Abstract
In London, Stamp Duty Land Tax (SDLT) is paid when a property changes ownership. The amount
of tax payable is determined by central government policy, and depends on the value of the
property. The current SDLT policy was introduced in 2014. This work aims to predict the SDLT
receivable in London over the next five years.
SDLT revenue will depend upon the value of property changing ownership, but also upon the
housing market. Any model of SDLT receipts will therefore need to include assumptions about the
housing market in London. Here, the housing market is considered in two parts: the total number
of properties sold, and the distribution of price across the properties sold. In this way, housing
market scenarios are constructed in which:
•
Growth is extrapolated from previous market trends
•
Some aspect of the housing market (either price or volume of sales) remains constant
•
There is a crash
These behaviours combine with different predictions of total sales numbers and price
distributions to give a range of possible housing market scenarios and, therefore, SDLT receipts.
Resulting revenues range from steady increases over the next five years to a decrease to 35%
of current values by 2017 for one of the crash scenarios. All revenues are in 2016 prices.
of tax payable is determined by central government policy, and depends on the value of the
property. The current SDLT policy was introduced in 2014. This work aims to predict the SDLT
receivable in London over the next five years.
SDLT revenue will depend upon the value of property changing ownership, but also upon the
housing market. Any model of SDLT receipts will therefore need to include assumptions about the
housing market in London. Here, the housing market is considered in two parts: the total number
of properties sold, and the distribution of price across the properties sold. In this way, housing
market scenarios are constructed in which:
•
Growth is extrapolated from previous market trends
•
Some aspect of the housing market (either price or volume of sales) remains constant
•
There is a crash
These behaviours combine with different predictions of total sales numbers and price
distributions to give a range of possible housing market scenarios and, therefore, SDLT receipts.
Resulting revenues range from steady increases over the next five years to a decrease to 35%
of current values by 2017 for one of the crash scenarios. All revenues are in 2016 prices.
Original language | English |
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Publisher | Institute for Policy Research, University of Bath |
Publication status | Published - 2017 |