What really drives property prices?

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.

How it works

Choose a city

Pick a city to explore its factor decomposition. Each city has its own hedonic model calibrated to local transaction data.

London flag
London property prices

Index Return -5.0% in the 12 months to Jul 2026. Dearest postcode area: West Central London, £11,022/m².

London overview Factors
New York flag
New York property prices

Index Return +3.8% in the 12 months to Aug 2026. Dearest borough: Manhattan, $8,914/m².

New York overview Factors
Paris flag
Paris property prices

Index Return +2.6% in the 12 months to Dec 2025. Dearest arrondissement: 6e — Luxembourg, €14,154/m².

Paris overview Factors
Singapore flag
Singapore property prices

Index Return +1.2% in the 12 months to Aug 2026. Dearest HDB town: Queenstown, S$11,398/m².

Singapore overview Factors
Taipei flag
Taipei property prices

Index Return -0.1% in the 12 months to Apr 2026. Dearest district: 中正區, NT$362,367/m².

Taipei overview Factors
Seoul flag
Seoul property prices

Index Return +17.4% in the 12 months to Aug 2026. Dearest district (gu): 강남구, ₩37,555,043/m².

Seoul overview Factors
Tokyo flag
Tokyo property prices

Index Return +11.8% in the 12 months to Jan 2026. Dearest ward or city: 港区, ¥1,870,833/m².

Tokyo overview Factors
Lyon flag
Lyon property prices

Index Return +0.2% in the 12 months to Dec 2025. Dearest arrondissement: Lyon 6e, €5,427/m².

Lyon overview Factors
Osaka flag
Osaka property prices

Index Return +10.1% in the 12 months to Jan 2026. Dearest ward or city: 大阪市北区, ¥987,500/m².

Osaka overview Factors
Busan flag
Busan property prices

Index Return +3.2% in the 12 months to Sep 2026. Dearest district (gu): 수영구, ₩8,670,860/m².

Busan overview Factors

The questions we answer

Standard price indices tell you how much the market moved. Hedonic decomposition tells you why it moved and what drove it.

Did the green premium grow?

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.

Was it new-builds or old stock?

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.

Is space being repriced?

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.

Market returns across cities

Each city's Index is a constant-quality price index: it reprices a slowly updated basket of homes, so a shift in what sells (bigger homes, cheaper districts) does not move it. The chart compares each city's Index Return since January 2017.

London has data from 1996 but is shown from 2017 here for a clean comparison. The full 30-year London series is on the Trends page.

Index Return by city since January 2017

Divergence reflects prices, not differences in what is being sold.

How the model works

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 gives two views. Each factor's Factor Return is the change in its Premium, the price gap between two otherwise identical homes that differ in that one characteristic: a rising House vs Flat Factor Return means buyers pay increasingly more for a house over a flat of the same size. The constant-quality Index splits into the Baseline Market and each factor's Factor Contribution, and Composition explains why the average price actually paid differs from it.

This is the same framework used in equity factor investing (think Fama-French), applied to real estate transactions.

Full methodology
What each factor tells you

A positive Factor Return means the market paid increasingly more for that characteristic. A negative one means its Premium compressed.

FactorWhat it measures
Baseline MarketThe part of the Index Return no factor explains
SizeHow the price per sqm of a larger home compares with a smaller one
Recent BuildPremium of recently built homes over older ones
Energy RatingPremium per EPC band
Outer vs Inner LondonOuter London against an otherwise identical central home
Construction EraVintage effect: period homes vs. post-war
Factor Analysis

Each factor's Factor Return and Transacted Distribution for the selected city: how the market has repriced each characteristic, and how much of it has been selling.

Open Factors
Trends

Cross-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 Trends
Methodology

How 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