MSc thesis · Data Science & Society · Tilburg University · Jan 2024
Comparative Analysis of ML Algorithms for Time on Market (TOM)
Prior academic work on how long Dutch homes sit on the market before selling leaned almost entirely on Ordinary Least Squares (OLS). Using the Funda-Sold dataset of Dutch house sales from 2020–2023, I benchmarked OLS against Random Forest, XGBoost, LightGBM and CatBoost — combined with Recursive Feature Elimination with Cross-Validation (RFECV-DT) for feature selection and randomized-search hyperparameter tuning.
Dataset: Funda-Sold dataset — Dutch house sales from 2020 to 2023, sourced from Funda.nl, the leading real-estate platform in the Netherlands.
Headline results
Method
From Funda.nl listings to a predictive TOM model.
- 01
Sourced the Funda-Sold dataset (2020–2023) of Dutch residential listings and cleaned it in Python.
- 02
Engineered features and reduced dimensionality with RFECV-DT, converging on 18 optimal variables.
- 03
Trained OLS as a baseline plus Random Forest, XGBoost, CatBoost and LightGBM with randomized-search tuning.
- 04
Compared performance on full and RFECV-reduced datasets using R², MAE and RMSE on a held-out test set.
Model comparison — test set (full dataset)
| CatBoostbest | 0.632 | 11.36 | 26.18 |
| Random Forest | 0.609 | 11.67 | 26.97 |
| LightGBM | 0.609 | 11.84 | 26.95 |
| XGBoost | 0.587 | 12.19 | 27.71 |
| OLS (baseline) | 0.435 | 14.96 | 32.41 |
- CatBoostbest
- R²
- 0.632
- MAE
- 11.36
- RMSE
- 26.18
- Random Forest
- R²
- 0.609
- MAE
- 11.67
- RMSE
- 26.97
- LightGBM
- R²
- 0.609
- MAE
- 11.84
- RMSE
- 26.95
- XGBoost
- R²
- 0.587
- MAE
- 12.19
- RMSE
- 27.71
- OLS (baseline)
- R²
- 0.435
- MAE
- 14.96
- RMSE
- 32.41
Source: Table 5, test-set performance on the full Funda-Sold dataset. Tap a column header (or a sort chip on mobile) to reorder.
What actually drives Time on Market
The variables that mattered — across every model.
- 01Construction status (existing vs. new build)
- 02Roof type
- 03Volume (m³)
- 04Plot area (m²)
- 05Building-related outdoor area (m²)
- 06Asking price per m² & last asking price
- 07Living space (m²)
- 08Type of residence
Takeaway
CatBoost outperformed OLS on every metric and remained the strongest model on both the full and RFECV-reduced datasets. Crucially, the top drivers — construction status, roof type, volume, plot area, outdoor area — stayed stable across every feature-selection method, giving the finding methodological weight beyond the raw metrics.