Building the City: From Slums to a Modern Metropolis

Abstract

We model the building of a city, estimate parameters of the model, and calculate welfare losses from institutional frictions encountered in changing land-use. We distinguish formal and slum construction technologies; in contrast to slums, formal structures can be built tall, are durable, and non-malleable. As the city grows areas are initially developed informally, then formally, and then redeveloped periodically. Slums are modelled as a technology choice; however, institutional frictions in land markets may hinder their conversion to formal usage that requires secure property rights. Using unique data on Nairobi for 2003 and 2015, we develop a novel set of facts that support assumptions of the model, estimate all parameters of the model, and calculate welfare losses of conversion frictions. We track the dynamic evolution of the city and compare it with model predictions. In the core city formal sector, about a third of buildings were torn down over 12 years and replaced by buildings on average three times higher. For slums in older areas near the centre, even after buying out slumlords, overcoming institutional frictions would yield gains amounting to about $18,000 per slum household, 30 times typical annual slum rent payments.

Examining the Effect of Squatter Settlements in the Evolution of Spatial Fragmentation in the Housing Market of the City of Buenos Aires by Using Geographical Weighted Regression

“…Henderson, Regan, and Venables [37] developed a model of the city's built environment growing in Nairobi by distinguishing between the formal and slum construction. The formal sector is more dynamic and involves investment decisions based on expected future rents.…”

Ask a Local: Improving the Public Pricing of Land Titles in Urban Tanzania

“…Much land in urban Africa is allocated low values of built capital, remains unplanned, and is settled under informal property rights ( UN Habitat 2016, Lall et al 2017. And yet, the development of these cities depends on the establishment of strong property rights (Henderson et al 2021, Djankov et al 2020. Formalisation creates transparency in prices enabling functional urban land markets, and improved property records facilitating taxation (Collier et al 2017).…”

Where are All the Jobs? A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries

“…For instance, Henderson et al (2021b) rely on satellite based population data to identify urban areas and estimate wages for cities in six SSA countries. Focusing on building characteristics, Lall et al (2021b) and Henderson et al (2021a) rely on building height data to investigate urban form for 397 cities and identify slum areas in Nairobi respectively. Another valuable source of data is derived via digital (often crowd-sourced) maps, e.g., Open Street Map (OSM).…”