Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values

Abstract

Updated cadastral land values are a matter of critical importance for local governments: higher revenue of property taxes, more equitable treatment to taxpayers, a fundamental input in the design of public policies related to access to land and housing for the most vulnerable and a key feature in land value capture strategies to finance public infrastructure, to name just a few public policies that require correct valuations of land. However, in Latin America, outdated cadastral values are common to most cities. The reasons for this can be found in the complexity of the mass appraisal process, lack of institutional and fiscal capacity to undertake it and bureaucratic resistance to its implementation.The objective of this paper is to present a mass appraisal methodology that uses only free and open data to achieve robust urban land valuations. Information from the OpenStreetMap Project is used to generate several land variables. In addition, the Global Human Settlement Layer of the European Commission is used to determine the level of consolidation of urban sprawl. Land value data were obtained from the Mapa de Valores de América Latina, a collaborative initiative that systemizes more than 68,000 data from more than 900 cities.This information is used to train three tree-based machine learning models: Random Forest, Quantile Random Forest and Gradient Boosting Model. The results support the viability of the proposed strategy, simplifying the mass appraisal process in terms of costs, time and complexity of the information used.