Eugenio Sanchez Pelayo and Gary Harper

UK house prices rose quickly during the pandemic. Earlier Bank of England analysis linked part of that rise to buyers paying more for houses than they would for a flat with similar characteristics, but could not show whether this revaluation would persist. With four more years of data, we find that it did. We also find London’s relative weakness began before Covid continued, helping explain the particularly weak performance of London flats. Finally, we decompose annual house-price growth to see what drove the cycle. Changes in the prices associated with property type and location contributed most during the pandemic boom, transaction composition accounted for most of the 2023–24 fall, and both contributed to the recovery in 2025.

The House Price Index (HPI) can move for very different reasons, and those reasons may have different implications. House prices feed into mortgage borrowing, collateral values and affordability, so it is important to look beneath the headline movements. The index can move because the prices associated with particular property characteristics have changed, such as houses becoming more expensive relative to similar flats. But it can also reflect persistent changes in the types of homes being sold and therefore in the weights used to construct the index. Identifying which force is at work helps us assess whether a movement is broad-based, concentrated in particular parts of the market, or more likely to persist or unwind. Fazio and Harper used transaction-level data matched to property characteristics from Energy Performance Certificates to show that part of the pandemic boom reflected houses becoming more expensive relative to otherwise similar flats, consistent with remote work changing what households wanted from a home. But their data ended in 2021, leaving open whether that revaluation unwound as pandemic conditions faded. We extend the analysis by four more years to test whether it persisted and to examine the longer-running weakness of London.

Our framework follows the broad approach used to construct the Office for National Statistics (ONS) HPI. It starts with monthly ‘hedonic regressions‘, a standard way of relating a property’s observed characteristics to the price paid. In each month, we regress the logarithm of prices on property type, number of rooms, floor size, new-build status, region, ONS Local Authority District and output area groups. In plain English, this estimates how much more or less was paid for each characteristic, holding the others constant. Where a characteristic is compared with a reference group, we describe the estimated effect as a premium or discount: for example, houses relative to flats, or regions relative to London. We then track those estimates over time and weight them by each combination’s share of transactions in the previous period to build a model-based HPI. This index is the fitted value implied by the model after controlling for observed property characteristics, allowing us to compare its movements with the official HPI and then decompose the drivers of those movements.

Chart 1: Our framework tracks the official house price cycle closely

Model-based and ONS house price growth relative to 12 months earlier

The model-based index captures the broad shape of the ONS HPI. The two series separate most in the unwind and recovery, when our index falls further in December 2023 and rises more sharply in 2025. The discrepancy may partly reflect different inputs: the ONS draws property characteristics from Valuation Office Agency records rather than Energy Performance Certificates.

Houses stayed expensive relative to flats

During the pandemic, buyers paid more for houses relative to otherwise similar flats, and that increase has persisted. We estimate it by comparing prices paid for different property types holding size, rooms, new-build status and location constant.

Chart 2: The pandemic revaluation of houses has persisted

Estimated house-over-flat premia from the monthly model, relative to otherwise similar flats

Detached houses already sold for substantially more than comparable flats before the pandemic, and the estimated premium over flats subsequently rose across all three house types. These increases have not unwound by the end of our sample. The premium held through the end of the stamp duty holiday (July 2020 to September 2021, with full relief ending in June 2021), the 2022 rise in mortgage rates and a subsequent period of lower transaction volumes. That said, the latest increase from June 2025 should be read with caution, as late registrations, especially for new builds, can revise the data.

London flats were hit by both shifts

The continued relative underperformance of London is only partially explained by the house-over-flat premium. Prices outside London had also been gaining ground relative to London before the pandemic. Once other characteristics are considered, London’s underperformance appears to have started around 2016–17 and continued after Covid. The two differ in timing: the house-over-flat premium rose sharply during the pandemic, while London’s underperformance was more gradual and longer-running.

Chart 3: London’s relative weakness predates the pandemic

Estimated regional discount from the monthly model, relative to otherwise similar properties in London

Most regions became less discounted relative to London, so upward movements in Chart 3 mean comparable properties outside London were gaining ground. The narrowing was largest in Wales, the North West and the Midlands, with Yorkshire and The Humber moving similarly. The South East, East and South West stayed closer to London throughout, while the North East changed little and remained the cheapest relative to London by the end of the sample.

This helps explain why London flats were particularly weak. They sat on the wrong side of both shifts: flats underperformed houses, and London underperformed most other regions.

Decomposing the boom, unwind and recovery

To see what drove growth at different points in the cycle, we compare the model-based index with counterfactual versions where one input is held at its value from 12 months earlier. That input can be either the estimated prices associated with property characteristics or the composition of transactions, meaning how the index is weighted across the homes sold. The approach builds on Fazio and Harper, who fixed selected regression coefficients at their pre-pandemic values. Unlike Fazio and Harper, who compared outcomes with a fixed pre-pandemic baseline, we focus on the drivers of year-on-year growth. We therefore compare each period with the preceding 12 months, so that the decomposition is directly aligned with the annual growth rates being measured.

Chart 4 summarises the decomposition across three phases of the cycle: the pandemic boom, when average annual model-based growth was 8.1%; the 2023–24 unwind, when growth averaged -1.8%; and the recovery in 2025, when growth averaged 3.7%.

Changes in the estimated prices associated with property type and location accounted for just over half of growth during the pandemic boom and around four fifths of the recovery. Transaction composition also supported growth in both periods.

Chart 4: Decomposition of growth drivers

Contributions to average annual model-based house-price growth, percentage points.

Notes: ‘Property type and location characteristics’ combines changes in the estimated prices associated with property type, region and neighbourhood. ‘Other factors’ combines the remaining model components, including rooms, floor size, new-build status and the reference-property contribution.

This decomposition is best understood as an accounting exercise rather than a causal estimate of why prices changed. It depends on the characteristics recorded in the data and on the model we use to estimate the prices associated with them. It cannot capture changes in unobserved property quality, or shifts in demand that are not reflected in those recorded characteristics.

Conclusion and policy implications

Before Covid, London property prices were already weakening relative to the rest of the country, and flats were a weaker part of the market. The pandemic reinforced that split. The premium paid for houses over flats rose and remained high, while London’s relative decline, underway since 2016, continued. That left London flats exposed to both shifts.

The persistence of the house-over-flat premium helps answer a question left open by the earlier analysis. If the revaluation had been a temporary response to pandemic conditions, some of it might have unwound as those conditions faded. So far, we find little evidence that this has happened. This does not mean that the premium cannot reverse, or that wider house prices will not respond to interest rates, incomes and other macroeconomic developments. But it suggests that the fading of pandemic conditions alone has not restored the previous relative-price pattern.

The decomposition also helps us interpret movements in headline house-price growth. During 2023–24, the decline in our model-based index mainly reflected a shift in transactions towards homes with fewer higher-priced characteristics, rather than a broad fall in the estimated prices associated with those characteristics. These two developments do not necessarily carry the same implications for the wider housing market. Distinguishing between them provides a clearer picture of whether headline movements are broad-based or concentrated in particular property types and locations, which is relevant when monitoring housing affordability, mortgage collateral and financial-stability risks.

Eugenio Sanchez Pelayo and Gary Harper work in the Data and Statistics Division.

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