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Land value dynamics and the spatial evolution of cities following COVID 19 using big data analytics
Erez Buda1, Dani Broitman1, Daniel Czamanski2
1Faculty of Architecture and Town Planning, Technion - Israel Institute of Technology, Haifa, Israel.
The Annals of Regional Science
|June 22, 2022
Summary
This study developed a land-use forecasting model using geo-referenced data to predict urban spatial configuration. It simulates post-COVID-19 scenarios, revealing potential long-lasting impacts on city development and land values.
Area of Science:
- Urban Planning and Spatial Analysis
- Geographic Information Systems (GIS)
- Econometrics
Background:
- Urban development is influenced by land-use regulations, building characteristics, and real estate transactions.
- Understanding spatial dynamics and development pressures is crucial for accurate urban forecasting.
- The COVID-19 pandemic introduced behavioral changes potentially impacting future urban spatial structures.
Purpose of the Study:
- To present a calibrated land-use forecasting model for a major metropolitan area.
- To estimate historical spatial dynamics of land values and identify development pressures.
- To simulate and forecast future urban spatial configurations, considering pandemic-induced behavioral shifts.
Main Methods:
- Calibration of a land-use forecasting model using extensive geo-referenced data, including land parcel information, regulations, building characteristics, and real estate transaction data.
- Estimation of spatial land value dynamics and identification of development pressure zones.
- Simulation of future urban spatial structures based on enduring behavioral changes observed during the COVID-19 pandemic.
Main Results:
- The model successfully estimated historical spatial dynamics of land values and pinpointed areas under development pressure.
- Simulations provide forecasts of future urban spatial configurations under different pandemic impact scenarios.
- Comparison between actual and forecasted scenarios highlights potential divergences from pre-COVID-19 trends.
Conclusions:
- The developed model offers plausible forecasts of urban spatial configuration, incorporating real-world data and simulated pandemic effects.
- The study provides insights into how enduring pandemic-related behavioral changes may reshape metropolitan spatial structures.
- Forecasting models are essential tools for interpreting urban dynamics and planning for future development trajectories.
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