diff --git a/analysis.ipynb b/analysis.ipynb new file mode 100644 index 0000000..3b4a58a --- /dev/null +++ b/analysis.ipynb @@ -0,0 +1,22379 @@ +{ + "cells": [ + { + "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": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "polars.config.Config" + ] + }, + "execution_count": 2, + "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": 3, + "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": 4, + "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": 5, + "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": 6, + "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": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== National Price Statistics ===\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (1, 7)\n", + "┌─────┬───────────┬───────────────┬──────────┬─────────┬──────────┬──────────┐\n", + "│ min ┆ max ┆ mean ┆ median ┆ p25 ┆ p75 ┆ p95 │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═════╪═══════════╪═══════════════╪══════════╪═════════╪══════════╪══════════╡\n", + "│ 1 ┆ 900000000 ┆ 233512.848852 ┆ 157000.0 ┆ 88500.0 ┆ 260000.0 ┆ 580000.0 │\n", + "└─────┴───────────┴───────────────┴──────────┴─────────┴──────────┴──────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== London Price Statistics ===\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (1, 7)\n", + "┌─────┬───────────┬───────────────┬──────────┬──────────┬──────────┬──────────┐\n", + "│ min ┆ max ┆ mean ┆ median ┆ p25 ┆ p75 ┆ p95 │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞═════╪═══════════╪═══════════════╪══════════╪══════════╪══════════╪══════════╡\n", + "│ 1 ┆ 793020000 ┆ 435285.860816 ┆ 253000.0 ┆ 150000.0 ┆ 450000.0 ┆ 1.0528e6 │\n", + "└─────┴───────────┴───────────────┴──────────┴──────────┴──────────┴──────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_price_stats(lazy_frame):\n", + " return lazy_frame.select(\n", + " pl.col(\"price\").min().alias(\"min\"),\n", + " pl.col(\"price\").max().alias(\"max\"),\n", + " pl.col(\"price\").mean().alias(\"mean\"),\n", + " pl.col(\"price\").median().alias(\"median\"),\n", + " pl.col(\"price\").quantile(0.25).alias(\"p25\"),\n", + " pl.col(\"price\").quantile(0.75).alias(\"p75\"),\n", + " pl.col(\"price\").quantile(0.95).alias(\"p95\"),\n", + " ).collect()\n", + "\n", + "print(\"=== National Price Statistics ===\")\n", + "display(get_price_stats(lf))\n", + "print(\"\\n=== London Price Statistics ===\")\n", + "display(get_price_stats(lf.filter(LONDON_FILTER)))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "name": "National", + "type": "bar", + "x": [ + "Terraced", + "Semi-detached", + "Detached", + "Flat/Maisonette", + "Other" + ], + "xaxis": "x", + "y": { + "bdata": "73+LABVQgAARcmwAtLdUAK8yCQA=", + "dtype": "u4" + }, + "yaxis": "y" + }, + { + "marker": { + "color": "crimson" + }, + "name": "London", + "type": "bar", + "x": [ + "Flat/Maisonette", + "Terraced", + "Semi-detached", + "Detached", + "Other" + ], + "xaxis": "x2", + "y": { + 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"white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "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": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Property Type Distribution" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.45 + ] + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.55, + 1 + ] + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ] + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 1 + ] + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_property_types(lazy_frame):\n", + " return (\n", + " lazy_frame.group_by(\"property_type\")\n", + " .agg(pl.len().alias(\"count\"))\n", + " .sort(\"count\", descending=True)\n", + " .with_columns(pl.col(\"property_type\").replace(PROPERTY_TYPE_MAP).alias(\"type_name\"))\n", + " .collect()\n", + " )\n", + "\n", + "property_types_national = get_property_types(lf)\n", + "property_types_london = get_property_types(lf.filter(LONDON_FILTER))\n", + "\n", + "fig = make_subplots(rows=1, cols=2, subplot_titles=(\"National\", \"London\"))\n", + "fig.add_trace(go.Bar(x=property_types_national[\"type_name\"], y=property_types_national[\"count\"], name=\"National\"), row=1, col=1)\n", + "fig.add_trace(go.Bar(x=property_types_london[\"type_name\"], y=property_types_london[\"count\"], name=\"London\", 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get_tenure(lf.filter(LONDON_FILTER))\n", + "\n", + "fig = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]], subplot_titles=(\"National\", \"London\"))\n", + "fig.add_trace(go.Pie(labels=tenure_national[\"tenure_name\"], values=tenure_national[\"count\"], name=\"National\"), row=1, col=1)\n", + "fig.add_trace(go.Pie(labels=tenure_london[\"tenure_name\"], values=tenure_london[\"count\"], name=\"London\"), row=1, col=2)\n", + "fig.update_layout(title_text=\"Freehold vs Leasehold\", height=400)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 2. Geographic Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Transactions=%{x}
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Time Series Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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(pl.len().alias(\"count\"), pl.col(\"price\").mean().alias(\"avg_price\"), pl.col(\"price\").median().alias(\"median_price\"))\n", + " .sort(\"year\")\n", + " .collect()\n", + " )\n", + "\n", + "yearly_national = get_yearly_stats(lf)\n", + "yearly_london = get_yearly_stats(lf.filter(LONDON_FILTER))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines+markers", + "name": "National", + "type": "scatter", + "x": { + "bdata": 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.sort(\"month\")\n", + " .collect()\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(go.Scatter(x=monthly_national[\"month\"], y=monthly_national[\"avg_price\"], name=\"National\", mode=\"lines\"))\n", + "fig.add_trace(go.Scatter(x=monthly_london[\"month\"], y=monthly_london[\"avg_price\"], name=\"London\", mode=\"lines\", line=dict(color=\"crimson\")))\n", + "fig.update_layout(title=\"Monthly Average Price (2015 onwards)\", xaxis_title=\"Month\", yaxis_title=\"Average Price (£)\", height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Property Type=Detached
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"gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "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": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "London: Average Price by Property Type Over Time" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Year" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Average Price (£)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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(yearly_by_type_london.to_pandas(), x=\"year\", y=\"avg_price\", color=\"type_name\",\n", + " title=\"London: Average Price by Property Type Over Time\",\n", + " labels={\"avg_price\": \"Average Price (£)\", \"year\": \"Year\", \"type_name\": \"Property Type\"}, markers=True)\n", + "fig.update_layout(height=500)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines", + "name": "Monthly", + "opacity": 0.5, + "type": "scatter", + "x": [ + "2015-01-01T00:00:00.000000", + "2015-02-01T00:00:00.000000", + "2015-03-01T00:00:00.000000", + "2015-04-01T00:00:00.000000", + "2015-05-01T00:00:00.000000", + "2015-06-01T00:00:00.000000", + "2015-07-01T00:00:00.000000", + "2015-08-01T00:00:00.000000", + 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"title": { + "text": "Price Index (1995 = 100)" + }, + "xaxis": { + "title": { + "text": "Year" + } + }, + "yaxis": { + "title": { + "text": "Index" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "base_price_national = yearly_national.filter(pl.col(\"year\") == 1995)[\"avg_price\"][0]\n", + "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((pl.col(\"avg_price\") / base_price_london * 100).alias(\"london_index\")).select(\"year\", \"london_index\"),\n", + " on=\"year\"\n", + ")\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(go.Scatter(x=yearly_indexed[\"year\"], y=yearly_indexed[\"national_index\"], name=\"National\", mode=\"lines+markers\"))\n", + "fig.add_trace(go.Scatter(x=yearly_indexed[\"year\"], y=yearly_indexed[\"london_index\"], name=\"London\", mode=\"lines+markers\", line=dict(color=\"crimson\")))\n", + "fig.add_hline(y=100, line_dash=\"dash\", line_color=\"gray\", annotation_text=\"1995 Baseline\")\n", + "fig.update_layout(title=\"Price Index (1995 = 100)\", xaxis_title=\"Year\", yaxis_title=\"Index\", height=500)\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": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Sales Volume=1212
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districtprice_2014price_2019price_2024cagr_10yrcagr_5yr
strf64f64f64f64f64
"HAVERING"250000.0360000.0430000.05.5729963.617519
"BEXLEY"250000.0350000.0415000.05.1988063.465607
"HILLINGDON"302250.0405000.0480000.04.7339523.456372
"SUTTON"285000.0380000.0445750.04.5742243.243222
"REDBRIDGE"298300.0405000.0475000.04.7620613.239934
"HOUNSLOW"315000.0400000.0466000.03.9938213.101548
"BARKING AND DAGENHAM"215000.0310000.0360000.05.2898253.035803
"HARROW"365000.0440000.0510000.03.4017122.996745
"ENFIELD"280000.0385000.0446000.04.7653542.985202
"WALTHAM FOREST"317408.5440625.0504000.04.7324342.724085
………………
"WANDSWORTH"528000.0610000.0635000.01.8624180.806556
"GREENWICH"300000.0435000.0450000.04.1379740.680335
"CAMDEN"670000.0760000.0780000.01.5317750.520862
"SOUTHWARK"415000.0540000.0552000.02.8937740.440546
"TOWER HAMLETS"380000.0485000.0492500.02.6271490.307383
"KENSINGTON AND CHELSEA"1.195e61.2e61.2e60.0417620.0
"LAMBETH"418000.0530000.0530000.02.4023580.0
"CITY OF WESTMINSTER"885000.0960000.0956000.00.774688-0.083473
"HACKNEY"434847.5600000.0590000.03.0983-0.335578
"CITY OF LONDON"790000.0950000.0901250.01.326215-1.048055
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title=\"Market Activity Change: 2024 vs 2019 Transaction Volume (%)\",\n", + " color=\"pct_change\", color_continuous_scale=\"RdYlGn\",\n", + " labels={\"pct_change\": \"Volume Change (%)\", \"district\": \"Borough\"})\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": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "New Build %=%{marker.color}
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"white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "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": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "New Build Share by Borough (2020+)" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "New Build %" + } + }, + "yaxis": { + "anchor": "x", + "categoryorder": "total ascending", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Borough" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# New Build Share by Borough\n", + "new_build_rate = (\n", + " lf.filter(LONDON_FILTER).filter(pl.col(\"date_of_transfer\").dt.year() >= 2020)\n", + " .group_by(\"district\", 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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": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Borough Price Ranges (2023+) - Find Your Budget" + }, + "xaxis": { + "title": { + "text": "Price (£)" + } + }, + "yaxis": { + "categoryorder": "total ascending" + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Budget Finder: Borough Price Ranges\n", + "recent_prices = (\n", + " lf.filter(LONDON_FILTER).filter(pl.col(\"date_of_transfer\").dt.year() >= 2023)\n", + " .group_by(\"district\").agg(pl.col(\"price\").quantile(0.25).alias(\"p25\"), pl.col(\"price\").median().alias(\"median\"), pl.col(\"price\").quantile(0.75).alias(\"p75\"))\n", + " .sort(\"median\").collect()\n", + ")\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(go.Bar(name=\"25th percentile\", y=recent_prices[\"district\"], x=recent_prices[\"p25\"], orientation=\"h\", marker_color=\"lightgreen\"))\n", + "fig.add_trace(go.Bar(name=\"Median\", y=recent_prices[\"district\"], x=recent_prices[\"median\"] - recent_prices[\"p25\"], orientation=\"h\", marker_color=\"steelblue\", base=recent_prices[\"p25\"]))\n", + "fig.add_trace(go.Bar(name=\"75th percentile\", y=recent_prices[\"district\"], x=recent_prices[\"p75\"] - recent_prices[\"median\"], orientation=\"h\", marker_color=\"coral\", base=recent_prices[\"median\"]))\n", + "\n", + "for budget in [300000, 500000, 750000, 1000000]:\n", + " fig.add_vline(x=budget, line_dash=\"dash\", line_color=\"gray\", annotation_text=f\"£{budget//1000}k\", annotation_position=\"top\")\n", + "\n", + "fig.update_layout(title=\"Borough Price Ranges (2023+) - Find Your Budget\", xaxis_title=\"Price (£)\",\n", + " yaxis={\"categoryorder\": \"total ascending\"}, barmode=\"stack\", height=900, legend_title=\"Price Point\")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "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(freehold_pct.select(\"district\", \"percentage\").rename({\"percentage\": \"freehold_pct\"}), on=\"district\", how=\"left\")\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", + " (100 - (pl.col(\"median\") - pl.col(\"median\").min()) / (pl.col(\"median\").max() - pl.col(\"median\").min()) * 100).alias(\"affordability_score\"),\n", + " ((pl.col(\"cagr_5yr\") - pl.col(\"cagr_5yr\").min()) / (pl.col(\"cagr_5yr\").max() - pl.col(\"cagr_5yr\").min()) * 100).alias(\"growth_score\"),\n", + " pl.col(\"freehold_pct\").alias(\"freehold_score\"),\n", + " ((pl.col(\"count_2024\") - pl.col(\"count_2024\").min()) / (pl.col(\"count_2024\").max() - pl.col(\"count_2024\").min()) * 100).alias(\"liquidity_score\"),\n", + " ((pl.col(\"new_build_pct\") - pl.col(\"new_build_pct\").min()) / (pl.col(\"new_build_pct\").max() - pl.col(\"new_build_pct\").min()) * 100).alias(\"development_score\"),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + 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"standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Value vs Growth Quadrant Analysis" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Affordability (higher=cheaper)" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "5yr Growth (higher=faster)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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(value_growth.to_pandas(), x=\"affordability_score\", y=\"growth_score\", text=\"district\",\n", + " size=\"liquidity_score\", color=\"freehold_score\", color_continuous_scale=\"Viridis\",\n", + " title=\"Value vs Growth Quadrant Analysis\",\n", + " labels={\"affordability_score\": \"Affordability (higher=cheaper)\", \"growth_score\": \"5yr Growth (higher=faster)\", \"liquidity_score\": \"Liquidity\", \"freehold_score\": \"Freehold %\"})\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(x=75, y=85, text=\"Best Value
(Affordable+Growing)\", showarrow=False, font=dict(size=11))\n", + "fig.add_annotation(x=25, y=85, text=\"Premium Growth
(Expensive+Growing)\", showarrow=False, font=dict(size=11))\n", + "fig.add_annotation(x=75, y=15, text=\"Stable & Affordable
(Cheap+Steady)\", showarrow=False, font=dict(size=11))\n", + "fig.add_annotation(x=25, y=15, text=\"Caution
(Expensive+Slow)\", showarrow=False, font=dict(size=11))\n", + "fig.update_traces(textposition=\"top center\", textfont_size=8)\n", + "fig.update_layout(height=700)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "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" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (15, 6)
rankdistrictmedian_price_2023cagr_5yrfreehold_pctcomposite_score
u32strf64f64f64f64
1"HAVERING"430000.03.61751976.22328575.259025
2"BEXLEY"412500.03.46560772.34021173.353459
3"HILLINGDON"480000.03.45637265.09349570.892053
4"BARKING AND DAGENHAM"370000.03.03580369.31358170.371373
5"SUTTON"440000.03.24322260.76262267.429089
6"BROMLEY"495000.02.46380166.71368667.23558
7"ENFIELD"445000.02.98520262.52818266.932568
8"HOUNSLOW"465000.03.10154851.21951266.582225
9"REDBRIDGE"479247.53.23993467.43167465.814413
10"CROYDON"400000.01.29913760.04405664.948873
11"EALING"510000.02.15205446.61176664.712359
12"HARROW"505000.02.99674559.19844164.357919
13"WALTHAM FOREST"510000.02.72408553.0711463.110433
14"BARNET"540000.01.73849348.42886262.662661
15"BRENT"525000.02.67868442.48424361.798071
" + ], + "text/plain": [ + "shape: (15, 6)\n", + "┌──────┬──────────────────────┬───────────────────┬──────────┬──────────────┬─────────────────┐\n", + "│ rank ┆ district ┆ median_price_2023 ┆ cagr_5yr ┆ freehold_pct ┆ composite_score │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ u32 ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞══════╪══════════════════════╪═══════════════════╪══════════╪══════════════╪═════════════════╡\n", + "│ 1 ┆ HAVERING ┆ 430000.0 ┆ 3.617519 ┆ 76.223285 ┆ 75.259025 │\n", + "│ 2 ┆ BEXLEY ┆ 412500.0 ┆ 3.465607 ┆ 72.340211 ┆ 73.353459 │\n", + "│ 3 ┆ HILLINGDON ┆ 480000.0 ┆ 3.456372 ┆ 65.093495 ┆ 70.892053 │\n", + "│ 4 ┆ BARKING AND DAGENHAM ┆ 370000.0 ┆ 3.035803 ┆ 69.313581 ┆ 70.371373 │\n", + "│ 5 ┆ SUTTON ┆ 440000.0 ┆ 3.243222 ┆ 60.762622 ┆ 67.429089 │\n", + "│ 6 ┆ BROMLEY ┆ 495000.0 ┆ 2.463801 ┆ 66.713686 ┆ 67.23558 │\n", + "│ 7 ┆ ENFIELD ┆ 445000.0 ┆ 2.985202 ┆ 62.528182 ┆ 66.932568 │\n", + "│ 8 ┆ HOUNSLOW ┆ 465000.0 ┆ 3.101548 ┆ 51.219512 ┆ 66.582225 │\n", + "│ 9 ┆ REDBRIDGE ┆ 479247.5 ┆ 3.239934 ┆ 67.431674 ┆ 65.814413 │\n", + "│ 10 ┆ CROYDON ┆ 400000.0 ┆ 1.299137 ┆ 60.044056 ┆ 64.948873 │\n", + "│ 11 ┆ EALING ┆ 510000.0 ┆ 2.152054 ┆ 46.611766 ┆ 64.712359 │\n", + "│ 12 ┆ HARROW ┆ 505000.0 ┆ 2.996745 ┆ 59.198441 ┆ 64.357919 │\n", + "│ 13 ┆ WALTHAM FOREST ┆ 510000.0 ┆ 2.724085 ┆ 53.07114 ┆ 63.110433 │\n", + "│ 14 ┆ BARNET ┆ 540000.0 ┆ 1.738493 ┆ 48.428862 ┆ 62.662661 │\n", + "│ 15 ┆ BRENT ┆ 525000.0 ┆ 2.678684 ┆ 42.484243 ┆ 61.798071 │\n", + "└──────┴──────────────────────┴───────────────────┴──────────┴──────────────┴─────────────────┘" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Borough Rankings with Composite Score\n", + "WEIGHTS = {\"affordability\": 0.30, \"growth\": 0.25, \"freehold\": 0.15, \"liquidity\": 0.15, \"development\": 0.15}\n", + "\n", + "ranking = (\n", + " comparison_normalized.drop_nulls()\n", + " .with_columns(\n", + " (pl.col(\"affordability_score\") * WEIGHTS[\"affordability\"] +\n", + " pl.col(\"growth_score\") * WEIGHTS[\"growth\"] +\n", + " pl.col(\"freehold_score\") * WEIGHTS[\"freehold\"] +\n", + " pl.col(\"liquidity_score\") * WEIGHTS[\"liquidity\"] +\n", + " pl.col(\"development_score\") * WEIGHTS[\"development\"]).alias(\"composite_score\")\n", + " )\n", + " .select([\"district\", pl.col(\"median\").alias(\"median_price_2023\"), \"cagr_5yr\", \"freehold_pct\", \"composite_score\"])\n", + " .sort(\"composite_score\", descending=True)\n", + " .with_row_index(\"rank\", offset=1)\n", + ")\n", + "\n", + "print(\"Top 15 Boroughs by Composite Score:\")\n", + "print(\"Weights: Affordability 30%, Growth 25%, Freehold 15%, Liquidity 15%, Development 15%\")\n", + "print(\"=\"*80)\n", + "ranking.head(15)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}