Most property indices tell you that prices went up or down. We go further: breaking the market into the specific characteristics that drove each move, city by city, year by year.
Pick a city to explore its factor decomposition. Each city has its own hedonic model calibrated to local transaction data.
Standard price indices tell you how much the market moved. Hedonic decomposition tells you why it moved and what drove it.
Energy-efficient homes are increasingly valued by buyers. But how much more are they actually paying, and has that premium widened or narrowed over time? The Energy Rating factor tracks exactly this.
In boom markets, new-build premiums often compress as buyers flee to existing stock. In downturns, they can surge as developers hold prices. The New Build factor isolates this dynamic.
After Covid, buyers in London paid sharply more per extra square metre than before. In New York, the relationship between size and price shifted across boroughs. The Floor Area factor captures these repricing events.
The Baseline Market is the quality-adjusted price appreciation that cannot be explained by changes in what properties are being sold or how characteristics are priced. It is the closest thing to a pure market return. The chart below compares this baseline across cities from 2017 onwards, with all series re-indexed to zero at the start of 2017.
London has data from 1995 but is shown from 2017 here for a clean comparison. The full 30-year London series is available on the Factors page.
All cities indexed to 0 at January 2017. Divergence reflects genuine market conditions, not differences in what is being sold.
At each point in time we run a cross-sectional OLS regression of log(price per sqm) on property characteristics using a rolling 3-month window. This gives us the market's implicit price for each attribute: how much extra buyers paid for a new build, for a high energy rating, or for a larger floor area, in that specific window.
Tracking these coefficients over time produces factor returns: how much each characteristic contributed to price changes. A rising "Freehold Premium" factor means buyers are paying increasingly more for freehold versus leasehold tenure. A falling "New Build" factor means the new-build premium has compressed.
This is the same framework used in equity factor investing (think Fama-French), applied to real estate transactions.
Full methodologyA positive factor return means the market paid increasingly more for that characteristic. A negative return means the premium compressed.
| Factor | What it measures |
|---|---|
| Baseline Market | Pure market appreciation, composition-adjusted |
| Floor Area | Repricing of size (price per extra sqm) |
| New Build | New vs. existing property premium |
| Energy Rating | Green premium growth over time |
| Freehold | Tenure premium (London-specific) |
| Construction Period | Vintage effect: period homes vs. post-war |
Per-factor return and beta charts for the selected city. See how each characteristic has been repriced since the first data point, and how stable the OLS coefficient has been over time.
Open FactorsCross-section views: factor correlation matrices, stacked contribution charts, and volatility. Useful for understanding which factors move together and how market regimes shift over time.
Open TrendsHow the regression is specified, how factors are selected, the quality controls applied, and the data sources behind each city. Read this if you want to understand what the model can and cannot capture.
Read methodology