Using machine learning and remote sensing to value property in Rwanda
Brimble, Paul, Patrick McSharry, Felix Bachofer, Jonathan Bower, and Andreas Braun. 2020. "Using machine learning and remote sensing to value property in Rwanda." Policy paper, International Growth Centre, London.
Property valuation models can achieve mass valuation transparently and cheaply. This paper develops a number of property valuation models for Kigali, Rwanda, and tests them on a unique dataset combining remote sensing data and infrastructure and amenities data for properties in Kigali, with sales transaction data for 2015. We use a machine learning approach, Minimum Redundancy Maximum Relevance, to select from 511 features those that minimise ten-fold cross validated Mean Absolute Error. Cross validated diagnostics are used to eliminate overfitting given that our goal is to generate a model that can be used to extrapolate value estimates out of sample.
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