This stage tests whether tightness today foretells price changes tomorrow. The dependent variable is future price growth; the key input is current tightness, with demand, supply, and the sold-price level alongside it.
Variables
- Tightness θ: search demand divided by listed supply, as defined above, the key predictor being tested.
- Search demand, listed supply, sold transactions: the counts behind tightness and the price, carried as inputs.
- Sold price: the Land Registry sold price for the area and year, a coarse area-level proxy weighted across the local authorities in each travel-to-work area.
- Annual price growth: the year-on-year change in the area’s sold price, and its one- and two-year-ahead values, the outcomes the lead-lag test predicts.
Data source
HM Land Registry sold prices, carried in the Rightmove listings feed, aggregated to the travel-to-work area, joined to the tightness series. Sold prices populate a majority of sale listings; the price is a weighted-median proxy rather than a hedonic index.
Summary statistics of the analysis variables
| Variable | Role | Mean | SD | Minimum | Maximum |
|---|---|---|---|---|---|
| Tightness θ (key predictor) | input | 3.60 | 1.73 | 1.04 | 17.71 |
| Search demand (enquiries) | input | 52,399 | 115,050 | 189 | 1,566,171 |
| Listed supply (listings) | input | 13,281 | 23,116 | 50 | 327,575 |
| Sold transactions (count) | input | 3,332 | 5,950 | 1 | 105,341 |
| Sold price (£) | input | £225,238 | £84,857 | £72,327 | £563,293 |
| Annual price growth | dependent | 2.2% | 6.7% | -32.9% | 21.0% |
| Price growth, one year ahead | dependent | 2.0% | 6.9% | -32.9% | 21.0% |
| Price growth, two years ahead | dependent | 4.7% | 10.4% | -48.0% | 28.5% |
Grain: one observation per travel-to-work area and year, 2016 to 2025. Price and the growth outcomes are defined on the sale market; the forward-growth variables thin toward the end of the window as the horizon runs past 2025.
Method
A panel of areas by year. Price growth a set number of years ahead is regressed on current log tightness, with area and year fixed effects and standard errors clustered by area; the coefficient is read across horizons. A reverse regression checks that the lead does not run the other way, and an out-of-sample nowcast tests whether the signal lowers forecast error.