ChrionML transforms Zillow’s published ZHVI market histories into quality-checked datasets, independently evaluated forecasts, and decision-ready findings—with transparent methods, limitations, and source links.
The same 80-month protocol produces a split result: Cambridge carries the higher forecast value and modest month-ahead momentum, while Ithaca has delivered stronger cumulative appreciation.
Preview the Week 2 box score →Week 2 · Four findings measured with one repeatable protocol
The September estimate is $888,570 for Cambridge versus $289,770 for Ithaca. This compares market level—not affordability or investment quality.
Inspect the forecast →The selected models imply a modest +0.19% move for Cambridge and a flat estimate for Ithaca. The comparison is directional, not a guarantee.
Review uncertainty →Ithaca rose approximately 29.5% over the observed window versus 17.8% for Cambridge, despite its much lower current price level.
Explore the trend →Drift reduces Cambridge’s baseline MAE by 6.8%. In Ithaca, the last-value baseline beats Drift—evidence that model choice should remain local.
Compare all models →Boston 02114 vs Providence 02903 · Final
The September estimate is $779,306 for Boston versus $438,308 for Providence. The gap describes market level, not a universal market winner.
Inspect the final forecast →The selected Drift models imply +0.44% month-ahead growth for Providence versus +0.06% for Boston—a 0.38 percentage-point advantage.
Review uncertainty →Providence gained approximately 53.2% from January 2020 through August 2026, compared with 4.9% for Boston’s condo/co-op series.
Explore the trend →Drift improves MAE by 4.4% in Boston and 24.2% in Providence versus the last-value baseline, using identical rolling evaluation rules.
Compare the models →Real data. Reproducible evaluation. Operational context.
ChrionML locates the appropriate Zillow ZHVI geography and housing-type series, compiles and quality-checks the monthly dataset, compares time-series candidates, runs chronological backtests, quantifies uncertainty, and publishes the findings.
Read the methodology →A platform proof of concept that converts experimental workflows into governed, versioned, observable services consumable by scientists and autonomous tools.
Explore the platform →Zillow publishes the underlying ZHVI series. ChrionML performs property-type selection, dataset construction, QA, modeling, backtesting, uncertainty analysis, interpretation, and publication. Each box score links to its market sources.
Review the standard →Scale deliberately, publish transparently
The objective is not simply to publish 64 forecasts. It is to build a comparable evidence base across markets—using consistent evaluation, clear local context, and a production workflow that can scale without sacrificing rigor.
Next phase: validate the publishing workflow across additional Northeast markets before expanding regionally.
We are building partnerships around local data access, independent model validation, sponsored city research, and applied machine-learning systems. Every engagement starts with a defined question and an evidence plan.
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