Real estate · Econometrics
CityDataLab
Hedonic factor models that separate genuine price growth from changes in what is being sold.
CityDataLab applies econometrics to the property market. Headline price indices are misleading because the mix of what sells changes over time — a market can look like it is rising simply because larger or better homes happened to trade. CityDataLab’s hedonic factor models strip that out.
By modelling price as a function of a property’s attributes, it isolates the true, like-for-like return on each characteristic — floor area, freehold versus leasehold, energy rating and location — across London, New York, Paris and more.
Genuine growth, not composition
The core idea is to separate price growth that is real from price growth that is just a change in composition. Hedonic regression does exactly this, and CityDataLab makes the results legible across cities and time.
- Hedonic regression on large open-property datasets.
- Like-for-like return on floor area, tenure, energy rating and location.
- Cross-city coverage — London, New York, Paris and beyond.
- A clear separation of true growth from changes in the mix of sales.
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