Data Collection for The Local Government Revenue Initiative (LoGRI) Property Tax Survey

Project Background

The survey was part of the Local Government Revenue Initiative (LoGRI), led by the International Centre for Tax and Development (ICTD) in collaboration with M31 Research. The initiative aimed to develop policy-relevant tools to help local governments in Zambia collect local revenue more fairly, equitably, and transparently. The project focused on a property survey in Mansa, targeting 5% of properties (approximately 1,300–1,400 properties), stratified by neighbourhoods. The goal was to collect external property characteristics to inform property tax reform and enhance trust between local authorities and citizens​

Survey Methodology

The survey utilised a stratified random sampling approach, with GPS coordinates generated by LoGRI guiding property selection. Enumerators followed a "controlled random walk" methodology to reach designated properties. Data collection focused on external property attributes, including the number of floors, wall materials, and general condition. Enumerators recorded geolocation data using tablet computers preloaded with survey tools such as ODK​ . Data quality assurance mechanisms included:

• High-Frequency Checks (HFCs): Daily data validation and correction.
• Backchecking: Random revisits to validate key variables.
• Spot Checks: Supervisor observations of enumerator performance.
• GIS Integration: GPS mapping for verification and follow-ups​

Field Work

Fieldwork was conducted in phases, starting with securing approvals and community sensitisation to ensure local cooperation. Enumerators, supervisors, and backcheckers were then recruited and trained on survey protocols, data tools, and COVID-19 safety measures. During data collection, enumerators conducted daily surveys, uploading data to a central server, while supervisors ensured protocol adherence and data quality. A follow-up phase targeted approximately 944 properties to validate street-related information. COVID-19 safety protocols, including mask-wearing, sanitisation, and social distancing, were strictly enforced throughout.

Survey Results

The survey successfully covered the target sample of properties, with enumerators collecting critical data on property characteristics. Data cleaning efforts identified discrepancies, particularly in street-related variables, requiring targeted follow-up visits. Findings were presented in dissemination events in Lusaka and Mansa, where key insights and recommendations were shared with stakeholders.

Overall Outcome

The survey achieved its objectives of collecting high-quality property data to support LoGRI's policy goals. The collaboration between M31 Research and LoGRI demonstrated the effectiveness of combining local expertise with technical rigour. Insights from the survey are expected to contribute to improved local revenue systems, fostering greater transparency, trust, and accountability in property tax administration​.