What question did this study set out to answer?

This research aims to evaluate how different measurements of urban green spaces impact housing price predictions using machine learning.

June 3, 2026Open Access

Assessing the Predictive Power of Urban Green Spaces in Machine Learning Models for Chicago Housing Prices

Key Points

This research aims to evaluate how different measurements of urban green spaces impact housing price predictions using machine learning.
Compared predictive power of park proximity and NDVI in housing price models.
Utilized real estate big data and GIS analysis in the evaluation.
Applied machine learning techniques to analyze the data.
NDVI was found to be a strong predictor of housing prices for single-family homes.
Housing prices were more accurately predicted on the urban fringe using NDVI compared to park proximity.

Abstract

Urban Green Space (UGS) plays an important role in the urban environment. This study compares the predictive power of two types of UGS measurements — park proximity and NDVI — in the machine learning models to predict housing prices in Chicago. By incorporating real estate big data, GIS analysis, and Machine Learning techniques, the result indicates NDVI is a strong predictor for single-family residential properties on the fringe of the urban core.

Bookmark

View Full Paper