Pre-print of Rey-Blanco, D., Arbués, P., López, F. A., & Páez, A. (2022). Using machine learning to identify spatial market segments. A reproducible study of major Spanish markets. Environment and Planning B: Urban Analytics and City Science. 23998083231166952

Predicting House Prices Based on Varied ML Methods Such as Random Forest and XGBoost

Use of web scraping, text analysis and hedonic regression model for property valuation task.

This repository contains the code and data related to the paper "Earthquake risk embedded in property prices: Evidence from five Japanese cities"

The project focuses on a critical and timely theme: assessing the impact of micromobility infrastructure on property values in London. This study sits at the intersection of urban planning, real estate economics, and sustainable transportation, reflecting a growing interest in how emerging mobility solutions contribute to urban life quality.