House sales in London SW10 take a few punches

The anatomy of SW10

Analyses of house sales often focus on the wider UK market. In this blog, we’ll take a deep dive into one of London’s more-than 100 postcode districts. We’ll draw on 10,000 property transactions to see how key events have shaped the market. The object of our focus will be SW10 which forms part of the Royal Borough of Kensington and Chelsea.

We’ll start with the anatomy of SW10. Over 80% of property transactions were for leasehold flats.  In contrast, detached freehold properties are a prized scarcity: Only 40 of the circa 10k transactions, over the past 20 years, were for detached properties.

SW10 House Sales by Estate and Property Type

The rise of house values

This plot shows the bulk of the transactions from the start of 1995 through to autumn 2017. Each dot is a transaction. I’ve focused here on those up to £2m. These account for close to 95% of the total. The excluded 5% range from £2m to a staggering £55m.

The most obvious characteristic of the SW10 market is the sustained growth in property value.  But there is a lot more to this picture on closer inspection.

House Prices Paid in SW10

The punch of the Financial Crisis, and a flurry of jabs

By overlaying the key events of the past 20 years, we see that the Financial Crisis in particular has had a dramatic effect on the market. The density of dots to the left is visibly different to that on the right. And the right-side reveals a tale of a market briefly evaporating at the beginning of the “credit crunch”, before tentatively starting to re-emerge.

By colouring the dots red and blue, we also see the tendency for freehold properties to secure higher prices than leasehold. Owning both the building and the land on which it stands, outright and in perpetuity, is clearly a significant benefit for freehold buyers.  There could also be a confounding factor of freehold properties tending to be larger on average, but the square-footage data associated with these transactions is unavailable to test if that is the case.

Another interesting feature of the data is the noticeable horizontal banding of dots at the £250k and £500k price levels. For most of the past 20 years, these have been key stamp duty thresholds.  More recently £250k, £925k and £1.5m have become the key break-points. Stamp duty is now a punishing 10% and 12% above £925k and £1.5m respectively.

The above plot also shows a distinct vertical banding around August / September 2003.  This is more clearly seen in the alternative histogram plot below.  What prompted this spike in sales volume is unclear.

House Prices Paid in SW10 Mapped to Key Events

The above chart shows the fall off in volume to the right of the Financial Crisis.  And it more clearly reveals the impact of the end-of-2014 stamp duty jab which took the wind out of a market that was just starting to recover from the 2007/08 “credit crunch”.

Is it time for change?

Stamp Duty Land Tax (SDLT), as it’s now known, severely disadvantaged those trying to sell at prices just above each threshold. The threshold rate applied to the total amount.

However, since the Autumn of 2014, rates apply only to the respective portions of the property price.  So, selling for £925,001, rather than £925,000, for example, does not now result in a steep jump in tax.

Nonetheless, the historically-high and sharply-rising rates do appear to have a dampening effect on the market.  “Life event” moves aside, the SW10 data reflect less mobility in the market. And my own personal experience is that buyers still want to negotiate down to the lower threshold price.

Is it time to tax land use, rather than moves, to free up mobility?

R toolkit

purrrmap[1]; possibly[1]; set_names[1]
dplyrfirst[3]; mutate[3]; as_tibble[2]; count[2]; filter[2]; recode[1]; select[1]; tibble[1]; tribble[1]
stringrstr_extract[2]; str_to_title[2]; fixed[1]; str_detect[1]
tibbleas_tibble[2]; enframe[1]; tibble[1]; tribble[1]
lubridatedate[11]; as_datetime[1]; stamp[1]; years[1]; ymd[1]
rebusalpha[3]; lookbehind[2]; one_or_more[2]; WRD[2]
tidyrfill[1]; unnest[1]
ggplot2aes[3]; alpha[3]; element_blank[2]; ggplot[2]; labs[2]; scale_y_continuous[2]; annotate[1]; coord_flip[1]; element_text[1]; facet_wrap[1]; geom_bar[1]; geom_label[1]; geom_point[1]; geom_smooth[1]; geom_vline[1]; stat[1]; theme[1]
scalesalpha[3]; dollar_format[1]; number[1]
ggthemestheme_economist[2]; scale_fill_economist[1]

The code may be viewed here.

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