For What It’s Worth: Measuring Land Value in the Era of Big Data and Machine Learning

This paper develops a new method for valuing land, a key asset on a nation’s balance sheet.

The method first employs an unsupervised machine learning method, which means clustering, to discretize unobserved heterogeneity, which will then be combined with a supervised learning algorithm, gradient boosted trees (GBT), to obtain property-level price predictions and estimates of the land component.