How has the market repriced each property characteristic over time? Each factor below isolates one attribute and tracks its contribution to price changes.
The two charts below give the big picture. The first shows the Baseline Market: pure price appreciation stripped of all compositional effects. The second shows how every tracked factor contributed on top of that baseline.
The intercept of the rolling OLS, cumulated over time. This is the market return you would have earned by holding a perfectly average property. It rises when the market appreciates for reasons the model cannot attribute to any tracked characteristic.
Each line is the running sum of one factor's period contributions. A factor that trends upward means the market has been paying progressively more for that attribute. A factor that trends downward means the attribute has been repriced lower over time.
The cumulative repricing of this characteristic since the model's start. A flat line means the market has priced this attribute consistently. A rising line means it has become progressively more valuable. The number in the header shows where the line ends today.
The raw OLS coefficient in each rolling window. Red shading indicates windows where the p-value exceeded 0.10, meaning the estimate was not statistically reliable. A volatile beta often reflects a thin market or a characteristic that is highly collinear with others in that period.
Where available, this shows how the mix of sold properties has shifted over time. It helps distinguish repricing (the market paying more for the same thing) from composition change (more of a different type being sold).
Use the city switcher above to compare the same factors across London, New York, Paris, and Singapore. Factor names are standardised where the underlying characteristic is the same; factors that only exist in one market are unique to that city's model.