Code, models and data, shared publicly.

Data-quality tests for PySpark DataFrames, with results returned as a long-format DataFrame.

Computes a dwelling's social-housing points score and the maximum legal rent that follows from it, implementing the Huurcommissie policy books against the VERA sector data standard.

Looks up the heritage status of Dutch addresses against the Cultural Heritage Agency and Kadaster APIs — tens of thousands of objects at a time, asynchronously, and without an API key.

Tools the standard data-science stack leaves out — plotting helpers, decorators and an estimator switch for scikit-learn pipelines — published on PyPI with documentation on Read the Docs.

Exposes the heritage lookup as an MCP server, so a language model can resolve an address to a building and ask whether it is a listed monument.

Checks whether housing data conforms to the VERA sector standard, as a set of tests that plug into the PySpark test framework.

A cross-validator for grouped time series where the groups hold unequal numbers of observations — contributed to scikit-lego and shipped in its 0.6.14 release.

The heritage status of every addressable unit in the Dutch address register, resolved against the Cultural Heritage Agency and the Kadaster by a staged pipeline built on the monumenten package and published as one Parquet file, with the register behind each status named beside it.

A Streamlit app on Hugging Face Spaces that looks up a Dutch address and says whether it is a national monument, stands in a protected townscape or is a municipal monument, naming the register behind each answer.

A QLoRA fine-tune of Llama 3.1 8B, quantised to four bits, that reads a dwelling's description and answers with its woningwaardering points — regression handed to a language model rather than to a regressor.

A gradient-boosted regressor predicting a dwelling's woningwaardering points from six property features, tuned by randomised search under cross-validation and published with the predictor that loads it, so a dwelling can be scored without retraining.

A Gradio app on Hugging Face Spaces that runs the gradient-boosted model live: enter a dwelling's features and it returns the points it predicts, an interval around them, the maximum legal rent those points allow and the rent sector they put the dwelling in.

Descriptions of self-contained Rotterdam dwellings in a fixed format, each paired with the woningwaardering points Dutch law awards it — generated from the woningwaardering package's own evaluations and split for training.

Dwelling valuations from three housing corporations in one dataset — a collaboration set up between Beveland Wonen, Thuisvester and Woonstad Rotterdam, each publishing its own region — deduplicated per organisation and combined into shared train, validation and test splits.