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AI for Real Estate Market Intelligence

Real Estate – Data Science & R&D

Market forecasting with spatial and time-series ML, CNNs; automated ETL, executive dashboards for confident investment.

RMSE < 0.1

Key Indicators Precision

R² > 0.8

Model Accuracy

80%

Confidence Intervals

Automated

Data Pipelines

Real Estate Impact

Advanced forecasting and optimization for real estate investment

Project Details

Client

Upfund

Sector

Real Estate – Data Science & R&D

Year

2024

Key Technologies

PythonXGBoostPostGISAirflow

Mission

Senior Lead Data Scientist – Real Estate Forecast & R&D

Real Estate Forecasting & Optimization

Long-term mission / Paris, France

ML: Forecasting real estate indicators via geospatial statistics and machine learning models, with 2D CNN testing for rasterized map analysis and geographic embedding integration. DE & DA: Data pipeline design (scraping, ETL), preprocessing / EDA. R&D: Scientific framing of issues (hypotheses, protocols), model synthesis.

Contexte & Environnement

Upfund, a real estate investment specialist, faced major challenges in real estate market forecasting and investment decision support. The complexity of residential and commercial real estate markets, combined with strong geographical and seasonal variability, required advanced predictive models.

Team

Senior Lead Data Scientist with focus on R&D and forecasting

Environment

Python with PostgreSQL/PostGIS, AWS S3, Airflow orchestration

Objectifs Cles

1Geospatial statistics and ML models for real estate forecasting (RMSE < 0.1, R² > 0.8)

22D CNN testing for rasterized map analysis with geographic embedding integration

3Probabilistic models with 80% confidence intervals for uncertainty measurement

4Automated data pipeline design (scraping, ETL) with continuous deployment

5State-of-the-art building and knowledge centralization for spatial statistics and time series

Technologies & Infrastructure

PythonPandasNumPyGeoPandasscikit-learnXGBoostLightGBMSARIMAProphet2D CNNAirflowDockerPostgreSQLPostGISAWS S3MLflowTableauPlotly

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