Land value dynamics and the spatial evolution of cities following COVID 19 using big data analytics.

Abstract

In this paper, we present results of a land-use forecasting model that we calibrated with vast geo-referenced data of a major metropolitan area. Each land parcel includes information concerning regulations indicating permitted land-uses as well as the certain characteristics of existing buildings. Data concerning all real estate transactions include information about the assets and the price of the exchanges. Based on these data we estimated the spatial dynamics of land values in the metropolitan area over time and identified locations experiencing development pressures. This analysis allows us to forecast plausible futures of the urban spatial configuration. Taking the approach one step further, we propose simulations motivated by the natural experiment of COVID 19. We assumed that part of the behavioral changes observed during the pandemic will endure. The resulting simulations provide forecasts of the future spatial structure of the metropolitan area. Comparing the actual and the forecasted scenarios we interpret the spatial dynamics of the city as they would be if a business-as-usual-pre-Covid-19 scenario is realized, and possible trend changes if the impact of the pandemic is long lasting.