • Da Silva, Everton

Abstract

Updated cadastral land values are a matter of critical importance for local governments: higher revenue of property taxes, more equitable treatment to taxpayers, a fundamental input in the design of public policies related to access to land and housing for the most vulnerable and a key feature in land value capture strategies to finance public infrastructure, to name just a few public policies that require correct valuations of land. However, in Latin America, outdated cadastral values are common to most cities. The reasons for this can be found in the complexity of the mass appraisal process, lack of institutional and fiscal capacity to undertake it and bureaucratic resistance to its implementation.

Suggested Citation

  • Carranza, Juan Pablo & Piumetto, Mario Andrés & Lucca, Carlos María & Da Silva, Everton, 2022. "Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values," Land Use Policy, Elsevier, vol. 119(C).
    • Handle: RePEc:eee:lauspo:v:119:y:2022:i:c:s0264837722002381
    DOI: 10.1016/j.landusepol.2022.106211
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    References listed on IDEAS

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    Cited by:

    1. Gao, Feng & Yi, Shiyi & Li, Xiaonuo & Chen, Weiping, 2024. "Ensemble intelligence algorithms and soil environmental quality to model economic quantity of land resource allocation and spatial inequality," Land Use Policy, Elsevier, vol. 141(C).
    2. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).

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