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"cell_type": "markdown",
"metadata": {},
"source": [
"# Land Registry Price Paid Data Analysis\n",
"\n",
"Analysis of UK property transactions from HM Land Registry, with focus on London."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"polars.config.Config"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import polars as pl\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots\n",
"from pathlib import Path\n",
"\n",
"pl.Config.set_tbl_rows(20)\n",
"pl.Config.set_fmt_str_lengths(50)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Schema: Schema({'transaction_id': String, 'price': Int64, 'date_of_transfer': Datetime(time_unit='us', time_zone=None), 'postcode': String, 'property_type': String, 'old_new': String, 'duration': String, 'paon': String, 'saon': String, 'street': String, 'locality': String, 'town_city': String, 'district': String, 'county': String, 'ppd_category': String, 'record_status': String})\n"
]
}
],
"source": [
"DATA_PATH = Path(\"../data_sources/pp-complete.parquet\")\n",
"lf = pl.scan_parquet(DATA_PATH)\n",
"print(f\"Schema: {lf.collect_schema()}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"PROPERTY_TYPE_MAP = {\n",
" \"D\": \"Detached\",\n",
" \"S\": \"Semi-detached\",\n",
" \"T\": \"Terraced\",\n",
" \"F\": \"Flat/Maisonette\",\n",
" \"O\": \"Other\",\n",
"}\n",
"TENURE_MAP = {\"F\": \"Freehold\", \"L\": \"Leasehold\"}\n",
"NEW_OLD_MAP = {\"Y\": \"New Build\", \"N\": \"Existing\"}\n",
"LONDON_FILTER = pl.col(\"county\") == \"GREATER LONDON\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 1. Overview & Basic Statistics"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total transactions: 30,813,304\n",
"London transactions: 3,908,410 (12.7%)\n"
]
}
],
"source": [
"row_count = lf.select(pl.len()).collect().item()\n",
"london_count = lf.filter(LONDON_FILTER).select(pl.len()).collect().item()\n",
"print(f\"Total transactions: {row_count:,}\")\n",
"print(f\"London transactions: {london_count:,} ({london_count / row_count * 100:.1f}%)\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Date range: 1995-01-01 00:00:00 to 2025-11-28 00:00:00\n"
]
}
],
"source": [
"date_stats = lf.select(\n",
" pl.col(\"date_of_transfer\").min().alias(\"earliest\"),\n",
" pl.col(\"date_of_transfer\").max().alias(\"latest\"),\n",
").collect()\n",
"print(f\"Date range: {date_stats['earliest'][0]} to {date_stats['latest'][0]}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== National Price Statistics ===\n"
]
},
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|---|
| i64 | i64 | f64 | f64 | f64 | f64 | f64 |
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},
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"cell_type": "code",
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"london_boroughs = (\n",
" lf.filter(LONDON_FILTER)\n",
" .group_by(\"district\")\n",
" .agg(\n",
" pl.len().alias(\"count\"),\n",
" pl.col(\"price\").mean().alias(\"avg_price\"),\n",
" pl.col(\"price\").median().alias(\"median_price\"),\n",
" )\n",
" .sort(\"avg_price\", descending=True)\n",
" .collect()\n",
")\n",
"\n",
"fig = px.bar(\n",
" london_boroughs.to_pandas(),\n",
" x=\"avg_price\",\n",
" y=\"district\",\n",
" orientation=\"h\",\n",
" title=\"London Boroughs by Average Price\",\n",
" color=\"count\",\n",
" color_continuous_scale=\"Reds\",\n",
" labels={\n",
" \"avg_price\": \"Average Price (£)\",\n",
" \"district\": \"Borough\",\n",
" \"count\": \"Transactions\",\n",
" },\n",
")\n",
"fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800)\n",
"fig.show()"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 3. Time Series Analysis"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def get_yearly_stats(lazy_frame):\n",
" return (\n",
" lazy_frame.with_columns(pl.col(\"date_of_transfer\").dt.year().alias(\"year\"))\n",
" .group_by(\"year\")\n",
" .agg(\n",
" pl.len().alias(\"count\"),\n",
" pl.col(\"price\").mean().alias(\"avg_price\"),\n",
" pl.col(\"price\").median().alias(\"median_price\"),\n",
" )\n",
" .sort(\"year\")\n",
" .collect()\n",
" )\n",
"\n",
"\n",
"yearly_national = get_yearly_stats(lf)\n",
"yearly_london = get_yearly_stats(lf.filter(LONDON_FILTER))"
]
},
{
"cell_type": "code",
"execution_count": 12,
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" mode=\"lines+markers\",\n",
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" (pl.col(\"avg_price\").pct_change() * 100).alias(\"yoy_change\")\n",
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"yearly_london_pct = yearly_london.with_columns(\n",
" (pl.col(\"avg_price\").pct_change() * 100).alias(\"yoy_change\")\n",
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"\n",
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"fig.add_trace(\n",
" go.Bar(\n",
" x=yearly_national_pct[\"year\"],\n",
" y=yearly_national_pct[\"yoy_change\"],\n",
" name=\"National\",\n",
" opacity=0.7,\n",
" )\n",
")\n",
"fig.add_trace(\n",
" go.Bar(\n",
" x=yearly_london_pct[\"year\"],\n",
" y=yearly_london_pct[\"yoy_change\"],\n",
" name=\"London\",\n",
" opacity=0.7,\n",
" )\n",
")\n",
"fig.add_hline(y=0, line_dash=\"dash\", line_color=\"gray\")\n",
"fig.update_layout(\n",
" title=\"Year-over-Year Price Change (%)\",\n",
" xaxis_title=\"Year\",\n",
" yaxis_title=\"Change (%)\",\n",
" barmode=\"group\",\n",
" height=500,\n",
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"fig.show()"
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{
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"def get_monthly_stats(lazy_frame, start_year=2015):\n",
" return (\n",
" lazy_frame.filter(pl.col(\"date_of_transfer\").dt.year() >= start_year)\n",
" .with_columns(pl.col(\"date_of_transfer\").dt.truncate(\"1mo\").alias(\"month\"))\n",
" .group_by(\"month\")\n",
" .agg(\n",
" pl.len().alias(\"count\"),\n",
" pl.col(\"price\").mean().alias(\"avg_price\"),\n",
" pl.col(\"price\").median().alias(\"median_price\"),\n",
" )\n",
" .sort(\"month\")\n",
" .collect()\n",
" )\n",
"\n",
"\n",
"monthly_national = get_monthly_stats(lf)\n",
"monthly_london = get_monthly_stats(lf.filter(LONDON_FILTER))\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(\n",
" go.Scatter(\n",
" x=monthly_national[\"month\"],\n",
" y=monthly_national[\"avg_price\"],\n",
" name=\"National\",\n",
" mode=\"lines\",\n",
" )\n",
")\n",
"fig.add_trace(\n",
" go.Scatter(\n",
" x=monthly_london[\"month\"],\n",
" y=monthly_london[\"avg_price\"],\n",
" name=\"London\",\n",
" mode=\"lines\",\n",
" line=dict(color=\"crimson\"),\n",
" )\n",
")\n",
"fig.update_layout(\n",
" title=\"Monthly Average Price (2015 onwards)\",\n",
" xaxis_title=\"Month\",\n",
" yaxis_title=\"Average Price (£)\",\n",
" height=500,\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
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"source": [
"yearly_by_type_london = (\n",
" lf.filter(LONDON_FILTER)\n",
" .with_columns(pl.col(\"date_of_transfer\").dt.year().alias(\"year\"))\n",
" .group_by(\"year\", \"property_type\")\n",
" .agg(pl.col(\"price\").mean().alias(\"avg_price\"), pl.len().alias(\"count\"))\n",
" .with_columns(pl.col(\"property_type\").replace(PROPERTY_TYPE_MAP).alias(\"type_name\"))\n",
" .sort(\"year\")\n",
" .collect()\n",
")\n",
"\n",
"fig = px.line(\n",
" yearly_by_type_london.to_pandas(),\n",
" x=\"year\",\n",
" y=\"avg_price\",\n",
" color=\"type_name\",\n",
" title=\"London: Average Price by Property Type Over Time\",\n",
" labels={\n",
" \"avg_price\": \"Average Price (£)\",\n",
" \"year\": \"Year\",\n",
" \"type_name\": \"Property Type\",\n",
" },\n",
" markers=True,\n",
")\n",
"fig.update_layout(height=500)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
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"source": [
"monthly_london_rolling = monthly_london.with_columns(\n",
" pl.col(\"avg_price\").rolling_mean(window_size=12).alias(\"rolling_12m_avg\")\n",
")\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(\n",
" go.Scatter(\n",
" x=monthly_london_rolling[\"month\"],\n",
" y=monthly_london_rolling[\"avg_price\"],\n",
" name=\"Monthly\",\n",
" mode=\"lines\",\n",
" opacity=0.5,\n",
" )\n",
")\n",
"fig.add_trace(\n",
" go.Scatter(\n",
" x=monthly_london_rolling[\"month\"],\n",
" y=monthly_london_rolling[\"rolling_12m_avg\"],\n",
" name=\"12-Month Rolling Avg\",\n",
" mode=\"lines\",\n",
" line=dict(width=3, color=\"crimson\"),\n",
" )\n",
")\n",
"fig.update_layout(\n",
" title=\"London: Monthly Price with 12-Month Rolling Average\",\n",
" xaxis_title=\"Month\",\n",
" yaxis_title=\"Average Price (£)\",\n",
" height=500,\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
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"base_price_london = yearly_london.filter(pl.col(\"year\") == 1995)[\"avg_price\"][0]\n",
"\n",
"yearly_indexed = yearly_national.with_columns(\n",
" (pl.col(\"avg_price\") / base_price_national * 100).alias(\"national_index\")\n",
").join(\n",
" yearly_london.with_columns(\n",
" (pl.col(\"avg_price\") / base_price_london * 100).alias(\"london_index\")\n",
" ).select(\"year\", \"london_index\"),\n",
" on=\"year\",\n",
")\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(\n",
" go.Scatter(\n",
" x=yearly_indexed[\"year\"],\n",
" y=yearly_indexed[\"national_index\"],\n",
" name=\"National\",\n",
" mode=\"lines+markers\",\n",
" )\n",
")\n",
"fig.add_trace(\n",
" go.Scatter(\n",
" x=yearly_indexed[\"year\"],\n",
" y=yearly_indexed[\"london_index\"],\n",
" name=\"London\",\n",
" mode=\"lines+markers\",\n",
" line=dict(color=\"crimson\"),\n",
" )\n",
")\n",
"fig.add_hline(\n",
" y=100, line_dash=\"dash\", line_color=\"gray\", annotation_text=\"1995 Baseline\"\n",
")\n",
"fig.update_layout(\n",
" title=\"Price Index (1995 = 100)\",\n",
" xaxis_title=\"Year\",\n",
" yaxis_title=\"Index\",\n",
" height=500,\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## 4. Borough Selection Guide for Property Buyers\n",
"\n",
"Analysis to help choose which London borough to buy in, considering affordability, growth, market activity, and property mix."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
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"source": [
"# Current median prices by property type for each borough\n",
"borough_by_type = (\n",
" lf.filter(LONDON_FILTER)\n",
" .filter(pl.col(\"date_of_transfer\").dt.year() >= 2023)\n",
" .group_by(\"district\", \"property_type\")\n",
" .agg(pl.col(\"price\").median().alias(\"median_price\"), pl.len().alias(\"count\"))\n",
" .with_columns(pl.col(\"property_type\").replace(PROPERTY_TYPE_MAP).alias(\"type_name\"))\n",
" .collect()\n",
")\n",
"\n",
"flats_by_borough = borough_by_type.filter(pl.col(\"property_type\") == \"F\").sort(\n",
" \"median_price\"\n",
")\n",
"\n",
"fig = px.bar(\n",
" flats_by_borough.to_pandas(),\n",
" x=\"median_price\",\n",
" y=\"district\",\n",
" orientation=\"h\",\n",
" title=\"Current Median Flat Prices by Borough (2023+)\",\n",
" color=\"count\",\n",
" color_continuous_scale=\"Viridis\",\n",
" labels={\n",
" \"median_price\": \"Median Price (£)\",\n",
" \"district\": \"Borough\",\n",
" \"count\": \"Sales Volume\",\n",
" },\n",
")\n",
"fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
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"# Heatmap: Price by borough and property type\n",
"pivot_data = borough_by_type.pivot(\n",
" on=\"type_name\", index=\"district\", values=\"median_price\"\n",
").sort(\"Flat/Maisonette\", nulls_last=True)\n",
"\n",
"fig = px.imshow(\n",
" pivot_data.select(\n",
" [\"Flat/Maisonette\", \"Terraced\", \"Semi-detached\", \"Detached\"]\n",
" ).to_pandas(),\n",
" y=pivot_data[\"district\"].to_list(),\n",
" x=[\"Flat\", \"Terraced\", \"Semi-detached\", \"Detached\"],\n",
" color_continuous_scale=\"RdYlGn_r\",\n",
" aspect=\"auto\",\n",
" title=\"Median Price by Borough and Property Type (2023+)\",\n",
" labels={\"color\": \"Price (£)\"},\n",
")\n",
"fig.update_layout(height=900)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
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shape: (33, 6)| district | price_2014 | price_2019 | price_2024 | cagr_10yr | cagr_5yr |
|---|
| str | f64 | f64 | f64 | f64 | f64 |
| "HAVERING" | 250000.0 | 360000.0 | 430000.0 | 5.572996 | 3.617519 |
| "BEXLEY" | 250000.0 | 350000.0 | 415000.0 | 5.198806 | 3.465607 |
| "HILLINGDON" | 302250.0 | 405000.0 | 480000.0 | 4.733952 | 3.456372 |
| "SUTTON" | 285000.0 | 380000.0 | 445750.0 | 4.574224 | 3.243222 |
| "REDBRIDGE" | 298300.0 | 405000.0 | 475000.0 | 4.762061 | 3.239934 |
| "HOUNSLOW" | 315000.0 | 400000.0 | 466000.0 | 3.993821 | 3.101548 |
| "BARKING AND DAGENHAM" | 215000.0 | 310000.0 | 360000.0 | 5.289825 | 3.035803 |
| "HARROW" | 365000.0 | 440000.0 | 510000.0 | 3.401712 | 2.996745 |
| "ENFIELD" | 280000.0 | 385000.0 | 446000.0 | 4.765354 | 2.985202 |
| "WALTHAM FOREST" | 317408.5 | 440625.0 | 504000.0 | 4.732434 | 2.724085 |
| … | … | … | … | … | … |
| "WANDSWORTH" | 528000.0 | 610000.0 | 635000.0 | 1.862418 | 0.806556 |
| "GREENWICH" | 300000.0 | 435000.0 | 450000.0 | 4.137974 | 0.680335 |
| "CAMDEN" | 670000.0 | 760000.0 | 780000.0 | 1.531775 | 0.520862 |
| "SOUTHWARK" | 415000.0 | 540000.0 | 552000.0 | 2.893774 | 0.440546 |
| "TOWER HAMLETS" | 380000.0 | 485000.0 | 492500.0 | 2.627149 | 0.307383 |
| "KENSINGTON AND CHELSEA" | 1.195e6 | 1.2e6 | 1.2e6 | 0.041762 | 0.0 |
| "LAMBETH" | 418000.0 | 530000.0 | 530000.0 | 2.402358 | 0.0 |
| "CITY OF WESTMINSTER" | 885000.0 | 960000.0 | 956000.0 | 0.774688 | -0.083473 |
| "HACKNEY" | 434847.5 | 600000.0 | 590000.0 | 3.0983 | -0.335578 |
| "CITY OF LONDON" | 790000.0 | 950000.0 | 901250.0 | 1.326215 | -1.048055 |
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"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Borough Price Growth: 5-Year vs 10-Year CAGR"
},
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "10-Year CAGR (%)"
}
},
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"anchor": "x",
"domain": [
0,
1
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"text": "5-Year CAGR (%)"
}
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}
},
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"output_type": "display_data"
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],
"source": [
"# 5-year vs 10-year CAGR scatter\n",
"fig = px.scatter(\n",
" growth_df.to_pandas(),\n",
" x=\"cagr_10yr\",\n",
" y=\"cagr_5yr\",\n",
" text=\"district\",\n",
" title=\"Borough Price Growth: 5-Year vs 10-Year CAGR\",\n",
" labels={\"cagr_10yr\": \"10-Year CAGR (%)\", \"cagr_5yr\": \"5-Year CAGR (%)\"},\n",
" color=\"price_2024\",\n",
" color_continuous_scale=\"Viridis\",\n",
")\n",
"fig.update_traces(textposition=\"top center\", textfont_size=8)\n",
"fig.add_hline(y=0, line_dash=\"dash\", line_color=\"gray\")\n",
"fig.add_vline(x=0, line_dash=\"dash\", line_color=\"gray\")\n",
"fig.update_layout(height=700)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
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"fig = px.bar(\n",
" growth_df.sort(\"cagr_5yr\").to_pandas(),\n",
" x=\"cagr_5yr\",\n",
" y=\"district\",\n",
" orientation=\"h\",\n",
" title=\"5-Year Price Growth by Borough (CAGR %)\",\n",
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" color_continuous_scale=\"RdYlGn\",\n",
" labels={\"cagr_5yr\": \"5-Year CAGR (%)\", \"district\": \"Borough\"},\n",
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]
},
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"orientation": "h",
"showlegend": true,
"textposition": "auto",
"type": "bar",
"x": {
"bdata": "diOcaIoaREBXLZj2KaxRQKwwC9Lc+ktAMhBWYgdFQEApCdC3G+hCQOqId9AseTZAjfZ1414IPkAAV/jZr+NKQESTEM6liEVAhHIMkqD1SEA1jmAfE/pCQEzR1rtyLFNA7COwqp0KUUAd+WC7kR1WQFkvpYsYukpA6eFj7/oTSEAVGL70LeNCQDg1Cbl4RDpAPmWvsCbUP0Bm2pHswZtRQOSXWArTk1BA69YAEEFYQUBXiYvZKtFSQMWMtl8JmEZAUhF9v3QIU0AHUj906UxTQJfSJgOeQ0NAbQomwk55UUAikTWeJqZLQKZegk+0eVJA+lWiwSrUQ0CWEJDzUwk8QJIWjgKEpEpA",
"dtype": "f8"
},
"xaxis": "x",
"y": [
"KINGSTON UPON THAMES",
"LAMBETH",
"HARINGEY",
"HILLINGDON",
"SUTTON",
"HAVERING",
"REDBRIDGE",
"GREENWICH",
"WALTHAM FOREST",
"EALING",
"CROYDON",
"CITY OF WESTMINSTER",
"HAMMERSMITH AND FULHAM",
"TOWER HAMLETS",
"BRENT",
"BARNET",
"HARROW",
"BEXLEY",
"BROMLEY",
"SOUTHWARK",
"WANDSWORTH",
"ENFIELD",
"CAMDEN",
"HOUNSLOW",
"HACKNEY",
"CITY OF LONDON",
"RICHMOND UPON THAMES",
"KENSINGTON AND CHELSEA",
"NEWHAM",
"ISLINGTON",
"MERTON",
"BARKING AND DAGENHAM",
"LEWISHAM"
],
"yaxis": "y"
},
{
"hovertemplate": "Property Type=Detached
% of Transactions=%{x}
Borough=%{y}",
"legendgroup": "Detached",
"marker": {
"color": "#00cc96",
"pattern": {
"shape": ""
}
},
"name": "Detached",
"orientation": "h",
"showlegend": true,
"textposition": "auto",
"type": "bar",
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},
"xaxis": "x",
"y": [
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"ENFIELD",
"BRENT",
"CAMDEN",
"LAMBETH",
"TOWER HAMLETS",
"BROMLEY",
"SOUTHWARK",
"HARROW",
"HAMMERSMITH AND FULHAM",
"SUTTON",
"MERTON",
"LEWISHAM",
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"HACKNEY",
"HARINGEY",
"WALTHAM FOREST",
"HOUNSLOW",
"WANDSWORTH",
"BARKING AND DAGENHAM",
"RICHMOND UPON THAMES",
"BARNET",
"REDBRIDGE",
"CITY OF WESTMINSTER",
"GREENWICH",
"ISLINGTON",
"KINGSTON UPON THAMES",
"HAVERING",
"HILLINGDON",
"CROYDON",
"KENSINGTON AND CHELSEA",
"EALING",
"NEWHAM"
],
"yaxis": "y"
},
{
"hovertemplate": "Property Type=Terraced
% of Transactions=%{x}
Borough=%{y}",
"legendgroup": "Terraced",
"marker": {
"color": "#ab63fa",
"pattern": {
"shape": ""
}
},
"name": "Terraced",
"orientation": "h",
"showlegend": true,
"textposition": "auto",
"type": "bar",
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},
"xaxis": "x",
"y": [
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"NEWHAM",
"HAVERING",
"HOUNSLOW",
"CAMDEN"
],
"yaxis": "y"
},
{
"hovertemplate": "Property Type=Semi-detached
% of Transactions=%{x}
Borough=%{y}",
"legendgroup": "Semi-detached",
"marker": {
"color": "#FFA15A",
"pattern": {
"shape": ""
}
},
"name": "Semi-detached",
"orientation": "h",
"showlegend": true,
"textposition": "auto",
"type": "bar",
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"HAVERING",
"HILLINGDON",
"TOWER HAMLETS"
],
"yaxis": "y"
}
],
"layout": {
"barmode": "stack",
"height": 900,
"legend": {
"title": {
"text": "Property Type"
},
"tracegroupgap": 0
},
"template": {
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"error_x": {
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},
"error_y": {
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"marker": {
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"pattern": {
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"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
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}
},
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],
"carpet": [
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],
"histogram": [
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}
},
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],
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],
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"type": "pie"
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"scatter": [
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},
"type": "scatter"
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],
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},
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],
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],
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},
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],
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}
},
"type": "scattermap"
}
],
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},
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],
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},
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],
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},
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}
},
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}
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},
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},
"autotypenumbers": "strict",
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},
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"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
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},
"hovermode": "closest",
"mapbox": {
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},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
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},
"bgcolor": "#E5ECF6",
"radialaxis": {
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}
},
"scene": {
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"gridcolor": "white",
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"showbackground": true,
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"gridcolor": "white",
"gridwidth": 2,
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}
},
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"line": {
"color": "#2a3f5f"
}
},
"ternary": {
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"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
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},
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"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
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},
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"zerolinewidth": 2
}
}
},
"title": {
"text": "Property Type Mix by Borough (2020+)"
},
"xaxis": {
"anchor": "y",
"domain": [
0,
1
],
"title": {
"text": "% of Transactions"
}
},
"yaxis": {
"anchor": "x",
"categoryarray": [
"HAVERING",
"BEXLEY",
"BARKING AND DAGENHAM",
"REDBRIDGE",
"BROMLEY",
"HILLINGDON",
"ENFIELD",
"HARROW",
"SUTTON",
"CROYDON",
"RICHMOND UPON THAMES",
"MERTON",
"KINGSTON UPON THAMES",
"WALTHAM FOREST",
"HOUNSLOW",
"BARNET",
"EALING",
"LEWISHAM",
"BRENT",
"GREENWICH",
"NEWHAM",
"HARINGEY",
"WANDSWORTH",
"HAMMERSMITH AND FULHAM",
"KENSINGTON AND CHELSEA",
"SOUTHWARK",
"LAMBETH",
"ISLINGTON",
"CAMDEN",
"HACKNEY",
"CITY OF WESTMINSTER",
"CITY OF LONDON",
"TOWER HAMLETS"
],
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0,
1
],
"title": {
"text": "Borough"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Property Type Mix by Borough\n",
"property_mix = (\n",
" lf.filter(LONDON_FILTER)\n",
" .filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
" .group_by(\"district\", \"property_type\")\n",
" .agg(pl.len().alias(\"count\"))\n",
" .with_columns(pl.col(\"property_type\").replace(PROPERTY_TYPE_MAP).alias(\"type_name\"))\n",
" .collect()\n",
")\n",
"totals = property_mix.group_by(\"district\").agg(pl.col(\"count\").sum().alias(\"total\"))\n",
"property_mix_pct = property_mix.join(totals, on=\"district\").with_columns(\n",
" (pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"percentage\")\n",
")\n",
"flat_pct = property_mix_pct.filter(pl.col(\"property_type\") == \"F\").sort(\n",
" \"percentage\", descending=True\n",
")\n",
"\n",
"fig = px.bar(\n",
" property_mix_pct.to_pandas(),\n",
" x=\"percentage\",\n",
" y=\"district\",\n",
" color=\"type_name\",\n",
" orientation=\"h\",\n",
" title=\"Property Type Mix by Borough (2020+)\",\n",
" labels={\n",
" \"percentage\": \"% of Transactions\",\n",
" \"district\": \"Borough\",\n",
" \"type_name\": \"Property Type\",\n",
" },\n",
" category_orders={\"district\": flat_pct[\"district\"].to_list()},\n",
")\n",
"fig.update_layout(height=900, barmode=\"stack\", legend_title=\"Property Type\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
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"source": [
"# Freehold vs Leasehold by Borough\n",
"tenure_mix = (\n",
" lf.filter(LONDON_FILTER)\n",
" .filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
" .group_by(\"district\", \"duration\")\n",
" .agg(pl.len().alias(\"count\"))\n",
" .with_columns(pl.col(\"duration\").replace(TENURE_MAP).alias(\"tenure_name\"))\n",
" .collect()\n",
")\n",
"totals_tenure = tenure_mix.group_by(\"district\").agg(\n",
" pl.col(\"count\").sum().alias(\"total\")\n",
")\n",
"tenure_mix_pct = tenure_mix.join(totals_tenure, on=\"district\").with_columns(\n",
" (pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"percentage\")\n",
")\n",
"freehold_pct = tenure_mix_pct.filter(pl.col(\"duration\") == \"F\").sort(\n",
" \"percentage\", descending=True\n",
")\n",
"\n",
"fig = px.bar(\n",
" tenure_mix_pct.to_pandas(),\n",
" x=\"percentage\",\n",
" y=\"district\",\n",
" color=\"tenure_name\",\n",
" orientation=\"h\",\n",
" title=\"Freehold vs Leasehold by Borough (2020+)\",\n",
" labels={\n",
" \"percentage\": \"% of Transactions\",\n",
" \"district\": \"Borough\",\n",
" \"tenure_name\": \"Tenure\",\n",
" },\n",
" category_orders={\"district\": freehold_pct[\"district\"].to_list()},\n",
" color_discrete_map={\"Freehold\": \"seagreen\", \"Leasehold\": \"coral\"},\n",
")\n",
"fig.update_layout(height=900, barmode=\"stack\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
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"source": [
"# Market Activity Change (2024 vs 2019)\n",
"volume_trend = (\n",
" lf.filter(LONDON_FILTER)\n",
" .with_columns(pl.col(\"date_of_transfer\").dt.year().alias(\"year\"))\n",
" .filter(pl.col(\"year\") >= 2019)\n",
" .group_by(\"district\", \"year\")\n",
" .agg(pl.len().alias(\"count\"))\n",
" .collect()\n",
")\n",
"volume_2019 = volume_trend.filter(pl.col(\"year\") == 2019).select(\n",
" \"district\", pl.col(\"count\").alias(\"count_2019\")\n",
")\n",
"volume_2024 = volume_trend.filter(pl.col(\"year\") == 2024).select(\n",
" \"district\", pl.col(\"count\").alias(\"count_2024\")\n",
")\n",
"volume_change = (\n",
" volume_2019.join(volume_2024, on=\"district\", how=\"inner\")\n",
" .with_columns(\n",
" (\n",
" (pl.col(\"count_2024\") - pl.col(\"count_2019\")) / pl.col(\"count_2019\") * 100\n",
" ).alias(\"pct_change\")\n",
" )\n",
" .sort(\"pct_change\", descending=True)\n",
")\n",
"\n",
"fig = px.bar(\n",
" volume_change.to_pandas(),\n",
" x=\"pct_change\",\n",
" y=\"district\",\n",
" orientation=\"h\",\n",
" title=\"Market Activity Change: 2024 vs 2019 Transaction Volume (%)\",\n",
" color=\"pct_change\",\n",
" color_continuous_scale=\"RdYlGn\",\n",
" labels={\"pct_change\": \"Volume Change (%)\", \"district\": \"Borough\"},\n",
")\n",
"fig.add_vline(x=0, line_dash=\"dash\", line_color=\"black\")\n",
"fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
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"# New Build Share by Borough\n",
"new_build_rate = (\n",
" lf.filter(LONDON_FILTER)\n",
" .filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n",
" .group_by(\"district\", \"old_new\")\n",
" .agg(pl.len().alias(\"count\"))\n",
" .collect()\n",
")\n",
"totals_nb = new_build_rate.group_by(\"district\").agg(\n",
" pl.col(\"count\").sum().alias(\"total\")\n",
")\n",
"new_build_pct = (\n",
" new_build_rate.filter(pl.col(\"old_new\") == \"Y\")\n",
" .join(totals_nb, on=\"district\")\n",
" .with_columns((pl.col(\"count\") / pl.col(\"total\") * 100).alias(\"new_build_pct\"))\n",
" .sort(\"new_build_pct\", descending=True)\n",
")\n",
"\n",
"fig = px.bar(\n",
" new_build_pct.to_pandas(),\n",
" x=\"new_build_pct\",\n",
" y=\"district\",\n",
" orientation=\"h\",\n",
" title=\"New Build Share by Borough (2020+)\",\n",
" color=\"new_build_pct\",\n",
" color_continuous_scale=\"Blues\",\n",
" labels={\"new_build_pct\": \"New Build %\", \"district\": \"Borough\"},\n",
")\n",
"fig.update_layout(yaxis={\"categoryorder\": \"total ascending\"}, height=800)\n",
"fig.show()"
]
},
{
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"execution_count": 27,
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"source": [
"# Budget Finder: Borough Price Ranges\n",
"recent_prices = (\n",
" lf.filter(LONDON_FILTER)\n",
" .filter(pl.col(\"date_of_transfer\").dt.year() >= 2023)\n",
" .group_by(\"district\")\n",
" .agg(\n",
" pl.col(\"price\").quantile(0.25).alias(\"p25\"),\n",
" pl.col(\"price\").median().alias(\"median\"),\n",
" pl.col(\"price\").quantile(0.75).alias(\"p75\"),\n",
" )\n",
" .sort(\"median\")\n",
" .collect()\n",
")\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(\n",
" go.Bar(\n",
" name=\"25th percentile\",\n",
" y=recent_prices[\"district\"],\n",
" x=recent_prices[\"p25\"],\n",
" orientation=\"h\",\n",
" marker_color=\"lightgreen\",\n",
" )\n",
")\n",
"fig.add_trace(\n",
" go.Bar(\n",
" name=\"Median\",\n",
" y=recent_prices[\"district\"],\n",
" x=recent_prices[\"median\"] - recent_prices[\"p25\"],\n",
" orientation=\"h\",\n",
" marker_color=\"steelblue\",\n",
" base=recent_prices[\"p25\"],\n",
" )\n",
")\n",
"fig.add_trace(\n",
" go.Bar(\n",
" name=\"75th percentile\",\n",
" y=recent_prices[\"district\"],\n",
" x=recent_prices[\"p75\"] - recent_prices[\"median\"],\n",
" orientation=\"h\",\n",
" marker_color=\"coral\",\n",
" base=recent_prices[\"median\"],\n",
" )\n",
")\n",
"\n",
"for budget in [300000, 500000, 750000, 1000000]:\n",
" fig.add_vline(\n",
" x=budget,\n",
" line_dash=\"dash\",\n",
" line_color=\"gray\",\n",
" annotation_text=f\"£{budget // 1000}k\",\n",
" annotation_position=\"top\",\n",
" )\n",
"\n",
"fig.update_layout(\n",
" title=\"Borough Price Ranges (2023+) - Find Your Budget\",\n",
" xaxis_title=\"Price (£)\",\n",
" yaxis={\"categoryorder\": \"total ascending\"},\n",
" barmode=\"stack\",\n",
" height=900,\n",
" legend_title=\"Price Point\",\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# Comprehensive Borough Comparison\n",
"comparison_data = (\n",
" recent_prices.select(\"district\", \"median\")\n",
" .join(growth_df.select(\"district\", \"cagr_5yr\"), on=\"district\", how=\"left\")\n",
" .join(\n",
" freehold_pct.select(\"district\", \"percentage\").rename(\n",
" {\"percentage\": \"freehold_pct\"}\n",
" ),\n",
" on=\"district\",\n",
" how=\"left\",\n",
" )\n",
" .join(volume_change.select(\"district\", \"count_2024\"), on=\"district\", how=\"left\")\n",
" .join(new_build_pct.select(\"district\", \"new_build_pct\"), on=\"district\", how=\"left\")\n",
")\n",
"\n",
"comparison_normalized = comparison_data.with_columns(\n",
" [\n",
" (\n",
" 100\n",
" - (pl.col(\"median\") - pl.col(\"median\").min())\n",
" / (pl.col(\"median\").max() - pl.col(\"median\").min())\n",
" * 100\n",
" ).alias(\"affordability_score\"),\n",
" (\n",
" (pl.col(\"cagr_5yr\") - pl.col(\"cagr_5yr\").min())\n",
" / (pl.col(\"cagr_5yr\").max() - pl.col(\"cagr_5yr\").min())\n",
" * 100\n",
" ).alias(\"growth_score\"),\n",
" pl.col(\"freehold_pct\").alias(\"freehold_score\"),\n",
" (\n",
" (pl.col(\"count_2024\") - pl.col(\"count_2024\").min())\n",
" / (pl.col(\"count_2024\").max() - pl.col(\"count_2024\").min())\n",
" * 100\n",
" ).alias(\"liquidity_score\"),\n",
" (\n",
" (pl.col(\"new_build_pct\") - pl.col(\"new_build_pct\").min())\n",
" / (pl.col(\"new_build_pct\").max() - pl.col(\"new_build_pct\").min())\n",
" * 100\n",
" ).alias(\"development_score\"),\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
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},
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"ticks": "",
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},
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"source": [
"# Borough Scores Heatmap\n",
"scores_only = (\n",
" comparison_normalized.select(\n",
" [\n",
" \"district\",\n",
" \"affordability_score\",\n",
" \"growth_score\",\n",
" \"freehold_score\",\n",
" \"liquidity_score\",\n",
" \"development_score\",\n",
" ]\n",
" )\n",
" .drop_nulls()\n",
" .sort(\"affordability_score\", descending=True)\n",
")\n",
"\n",
"fig = px.imshow(\n",
" scores_only.select(\n",
" [\n",
" \"affordability_score\",\n",
" \"growth_score\",\n",
" \"freehold_score\",\n",
" \"liquidity_score\",\n",
" \"development_score\",\n",
" ]\n",
" ).to_pandas(),\n",
" y=scores_only[\"district\"].to_list(),\n",
" x=[\"Affordability\", \"5yr Growth\", \"Freehold %\", \"Liquidity\", \"New Builds\"],\n",
" color_continuous_scale=\"RdYlGn\",\n",
" aspect=\"auto\",\n",
" title=\"Borough Scores Heatmap (Higher = Better)\",\n",
" labels={\"color\": \"Score\"},\n",
")\n",
"fig.update_layout(height=900)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
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5yr Growth (higher=faster)=%{y}
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"LAMBETH",
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"HACKNEY",
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"# Value vs Growth Quadrant\n",
"value_growth = comparison_normalized.drop_nulls()\n",
"median_afford = value_growth[\"affordability_score\"].median()\n",
"median_growth = value_growth[\"growth_score\"].median()\n",
"\n",
"fig = px.scatter(\n",
" value_growth.to_pandas(),\n",
" x=\"affordability_score\",\n",
" y=\"growth_score\",\n",
" text=\"district\",\n",
" size=\"liquidity_score\",\n",
" color=\"freehold_score\",\n",
" color_continuous_scale=\"Viridis\",\n",
" title=\"Value vs Growth Quadrant Analysis\",\n",
" labels={\n",
" \"affordability_score\": \"Affordability (higher=cheaper)\",\n",
" \"growth_score\": \"5yr Growth (higher=faster)\",\n",
" \"liquidity_score\": \"Liquidity\",\n",
" \"freehold_score\": \"Freehold %\",\n",
" },\n",
")\n",
"\n",
"fig.add_hline(y=median_growth, line_dash=\"dash\", line_color=\"gray\")\n",
"fig.add_vline(x=median_afford, line_dash=\"dash\", line_color=\"gray\")\n",
"fig.add_annotation(\n",
" x=75,\n",
" y=85,\n",
" text=\"Best Value
(Affordable+Growing)\",\n",
" showarrow=False,\n",
" font=dict(size=11),\n",
")\n",
"fig.add_annotation(\n",
" x=25,\n",
" y=85,\n",
" text=\"Premium Growth
(Expensive+Growing)\",\n",
" showarrow=False,\n",
" font=dict(size=11),\n",
")\n",
"fig.add_annotation(\n",
" x=75,\n",
" y=15,\n",
" text=\"Stable & Affordable
(Cheap+Steady)\",\n",
" showarrow=False,\n",
" font=dict(size=11),\n",
")\n",
"fig.add_annotation(\n",
" x=25, y=15, text=\"Caution
(Expensive+Slow)\", showarrow=False, font=dict(size=11)\n",
")\n",
"fig.update_traces(textposition=\"top center\", textfont_size=8)\n",
"fig.update_layout(height=700)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 15 Boroughs by Composite Score:\n",
"Weights: Affordability 30%, Growth 25%, Freehold 15%, Liquidity 15%, Development 15%\n",
"================================================================================\n"
]
},
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"\n",
"
shape: (15, 6)| rank | district | median_price_2023 | cagr_5yr | freehold_pct | composite_score |
|---|
| u32 | str | f64 | f64 | f64 | f64 |
| 1 | "HAVERING" | 430000.0 | 3.617519 | 76.223285 | 75.259025 |
| 2 | "BEXLEY" | 412500.0 | 3.465607 | 72.340211 | 73.353459 |
| 3 | "HILLINGDON" | 480000.0 | 3.456372 | 65.093495 | 70.892053 |
| 4 | "BARKING AND DAGENHAM" | 370000.0 | 3.035803 | 69.313581 | 70.371373 |
| 5 | "SUTTON" | 440000.0 | 3.243222 | 60.762622 | 67.429089 |
| 6 | "BROMLEY" | 495000.0 | 2.463801 | 66.713686 | 67.23558 |
| 7 | "ENFIELD" | 445000.0 | 2.985202 | 62.528182 | 66.932568 |
| 8 | "HOUNSLOW" | 465000.0 | 3.101548 | 51.219512 | 66.582225 |
| 9 | "REDBRIDGE" | 479247.5 | 3.239934 | 67.431674 | 65.814413 |
| 10 | "CROYDON" | 400000.0 | 1.299137 | 60.044056 | 64.948873 |
| 11 | "EALING" | 510000.0 | 2.152054 | 46.611766 | 64.712359 |
| 12 | "HARROW" | 505000.0 | 2.996745 | 59.198441 | 64.357919 |
| 13 | "WALTHAM FOREST" | 510000.0 | 2.724085 | 53.07114 | 63.110433 |
| 14 | "BARNET" | 540000.0 | 1.738493 | 48.428862 | 62.662661 |
| 15 | "BRENT" | 525000.0 | 2.678684 | 42.484243 | 61.798071 |