CityDataLab estimates a rolling cross-sectional regression on every London residential transaction since 1995. Instead of producing a single price index, we publish the contribution of each property characteristic — floor area, energy rating, freehold tenure, location and more — so you can see why prices moved, not just that they moved. This post is a guided tour of what the model says about London right now and where to go for more detail.
The table below shows the latest 12-month return on each major London factor, computed geometrically on the cumulative coefficient curve. The Baseline Market figure is the quality-adjusted intercept — the bit of price growth that isn't explained by composition (different mix of houses vs flats, different postcode mix, etc.).
| Factor | Cumulative (since dataset start) | 12-month return |
|---|---|---|
| Baseline Market | +589.1% | +9.4% |
| Floor Area | -2.5% | -3.8% |
| Energy Rating | -9.8% | -4.1% |
| Freehold | +0.2% | +0.1% |
| House vs Flat | -2.4% | -1.2% |
| Location Premium | +1.5% | +0.0% |
| Price Tier | -1.2% | +0.1% |
Baseline Market returned +9.4% over the year to October 2025. The chart below shows the same figure on a rebased basis since January 2020 — useful for understanding the COVID run-up and the post-rate-rise correction together.
The London model is built on two public datasets joined by postcode:
county = GREATER LONDON, which gives us roughly five million transactions.Aligned methodology, aligned axes — one chart, four markets.
Open the global comparison →Methodology: rolling 3-month cross-sectional OLS of log(price/sqm) on hedonic property characteristics with postcode-area dummies as controls. See methodology & data sources for the regression specification.