What the client needed
Local councils in Zambia need efficient and fair ways to identify and value properties if property tax is to become a sustainable source of revenue.
The Local Government Revenue Initiative (LoGRI), hosted at the University of Toronto's Munk School, wanted to pilot different approaches to property identification and valuation in Mansa and Chipata, and to learn how mapping affects residents' attitudes towards property tax.
How M31 Research delivered
M31 Research is delivering two workstreams. For mapping and valuation, technicians digitise built properties from satellite imagery in QGIS, enumerators ground-truth property characteristics with CAPI, and real estate experts value a 5% sample.
In Mansa we are also implementing a spatial regression discontinuity design: a baseline survey of about 2,000 respondents, endline and follow-up surveys with tax-salient messaging, a mock election module using replica ballots, and focus group discussions. Backchecks, 10 to 20% re-interviews and daily data submission safeguard quality.
What we delivered
- QGIS mapping of built properties in both towns
- CAPI ground-truthing of property characteristics
- Expert valuation of a 5% sample
- Baseline, endline and follow-up surveys in Mansa
- Mock election module
- Focus group discussions
- Validated spatial and survey datasets
What changed for the client
The assignment will deliver validated spatial datasets, cleaned property and valuation data, and experimental evidence on how mapping and messaging shape attitudes to property tax. These outputs will help LoGRI and Zambian councils pilot more efficient, sustainable and equitable property tax administration.
