+
+

Stamp Duty Land Tax#

+

Stamp Duty Land Tax (SDLT) is a tax imposed on the purchase and rental of properties or land with values over a certain threshold in the UK. It applies to both residential and non-residential (commercial) properties.

+

Stamp Duty Land Tax parameters can be found in policyengine_uk/parameters/gov/hmrc/stamp_duty and logic in policyengine_uk/variables/gov/hmrc/stamp_duty_land_tax.py.

+
+

Legislation#

+

The regulation of SDLT is defined in part 4 of the Finance Act 2003.

+
+
+

Rates#

+

The rates of SDLT depend on various factors, including the purchase price of the property, property type, and whether it is an additional property or a purchase by a first-time buyer. Generally, SDLT rates are higher for properties or land with a purchase price exceeding a specific threshold, often referred to as “stamp duty bands” or “stamp duty brackets.” The rules and thresholds for SDLT may change over time due to government policy adjustments. First-time buyers may be eligible for reduced SDLT rates if the purchase price of the property is not more than £500,000.

+

Here we show current rates and thresholds for SDLT under different situations in 2023 below:

+
  • Residential
    • +
      +

    • Rent

      +

      Up to £125,000: 0%

      +

      Over £125,000: 1%

      +
      • +

    • Purchase

      +
        +

      • Main property

        +
          +

        • First-time

          +

          Up to £300,000: 0%

          +

          £300,000 to £500,000: 5%

          +

          Over £500,000: Ineligible for relief, follow standard rates

          +
          • +

        • Subsequent (standard rates):

          +

          Up to £125,000: 0%

          +

          £125,000 to £250,000: 2%

          +

          £250,000 to £925,000: 5%

          +

          £925,000 to £1,500,000: 10%

          +

          Over £1,500,000: 12%

          +
          • +
        +
        • +

      • Additional property

        +

        An additional 3% on top of the standard rates for each band. However, a secondary property or land with a purchase price or market value less than £40,000 will be exempt from SDLT.

        +

        Less than £40,000: 0%

        +

        £40,000 to £125,000: 3%

        +

        £125,000 to £250,000: 5%

        +

        £250,000 to £925,000: 8%

        +

        £925,000 to £1,500,000: 13%

        +

        Over £1,500,000: 15%

        +
        • +
      +
      • +
    +
  • Non-residential
    • +
      +

    • Rent

      +

      Up to £150,000: 0%

      +

      £150,000 to £5,000,000: 1%

      +

      Over £5,000,000: 2%

      +
      • +

    • Purchase

      +

      Up to £150,000: 0%

      +

      £150,000 to £250,000: 2%

      +

      Over £250,000: 5%

      +
      • +
    +
    +
    + + +Hide code cell source + +
    +
    import pandas as pd
+import plotly.express as px
+from policyengine_core.charts import format_fig
+
+residential_rent = pd.DataFrame({
+    "Threshold": [0, 125000, 5200000],
+    "Rate": [0, 0.01, 0.01],
+    "Label": ["Residential rent"]*3
+})
+residential_purchase_main_first = pd.DataFrame({
+    "Threshold": [0, 300000, 500000, 925000, 1500000, 5200000], 
+    "Rate": [0, 0.05, 0.05, 0.1, 0.12, 0.12], 
+    "Label": ["Residential purchase main (first home)"]*6   
+})
+residential_purchase_main_standard = pd.DataFrame({
+    "Threshold": [0, 125000, 250000, 925000, 1500000, 5200000],
+    "Rate": [0, 0.02, 0.05, 0.1, 0.12, 0.12],
+    "Label": ["Residential purchase main standard"]*6
+})
+residential_purchase_additional = pd.DataFrame({
+    "Threshold": [0, 40000, 125000, 250000, 925000, 1500000, 5200000], 
+    "Rate": [0, 0.03, 0.05, 0.08, 0.13, 0.15, 0.15], 
+    "Label": ["Residential purchase additional"]*7
+})
+non_residential_rent = pd.DataFrame({
+    "Threshold": [0, 150000, 5000000, 5200000],
+    "Rate": [0, 0.01, 0.02, 0.02],
+    "Label": ["Non-residential rent"]*4
+})
+non_residential_purchase = pd.DataFrame({
+    "Threshold": [0, 150000, 250000, 5200000],
+    "Rate": [0, 0.02, 0.05, 0.05],
+    "Label": ["Non-residential purchase"]*4
+})
+df_list = [residential_rent, residential_purchase_main_first, residential_purchase_main_standard, residential_purchase_additional,
+           non_residential_rent, non_residential_purchase]
+df = pd.concat(df_list)
+# plot
+fig = px.line(
+    df,
+    x="Threshold",
+    y="Rate",
+    color="Label",
+    title = "Stamp duty land tax rates over property price thresholds"
+).update_layout(
+    yaxis_tickformat=",.0%",
+    xaxis_tickprefix="£",
+    legend=dict(
+        orientation="h",
+        yanchor="bottom",
+        y=-0.3,
+        xanchor="right",
+        x=0.9,
+        title=''
+    )
+).update_traces(
+    line_shape="hv"
+)
+fig = format_fig(fig)
+fig
+
    +
    +
    +
    +
    +
    +
    +
    +

    The first home discount only applies for homes priced under £500,000. When simulating the marginal effect of SDLT, we find a cliff effect for first-time buyers.

    +
    +
    + + +Hide code cell source + +
    +
    from policyengine_uk import Simulation
+
+sim = Simulation(
+    situation=dict(
+        households=dict(
+            household=dict(
+                main_residential_property_purchased_is_first_home=True,
+                members=["person"]
+            )
+        ),
+        people=dict(
+            person=dict(
+                age=30,
+            )
+        ),
+        axes=[[
+            dict(
+                name="main_residential_property_purchased",
+                min=0,
+                max=5_000_000,
+                count=1_000,
+            )
+        ]]
+    )
+)
+
+import pandas as pd
+
+stamp_duty = sim.calculate("stamp_duty_land_tax")
+home_price = sim.calculate("main_residential_property_purchased")
+
+marginal_rate = (stamp_duty[1:] - stamp_duty[:-1]) / (home_price[1:] - home_price[:-1])
+home_price = home_price[:-1]
+
+df = pd.DataFrame({
+    "Home price": home_price,
+    "Marginal SDLT rate": marginal_rate,
+    "SDLT": stamp_duty[:-1]
+})
+
+import plotly.express as px
+from plotly.subplots import make_subplots
+
+# left shows marginal rate, right shows total SDLT
+
+fig = make_subplots(rows=1, cols=2, subplot_titles=("Marginal SDLT rate", "Total SDLT"))
+
+fig.add_trace(
+    px.line(
+        df,
+        x="Home price",
+        y="Marginal SDLT rate",
+        title = "Marginal SDLT rate"
+    ).update_traces(
+        line_shape="hv"
+    ).data[0],
+    row=1, col=1
+).update_xaxes(
+    row=1, col=1,
+    title_text="Price",
+    tickprefix="£"
+).update_yaxes(
+    row=1, col=1,
+    tickformat=",.0%"
+)
+
+fig.add_trace(
+    px.line(
+        df,
+        x="Home price",
+        y="SDLT",
+        title = "Total SDLT"
+    ).update_traces(
+        line_shape="hv"
+    ).data[0],
+    row=1, col=2
+).update_xaxes(
+    row=1, col=2,
+    title_text="Price",
+    tickprefix="£"
+).update_yaxes(
+    row=1, col=2,
+    tickprefix="£"
+)
+
+fig = format_fig(fig)
+fig
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    SDLT Statistics for 2019 and 2020#

    +

    HM Revenue & Customs has collected statistics for revenues generated from Stamp Duty Land Tax (SDLT). The SDLT statistics were collected separately for residential and non-residental properties, and for each property type, HMRC divided the data into two categories: corporate and households. The graph below shows SDLT statistics for 2019 and 2020:

    +
    +
    + + +Hide code cell source + +
    +
    from policyengine_uk.system import system
+from policyengine_core.charts import format_fig
+sdlt = system.parameters.gov.hmrc.stamp_duty.statistics
+stats = {
+    "Year": [],
+    "Revenue (£m)": [],
+    "Type": []
+}
+# residential corporate
+for parameter in sdlt.residential.corporate.revenue.values_list:
+    stats["Year"].append(parameter.instant_str[:4])
+    stats["Revenue (£m)"].append(parameter.value/1000000)
+    stats["Type"].append("Residental corporate")
+# residential household
+for parameter in sdlt.residential.household.revenue.values_list:
+    stats["Year"].append(parameter.instant_str[:4])
+    stats["Revenue (£m)"].append(parameter.value/1000000)
+    stats["Type"].append("Residental household")
+# non-residential corporate
+for parameter in sdlt.non_residential.corporate.revenue.values_list:
+    stats["Year"].append(parameter.instant_str[:4])
+    stats["Revenue (£m)"].append(parameter.value/1000000)
+    stats["Type"].append("Non-residental corporate")
+# non-residential household
+for parameter in sdlt.non_residential.household.revenue.values_list:
+    stats["Year"].append(parameter.instant_str[:4])
+    stats["Revenue (£m)"].append(parameter.value/1000000)
+    stats["Type"].append("Non-residental household")
+
+df = pd.DataFrame(stats)
+df.sort_values("Year", inplace=True)
+fig = px.bar(
+    df,
+    x="Type",
+    y="Revenue (£m)",
+    color="Year",
+    barmode="group",
+    text_auto=True,
+    title="SDLT statistics for 2019 and 2020",
+    height=500
+).update_traces(
+    textposition="outside"
+).update_layout(
+    xaxis_title="Property type"
+)
+fig = format_fig(fig)
+fig
+
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +