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Machine learning model predicts Dakar residential rents with high accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine learning pipeline to predict residential rents in Dakar, a city where over half of households are renters. An original dataset of 1,507 rental listings was created and enriched with new features. An optimized XGBoost model achieved the best performance, with an R^2 of 0.847, and feature importance analysis using SHAP values revealed that location is a highly influential predictor. AI

IMPACT Provides a benchmark for applying machine learning to real estate prediction in emerging markets.

RANK_REASON The cluster contains an academic paper detailing a machine learning methodology and its results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning model predicts Dakar residential rents with high accuracy

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The cluster contains an academic paper detailing a machine learning methodology and its results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Amadou Tidiane Kassa Diallo ·

    Predicting Residential Rents in Dakar Using Machine Learning

    arXiv:2608.30865v1 Announce Type: new Abstract: Dakar's residential rental market remains poorly documented despite its economic and social importance: 54.4% of households are renters, compared to 23.3% nationally. This study develops a complete machine learning pipeline to predi…

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