The chart below each heading overlays the cumulative return of one factor across every city that has it, all rebased to 0% at January 2017 so divergence reflects market conditions rather than differences in dataset length.
Jump to any factor — the count shows how many of the four covered cities have it.
Quality-adjusted price appreciation that cannot be explained by changes in what is being sold or how characteristics are priced. The closest thing to a pure market return.
Z-score of each area's trailing-12-month median price/sqm against the city-wide distribution. Rising = expensive areas pulling away from cheap ones (gentrification or polarisation); falling = convergence.
Septile bucket of each transaction's price/sqm relative to the trailing-12-month distribution, scored from -3 (cheapest) to +3 (dearest). Rising = the most expensive deals are commanding a growing premium over hedonic predictions; falling = price convergence.
U-shaped vintage premium centred on each city's mid-century cohort. Positive cumulative return means stock far from that cohort (older period homes and recent builds) has outperformed mid-century stock.
Encoding is city-specific (centred on band 4 for London/NYC, band 3 for Paris, band 2 for Singapore) because each city's "middle" era is different. The series share an interpretation but not a unit, so read this chart for direction and timing rather than absolute magnitude.
These factors appear in two or three city models but not all four. Each chart shows only the cities where the factor is present.
Repricing of property size: how much the market is paying for an additional square metre over time. A rising line means space has become progressively more expensive.
Encoding differs by city. London uses a linear total_floor_area (price per extra sqm); Paris uses log_floor_area so the per-sqm premium is non-linear in size; Singapore uses log_floor_area_sq_c80, a squared-log deviation from the typical 80sqm 4-room HDB flat. Read the chart for direction and timing of size-premium shifts, not for absolute magnitude. NYC is omitted because its size factor is log_units_res (number of residential units per building), not floor area.
How the market reprices a step change in a property's energy rating. A rising line means the green premium has expanded and buyers are paying progressively more per rating step.
London uses a 3-level collapse of EPC ratings (G = -1, C-F = 0, A-B = +1). Paris uses GES (greenhouse-gas emissions) on a continuous letter scale. Both are interpreted in the same direction (positive = green premium widening) but the units differ. NYC and Singapore have no comparable energy factor in their selected models.
Rooms per sqm: the market preference for partitioned layouts over open-plan, holding total size constant. Positive cumulative return means divided layouts have grown more valuable.
Both cities use the same rooms_per_sqm encoding, so this comparison is directly apples-to-apples in units. NYC and Paris do not have this factor in their selected model.
Linear ageing effect: how the market reprices a building per additional year of age, holding construction-era cohort constant. Captures ongoing depreciation versus renovation cycles.
NYC uses a linear building_age; Singapore uses log_building_age because HDB's 99-year leases compress remaining tenure faster in the early years than the late ones. London and Paris encode age primarily through Construction Period bands rather than a separate linear factor.