Research in Brief

142

Using Machine Learning to Create a Property Tax Roll: Evidence from the City of Kananga, D.R. Congo

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Using Machine Learning to Create a Property Tax Roll: Evidence from the City of Kananga, D.R. Congo

Property taxation is often the primary source of government revenue at the local level, and is essential for provision of local public goods. However, it remains one of the most under-utilised taxes in developing countries. This is partly because taxing properties requires mapping and assessing the value of properties, which is complex and expensive. Only 39 per cent of non-OECD countries and 15 per cent of sub-Saharan African nations have mapped their largest city’s private plots.

Several approaches have been proposed to map and value properties. Some countries rely on in-person appraisal visits, but, while accurate, these are typically costly and prone to corruption. For this reason, many countries, such as Pakistan, Sierra Leone, and Malawi, have instead adopted simplified valuation methods. The most common approach is points-based valuation, which consists of assigning points based on the surface area of the land and buildings. Additional points are awarded for positive features, and deducted for negative features.

Summary of ICTD Working Paper 176.

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Bergeron, A.; Fournier, A.; Kabeya, J.K.; Tourek, G. and Weigel, J.L. (2024) Using Machine Learning to Create a Property Tax Roll: Evidence from the City of Kanaga, D.R. Congo, ICTD Research in Brief 142, Brighton: Institute of Development Studies, DOI: 10.19088/ICTD.2024.101

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