A New Approach to Property Valuation Using Remote Sensing Data and Machine Learning in Kigali, Rwanda

Kurzfassung

Valuation of land in countries of the Global South can contribute to the achievement of various important global, regional and national development goals such as: sustainable urban development, promoting responsible investments and addressing conflicts often associated with large-scale land investments. Taxes on land and property can serve as an important tool to finance the urbanization through the recovery of profit generated by the increasing value of property as a result of public infrastructure investment. The revenue is critical for municipalities and other local authorities to upgrade informal settlements, support the resettlement of displaced people and for infrastructure investment and development projects. In addition, it is vital for improving transparency in opaque land markets. Rwanda had relative low land taxes which were recently increased. A new property tax law will come into force on 1st January 2019. Along with Rwanda’s good land governance, the new tax law may represent an opportunity to generate municipal revenue. Land value capture can be determined by a list of factors such as public investment in infrastructure, land use regulations, demographic change and economic development, private investment in land, and locational properties. Earth observation (EO) and geospatial analysis provide data, tools and methods that help to explain the relation of land values and such explanatory variables. This study trials a property valuation methodology for Rwanda's capital, Kigali by applying machine learning techniques to parcel transactions data from 2013 to 2015 and remote sensing data from 2009 and 2015 on building footprints. The building information (building footprint and building type) is derived from aerial images (2009) and a Pleiades scene (2015). Additionally, spatial parameters describing the spatial interrelations of properties within the city are derived from a multi-source dataset and include distance-based values (distance to CBD, schools, public transport, etc.), accessibility, location parameters (topographic position) and neighbourhood statistics (green area, urban structure type, density values, etc.). This approach can help to understand the key determinants of land and building values and be used to create a database with accurate estimates of property values for each parcel in Kigali. The study is conducted with the local authorities, led by the Ministry of Finance and Economic Planning (MINECOFIN). This database will be used to provide the local tax administration (Rwanda Revenue Authority) with an important tool for collecting property tax revenues as the main method of property valuation in Rwanda is through taxpayer self-assessments. In order to support the tax administration in determining the validity of these self-assessments, the estimates from this database can be used to trigger certified counter-valuations if necessary with the aim of raising domestic tax revenues. Therefore, this study highlights how remote sensing methodologies can be effectively used to complement traditional valuation techniques through an application in Rwanda.