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 Singapore home, public or private. 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. 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).
Jump to any factor — cumulative return to date is shown on each card.
Singapore factors are fitted across both housing markets - HDB resale flats and URA private residential sales - over the window where both feeds exist. The Private vs Public factor carries the spread between them. Room count applies to HDB rows only, since URA publishes none. Use the city switcher above to compare with the other cities.