{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eb485cfb",
   "metadata": {},
   "source": [
    "# New-builds vs existing houses in London — price gap & appreciation\n",
    "\n",
    "**Question.** Are new-build houses priced systematically above or below comparable existing\n",
    "houses, and do they appreciate differently afterwards? We test this on our own\n",
    "`properties.parquet` (HM Land Registry Price Paid + EPC), restricted as requested to\n",
    "**terraced and detached houses in London**.\n",
    "\n",
    "**Two things we measure**\n",
    "\n",
    "1. **Price gap (the \"new-build premium\").** Median £/sqm of new-build sales vs existing\n",
    "   (resale) sales, by year and by London area. A positive gap means new-builds change hands\n",
    "   dearer per square metre than the established stock around them.\n",
    "2. **Appreciation / depreciation.** For properties that sold more than once, the annualised\n",
    "   price growth (CAGR) of homes first sold as new-builds vs homes only ever resold. This is\n",
    "   where the \"new-car effect\" shows up: if the new premium is real but unrecoverable, new-builds\n",
    "   underperform on resale.\n",
    "\n",
    "**How a sale is flagged as a new-build.** The pipeline now keeps the PPD `old_new` flag on each\n",
    "transaction inside `historical_prices` as `is_new`. This notebook uses that per-sale flag when the\n",
    "parquet carries it. For a parquet generated *before* that change, it falls back to the exact\n",
    "equivalent the pipeline already encodes: `Is construction date approximate == 0` marks a property\n",
    "whose construction date came from a new-build first transfer (i.e. `old_new == \"Y\"`), and the\n",
    "new-build sale is that first transfer (sale year == construction year). Both routes identify the\n",
    "same developer sale; the cell below prints which one is active.\n",
    "\n",
    "**Caveats baked into the reading (see the final section).** Recorded new-build prices include\n",
    "developer incentives (stamp duty paid, upgrades) that HM Land Registry does *not* strip, so they\n",
    "overstate true value; £/sqm controls for size but not spec/micro-location; CAGRs are nominal; and\n",
    "terraced/detached new-builds are a thin slice in London, so we report N everywhere and apply\n",
    "minimum sample sizes, mirroring the defensibility rules in `cheaper_twins.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a4fe494",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-28T11:01:55.703805Z",
     "iopub.status.busy": "2026-06-28T11:01:55.703729Z",
     "iopub.status.idle": "2026-06-28T11:01:55.964404Z",
     "shell.execute_reply": "2026-06-28T11:01:55.964020Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import polars as pl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"figure.figsize\": (9, 4.5),\n",
    "    \"figure.dpi\": 110,\n",
    "    \"axes.grid\": True,\n",
    "    \"grid.alpha\": 0.25,\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"font.size\": 10,\n",
    "})\n",
    "\n",
    "def _find_data_dir() -> Path:\n",
    "    # Locate property-data/ whether the notebook runs from analysis/ or the repo root.\n",
    "    here = Path.cwd()\n",
    "    for base in (here, *here.parents):\n",
    "        cand = base / \"property-data\"\n",
    "        if (cand / \"properties.parquet\").exists():\n",
    "            return cand\n",
    "    raise FileNotFoundError(\"Could not locate property-data/properties.parquet from CWD upward\")\n",
    "\n",
    "DATA = _find_data_dir()\n",
    "\n",
    "# London = the eight London postal areas (their districts are the \"areas\" we split on).\n",
    "LONDON_AREAS = [\"E\", \"EC\", \"N\", \"NW\", \"SE\", \"SW\", \"W\", \"WC\"]\n",
    "HOUSE_TYPES = [\"Terraced\", \"Detached\"]\n",
    "\n",
    "# Quality filters (robust medians; drop obvious data errors).\n",
    "MIN_PRICE, MAX_PRICE = 10_000, 50_000_000\n",
    "MIN_AREA, MAX_AREA = 20, 1_500          # sqm\n",
    "MIN_PSQM, MAX_PSQM = 500, 60_000        # £/sqm sanity band\n",
    "MIN_HOLD_YEARS = 2                       # need a real holding span for CAGR\n",
    "MIN_CELL_N = 30                          # minimum new-build obs to report an area\n",
    "START_YEAR = 2005                        # PPD coverage is reliable from here\n",
    "\n",
    "AREA_RE = r\"^([A-Z]{1,2})[0-9]\"               # postal area: SW11 -> SW\n",
    "DISTRICT_RE = r\"^([A-Z]{1,2}[0-9][A-Z0-9]?)\"  # outward code: SW11 2AB -> SW11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f1dd6f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-28T11:01:55.965841Z",
     "iopub.status.busy": "2026-06-28T11:01:55.965714Z",
     "iopub.status.idle": "2026-06-28T11:01:56.329401Z",
     "shell.execute_reply": "2026-06-28T11:01:56.328975Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "historical_prices fields: ['year', 'month', 'price']\n",
      "Using per-sale `is_new` flag: False -> falling back to `Is construction date approximate == 0`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "London terraced/detached properties: 333,638\n",
      "  of which first sold as new-build: 11,377 (3.4%)\n",
      "shape: (2, 2)\n",
      "┌───────────────┬────────┐\n",
      "│ Property type ┆ len    │\n",
      "│ ---           ┆ ---    │\n",
      "│ str           ┆ u32    │\n",
      "╞═══════════════╪════════╡\n",
      "│ Detached      ┆ 20936  │\n",
      "│ Terraced      ┆ 312702 │\n",
      "└───────────────┴────────┘\n"
     ]
    }
   ],
   "source": [
    "# Does this parquet already carry the per-sale `is_new` flag?\n",
    "schema = pl.scan_parquet(DATA / \"properties.parquet\").collect_schema()\n",
    "HP_FIELDS = [f.name for f in schema[\"historical_prices\"].inner.fields]\n",
    "HAS_IS_NEW = \"is_new\" in HP_FIELDS\n",
    "print(\"historical_prices fields:\", HP_FIELDS)\n",
    "print(\"Using per-sale `is_new` flag:\" , HAS_IS_NEW,\n",
    "      \"\" if HAS_IS_NEW else \"-> falling back to `Is construction date approximate == 0`\")\n",
    "\n",
    "# Load only what we need, filter to London terraced/detached, collect (streaming).\n",
    "props = (\n",
    "    pl.scan_parquet(DATA / \"properties.parquet\")\n",
    "    .select(\n",
    "        \"Postcode\", \"Property type\", \"Total floor area (sqm)\",\n",
    "        \"Construction year\", \"Is construction date approximate\", \"historical_prices\",\n",
    "    )\n",
    "    .with_columns(\n",
    "        pl.col(\"Postcode\").str.extract(AREA_RE, 1).alias(\"area\"),\n",
    "        pl.col(\"Postcode\").str.extract(DISTRICT_RE, 1).alias(\"district\"),\n",
    "    )\n",
    "    .filter(pl.col(\"area\").is_in(LONDON_AREAS))\n",
    "    .filter(pl.col(\"Property type\").is_in(HOUSE_TYPES))\n",
    "    .filter(pl.col(\"Total floor area (sqm)\").is_between(MIN_AREA, MAX_AREA))\n",
    "    .collect(engine=\"streaming\")\n",
    ")\n",
    "\n",
    "# Property-level new-build flag (a home first sold as a new-build).\n",
    "if HAS_IS_NEW:\n",
    "    is_newbuild_prop = pl.col(\"historical_prices\").list.first().struct.field(\"is_new\")\n",
    "else:\n",
    "    is_newbuild_prop = pl.col(\"Is construction date approximate\") == 0\n",
    "props = props.with_columns(is_newbuild_prop.fill_null(False).alias(\"is_newbuild_prop\"))\n",
    "\n",
    "n = props.height\n",
    "n_new = int(props[\"is_newbuild_prop\"].sum())\n",
    "print(f\"\\nLondon terraced/detached properties: {n:,}\")\n",
    "print(f\"  of which first sold as new-build: {n_new:,} ({100*n_new/n:.1f}%)\")\n",
    "print(props.group_by(\"Property type\").len().sort(\"Property type\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71de6d1",
   "metadata": {},
   "source": [
    "## 1. Price gap — the new-build premium (£/sqm)\n",
    "\n",
    "We explode each property's sale history into one row per transaction, compute £/sqm using the\n",
    "property's floor area, and label each sale new-build or existing. £/sqm is the right lens because\n",
    "it neutralises the fact that new houses are often a bit larger or smaller than the resale stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35b0be89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-28T11:01:56.330386Z",
     "iopub.status.busy": "2026-06-28T11:01:56.330290Z",
     "iopub.status.idle": "2026-06-28T11:01:56.371873Z",
     "shell.execute_reply": "2026-06-28T11:01:56.371449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sale rows: 417,721  | new-build sales: 11,388\n"
     ]
    }
   ],
   "source": [
    "# One row per past sale, with £/sqm and a per-sale new-build flag.\n",
    "sales = (\n",
    "    props.explode(\"historical_prices\")\n",
    "    .filter(pl.col(\"historical_prices\").is_not_null())\n",
    "    .unnest(\"historical_prices\")\n",
    "    .filter(pl.col(\"price\").is_between(MIN_PRICE, MAX_PRICE))\n",
    "    .with_columns((pl.col(\"price\") / pl.col(\"Total floor area (sqm)\")).alias(\"psqm\"))\n",
    "    .filter(pl.col(\"psqm\").is_between(MIN_PSQM, MAX_PSQM))\n",
    ")\n",
    "\n",
    "if HAS_IS_NEW:\n",
    "    sale_is_new = pl.col(\"is_new\")\n",
    "else:\n",
    "    # The new-build sale is the first transfer: approx==0 and sold in the build year.\n",
    "    sale_is_new = (pl.col(\"Is construction date approximate\") == 0) & (\n",
    "        pl.col(\"year\") == pl.col(\"Construction year\")\n",
    "    )\n",
    "sales = sales.with_columns(sale_is_new.fill_null(False).alias(\"sale_is_new\"))\n",
    "\n",
    "print(f\"sale rows: {sales.height:,}  | new-build sales: {int(sales['sale_is_new'].sum()):,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57bf59d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-28T11:01:56.372720Z",
     "iopub.status.busy": "2026-06-28T11:01:56.372645Z",
     "iopub.status.idle": "2026-06-28T11:01:56.498944Z",
     "shell.execute_reply": "2026-06-28T11:01:56.498635Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1320x495 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (21, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>year</th><th>existing</th><th>new</th><th>premium_pct</th><th>n_new</th><th>n_existing</th></tr><tr><td>i32</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td><td>u32</td></tr></thead><tbody><tr><td>2005</td><td>2721.706239</td><td>3048.719512</td><td>12.01501</td><td>313</td><td>14160</td></tr><tr><td>2006</td><td>2965.517241</td><td>3536.585366</td><td>19.256948</td><td>229</td><td>17414</td></tr><tr><td>2007</td><td>3316.561181</td><td>4080.313014</td><td>23.028426</td><td>160</td><td>16402</td></tr><tr><td>2008</td><td>3260.869565</td><td>3971.915888</td><td>21.805421</td><td>59</td><td>7501</td></tr><tr><td>2009</td><td>3259.259259</td><td>3994.08284</td><td>22.545724</td><td>95</td><td>7179</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2021</td><td>7190.082645</td><td>6183.501684</td><td>-13.999574</td><td>172</td><td>17430</td></tr><tr><td>2022</td><td>7785.262206</td><td>7007.042254</td><td>-9.996066</td><td>157</td><td>14612</td></tr><tr><td>2023</td><td>7672.643067</td><td>7328.301887</td><td>-4.487908</td><td>75</td><td>11148</td></tr><tr><td>2024</td><td>7692.307692</td><td>7114.752006</td><td>-7.508224</td><td>90</td><td>12866</td></tr><tr><td>2025</td><td>7767.649502</td><td>6830.042017</td><td>-12.070672</td><td>28</td><td>12678</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (21, 6)\n",
       "┌──────┬─────────────┬─────────────┬─────────────┬───────┬────────────┐\n",
       "│ year ┆ existing    ┆ new         ┆ premium_pct ┆ n_new ┆ n_existing │\n",
       "│ ---  ┆ ---         ┆ ---         ┆ ---         ┆ ---   ┆ ---        │\n",
       "│ i32  ┆ f64         ┆ f64         ┆ f64         ┆ u32   ┆ u32        │\n",
       "╞══════╪═════════════╪═════════════╪═════════════╪═══════╪════════════╡\n",
       "│ 2005 ┆ 2721.706239 ┆ 3048.719512 ┆ 12.01501    ┆ 313   ┆ 14160      │\n",
       "│ 2006 ┆ 2965.517241 ┆ 3536.585366 ┆ 19.256948   ┆ 229   ┆ 17414      │\n",
       "│ 2007 ┆ 3316.561181 ┆ 4080.313014 ┆ 23.028426   ┆ 160   ┆ 16402      │\n",
       "│ 2008 ┆ 3260.869565 ┆ 3971.915888 ┆ 21.805421   ┆ 59    ┆ 7501       │\n",
       "│ 2009 ┆ 3259.259259 ┆ 3994.08284  ┆ 22.545724   ┆ 95    ┆ 7179       │\n",
       "│ …    ┆ …           ┆ …           ┆ …           ┆ …     ┆ …          │\n",
       "│ 2021 ┆ 7190.082645 ┆ 6183.501684 ┆ -13.999574  ┆ 172   ┆ 17430      │\n",
       "│ 2022 ┆ 7785.262206 ┆ 7007.042254 ┆ -9.996066   ┆ 157   ┆ 14612      │\n",
       "│ 2023 ┆ 7672.643067 ┆ 7328.301887 ┆ -4.487908   ┆ 75    ┆ 11148      │\n",
       "│ 2024 ┆ 7692.307692 ┆ 7114.752006 ┆ -7.508224   ┆ 90    ┆ 12866      │\n",
       "│ 2025 ┆ 7767.649502 ┆ 6830.042017 ┆ -12.070672  ┆ 28    ┆ 12678      │\n",
       "└──────┴─────────────┴─────────────┴─────────────┴───────┴────────────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median £/sqm by sale year, new-build vs existing (only years with enough new-build sales).\n",
    "MIN_YEAR_N = 20\n",
    "by_year = (\n",
    "    sales.filter(pl.col(\"year\") >= START_YEAR)\n",
    "    .group_by(\"year\", \"sale_is_new\")\n",
    "    .agg(pl.col(\"psqm\").median().alias(\"med_psqm\"), pl.len().alias(\"n\"))\n",
    "    .sort(\"year\")\n",
    ")\n",
    "piv = (\n",
    "    by_year.pivot(values=[\"med_psqm\", \"n\"], index=\"year\", on=\"sale_is_new\")\n",
    "    .rename({\"med_psqm_true\": \"new\", \"med_psqm_false\": \"existing\",\n",
    "             \"n_true\": \"n_new\", \"n_false\": \"n_existing\"})\n",
    "    .with_columns((100 * (pl.col(\"new\") / pl.col(\"existing\") - 1)).alias(\"premium_pct\"))\n",
    "    .filter((pl.col(\"n_new\") >= MIN_YEAR_N) & pl.col(\"existing\").is_not_null())\n",
    "    .sort(\"year\")\n",
    ")\n",
    "\n",
    "yr = piv[\"year\"].to_numpy()\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4.5))\n",
    "ax1.plot(yr, piv[\"existing\"].to_numpy(), marker=\"o\", label=\"Existing houses\", color=\"#0f766e\")\n",
    "ax1.plot(yr, piv[\"new\"].to_numpy(), marker=\"s\", label=\"New-build houses\", color=\"#d97706\")\n",
    "ax1.set_title(\"Median £/sqm by sale year\")\n",
    "ax1.set_xlabel(\"Year\"); ax1.set_ylabel(\"£/sqm\")\n",
    "ax1.yaxis.set_major_formatter(mticker.StrMethodFormatter(\"£{x:,.0f}\"))\n",
    "ax1.legend()\n",
    "\n",
    "colors = [\"#0f766e\" if v >= 0 else \"#b91c1c\" for v in piv[\"premium_pct\"]]\n",
    "ax2.bar(yr, piv[\"premium_pct\"].to_numpy(), color=colors)\n",
    "ax2.axhline(0, color=\"black\", lw=0.8)\n",
    "ax2.set_title(\"New-build premium (median £/sqm, new vs existing)\")\n",
    "ax2.set_xlabel(\"Year\"); ax2.set_ylabel(\"Premium %\")\n",
    "ax2.yaxis.set_major_formatter(mticker.PercentFormatter(decimals=0))\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "piv.select(\"year\", \"existing\", \"new\", \"premium_pct\", \"n_new\", \"n_existing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6cc9db0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-28T11:01:56.499843Z",
     "iopub.status.busy": "2026-06-28T11:01:56.499765Z",
     "iopub.status.idle": "2026-06-28T11:01:56.559803Z",
     "shell.execute_reply": "2026-06-28T11:01:56.559537Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New-build premium by London postal area (sales since 2015, min 30 new sales):\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA84AAAHjCAYAAADyuEaZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAQ6wAAEOsBUJTofAAAcolJREFUeJzt3Xd4VGXexvF7MoH0Rgg9ELogVXrv6CJSFwSlROki0gTBshRRXESwgIKIsiK7KIpIUaogmIBiWVCqIL1IC4EAIcnkef9gMy9DkkMSA8PI93NduWCe85xznjO/OZO5c8rYjDFGAAAAAAAgQ17uHgAAAAAAAHcygjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgA8QlRUlJo2bXpLlh0dHS2bzebSNn78eNlsNh08eDBLy2jatKmioqJyf3C4Yxw8eFA2m03jx49391AydSv3k7+67O7znu7DDz+Uj4+PDh06lO15eZ3dHg6HQ+XLl9eAAQPcPRQAIjgDd5UNGzbIZrPJZrNp2rRpGfYpU6YMAfA2OXv2rPr166dixYopb968KlasmLp06aKTJ09m2P/FF1+Ur6+vLl68eJtHCty5xo8fryVLlrh7GB7l0qVLGjt2rAYMGKASJUq4ezi3VGJiot577z116tRJpUuXlp+fnyIjI9WmTRutX78+0/k2btyo5s2bKygoSEFBQWrevLk2bdqUrl9CQoImTpyoDh06qESJErLZbCpTpkymy503b57z9/CNPzVr1nTpa7fbNWHCBM2dO1fbt2/P+ZMAIFd4u3sAANzjpZde0uOPP67Q0FB3D8Xt5syZo1mzZt329bZr106bN29W7969Va9ePZ0/f14rV67UqVOnVKhQoXT9P/vsM7Vs2VJBQUG3fayQSpQooStXrsjbm1+dd5IJEyaod+/e6tChg7uH4jFmz56tEydOaMSIETmaf8+ePenO0rlTHTx4UP369VO9evXUq1cvRUZG6siRI5o9e7aaN2+uf/7znxo9erTLPKtWrVLbtm1VtGhRjRs3Tj4+Pnr33XfVvHlzffXVV2rZsqWz75kzZzRu3DgVKFBA1atX17lz57I0rmeffVYVKlRwaQsPD0/Xr2vXrho1apQmTpyoTz/9NAfPAIDcwm9/4C5Uq1Ytbd26VS+99JJeffVVdw/H7fLkyaM8efLc1nX+8ssvio2NVa9evfTBBx8422/8AJfm999/17Zt2/TUU0/driHekZKSkpSamipfX9/bvm6bzeaW9eKvxxijS5cuKTAw0C3rfuedd/7U5SU+Pj65O6hbKCIiQj/++KPuu+8+l/b+/furUqVKeuGFF9SvXz+FhYVJunZ69MCBA+Xj46ONGzeqePHikqRevXrp3nvv1aBBg7Rnzx55eV07abNw4cI6fPiwIiMjJSnLz2mrVq2ydLq7l5eXevbsqVdffVXHjx9XkSJFsrjlAHIbp2oDd6F27dqpYcOGeuutt7J8fdv+/fsVHR2tIkWKOE8rfuKJJ3TmzBlnn9jYWNlsNr311lsu87Zp00Y2m00vvviiS/sjjzwif39/Xb16Nctj37Ztm1q3bq2goCCFhISoU6dO2r9/v0uftFPhNmzYkG7+jK5nzqgtM/v27VPHjh0VEhKioKAgtW7dWtu2bcvy+NPs2bNHklSnTp0s9f/ss89kt9vVvn17Z9uCBQtUr1495cuXT35+fipevLg6deqknTt3usz7448/qmXLlgoICFBYWJg6d+6sgwcPZnidos1mU3R0tDZu3KiGDRsqICBABQsW1DPPPCOHw6GrV69qzJgxioyMlK+vr2rVqqUtW7ZkaRvSLhWYN2+e3nnnHVWoUEG+vr6KiorS+PHjlZKS4tI/rS5nz55V//79VbhwYfn5+TnXl5SUpClTpqhKlSry8/NTcHCwWrZsqY0bN7os5/prkxcvXqz77rvP+XxNnTpVkhQfH68BAwaoUKFC8vPzU/PmzbV3795Ml5PRNt0oo2tm07bp3Llz6tu3rwoUKKDAwEC1bt1av/32myRp6dKlqlWrlvz9/VW0aFFNnjw5S8/v9W62n5w4cUJ58uRR165dM5x/2rRpstlsWr58ueV60rZx586dGjFihIoWLSpfX19VrVpVCxcuzHCelStXqlmzZgoODpafn5+qVaummTNnyhjj0u/o0aPq37+/SpYsKV9fX+XPn181atTQyy+/LOn/n3tJ+te//uVyymua1atXq3v37s5TdIODg9W4cWMtW7bs5k+ihd27d2vw4MGqVKmSQkJC5Ofnp8qVK2vq1KlyOBwufdPej9auXavJkyerXLly8vHxcb72pGv7d5MmTZzPSfXq1fXee++lW29ubM+PP/6offv26cEHH0w3LS4uTqNGjVLZsmXl5+ensLAwVa5cWcOGDXPpl9F7R1rb3r171b59e4WEhCgwMFBt2rTRvn370q3LGKN58+apQYMGCg4Olr+/v+655x499dRTSkpKcumb1ecnI+Hh4elCs3Qt8DZp0kRJSUnO92NJ2rRpkw4ePKguXbo4Q7MkhYSEqG/fvtq3b59iY2Od7T4+Ps7QnF0JCQlZ+v334IMPKiUlRZ999lmO1gMgd3DEGbhLTZ06VXXr1tVzzz2njz76yLLvf//7XzVt2lT+/v56/PHHVaJECf3222965513tG7dOn3//fcKCQlR7dq1FRQUpLVr12rIkCGSpOTkZG3cuFFeXl5au3atXnjhBUnXPjR9/fXXatiwYZaPXhw9elTNmjVTu3btNGXKFO3atUuzZs1SbGysfvzxRxUtWvTPPSk3ceTIEdWvX1/nz5/XwIEDVaFCBW3ZskVNmzbN8BQ7K4mJiZIkf3//LPVfvHixmjRp4lzPggUL1KNHDzVo0EDjxo1TYGCgjh07pq+//lp79uxRxYoVJUk//fSTGjduLLvdriFDhigyMlKrV69W06ZNdenSpQzX9fPPP6tDhw7q06ePevTooS+//FJTpkyR3W7XL7/8ogsXLujpp5/WpUuX9Nprr6lt27Y6cOBAlk8hnzFjho4ePaqBAwcqX758+uKLLzRhwgTt379f8+fPT9e/ZcuWCg8P15gxY5SamqpChQopJSVFbdq00TfffKPu3btr4MCBunz5sj766CM1b95cS5YsUdu2bV2Ws2LFCs2cOVODBg1S3759tXDhQo0aNUq+vr764IMPVLRoUb3wwgs6ceKEXnvtNXXo0EG//vqr88hSbnrggQdUsGBBjRs3TsePH9e0adPUunVrvfjiixo5cqQGDhyoxx57TAsXLtSzzz6rqKgode/ePUvLzsp+UrhwYbVr105ffPGFzpw5o/z587ss47333lNkZKT+9re/ZWmdvXr1kjFGI0aM0NWrVzVv3jx1795dCQkJ6tu3r7Pf3Llz1a9fPxUvXlyjRo1SYGCgPv30Uz355JPatm2b3n33XUlSSkqKWrVqpSNHjmjQoEG65557lJCQoN27d+vrr792nuY6f/589ezZU40aNVL//v3TjWvevHn6448/1KNHDxUrVkynT5/Wv/71L7Vr104LFy7Uww8/nKXtu9GGDRu0fv16tW3bViVLllRiYqK+/PJLjRo1Sr///rvefvvtdPOMGjVKly9fVu/evRUREeEMW+PGjdPEiRPVrFkzjRs3Tn5+flq1apX69eunffv26ZVXXsnV7Um7rrdu3brppnXt2lXr169X//79Va1aNSUlJWn//v1au3Ztlp6XY8eOqXHjxmrXrp3++c9/6rffftNbb72l9u3b65dffnHZl6Kjo/Xhhx+qevXqGjVqlAoUKKD9+/dr8eLFmjhxovLmzZvt5ye7jh07JkkqWLCgs+27776TJNWvXz9d/7S277//Xg0bNszxeiWpffv2unDhgqRr9xbp16+fRowYkeGlIDVr1lTevHm1fv165+9WAG5gANw11q9fbySZF1980RhjTJcuXYzNZjM//vijs0/p0qVNiRIlXOarVq2aKVmypDl79qxL+3fffWfsdrsZP368s61t27YmODjYpKSkGGOM+eabb4wk06tXL5M3b16TkJBgjDFm27ZtRpJ55ZVXsjT2EiVKGEnm1VdfdWlfvHixkWR69+7tbPvggw+MJLN+/fp0y+ndu7e58a0vo7Zx48YZSebAgQPOtp49expJ5vPPP3fpO3nyZCMp3fOWkaSkJHP69GnzzjvvGEnmrbfeMqdPnzanT582586dy3Ceo0ePGpvNZmbMmOFs69ixowkKCjJJSUmW62vUqJHx8vIyP/zwg0v7gAEDjCTTpEkTl3ZJxmazmdjYWJf2atWqGZvNZh588EGTmprqbP/888+NJDN79uybbnva68/f398cPHjQ2e5wOEyHDh3S1SytLt26dXNZpzHGvP7660aSWbx4sUt7UlKSqV69uilZsqSz7cCBA0aS8fPzM/v373e2JyYmmoIFCxqbzWYGDRrkspzp06cbSWbVqlXpljNu3Lh02/TBBx+k296MXkNp2zRgwIAM1xcYGOjSP22M9erVS7f8jGRnP1m9erWRZKZOnerSd9OmTem2MzNp21ijRg2TmJjobD9//rwpXry4CQoKMvHx8c62wMBAU7hwYXP69Gln3+TkZNOqVSsjyWzatMkYk733hxu363pp7zfXu3TpkilbtqypWLFihtty/fOfmYyWa4wxjzzyiLHb7ebEiRPOtrT3o9KlS5uLFy+69P/pp5+MzWYzTz31VLplPfnkk8bLy8vlNZud7clM2mvw5MmTLu3nz583kszAgQNvuowSJUqke+9Ie+39+9//dmlPe3+8fl9atGiRkWQ6depkkpOTXfqnpqY69/fsPj/ZsXTp0gzfA5988kkjyXz55Zfp5tmxY4eRZIYPH57pckuUKGFKly6d6fSPP/7YdO3a1bz77rtm2bJlZtasWaZBgwZGkmnbtq1xOBwZzle6dGlTpkyZrG0cgFuCU7WBu9jkyZPl7e2d6XW1kvTrr7/qv//9r7p166bU1FSdOXPG+VOqVCmVKVNGq1atcvZv0aKFLly4oK1bt0qS1q1bp3z58mn06NFKSkpy3pV03bp1zv5ZFRQUlO6v7R07dlSFChX0+eefKzU1NcvLyq7U1FQtWbJEFStWTHcTomHDhmX5WsWYmBhFRERo0KBBkqQhQ4YoIiJCERERql69eobzfP7555KubWua0NBQXb58WcuWLct0u0+fPq1NmzbpgQceUI0aNVympR35z0i9evVUr149l7bGjRvLGKOhQ4e6nArbpEkTSUp3WrOVHj16uNzJ18vLS2PHjpWkDE9FfOaZZ9KdSj9//nxFRUWpUaNGLq/J+Ph4tWvXTgcOHEg3po4dO6pUqVLOxz4+PqpTp46MMRo+fLhL35xsV3aMHDkyw/W1a9fO5RrJtDFmZxxZ3U9atmypMmXKpDvldc6cObLb7erTp0+2tuf6M0dCQkI0ePBgXbx4UWvWrJF07TTjhIQEDRkyxOUIt7e3t55//nlJ/1//kJAQSdeOjmZ2l/msCAgIcP7/0qVLOnv2rC5fvqzmzZtr586dOb5D/fXLvXr1qs6dO6czZ87ogQcekMPh0A8//JBunieffDLd+8SCBQtkjFGfPn1cXsdnzpxRu3btlJqa6nK0Nze25/Tp05KkfPnyubT7+fnJ19dX3333nX7//fesPRE3KFKkSLozI1q1aiXJdV9KO8vptddeS3eE9frT7bP7/GTVzp071atXL4WFhen99993mXb58mVJGV/HnXaPg7Q+OdG1a1d9/PHH6tevn9q2basBAwZo06ZN6tatm5YvX65PPvkkw/nCw8N16tSpHK8XwJ9HcAbuYqVLl9agQYO0bt06ffXVVxn22bVrl6RrITst4F3/s2fPHv3xxx/O/ml3G037MLN27Vo1a9ZM9957r4oWLerSHhYW5rz2LCkpSSdPnnT5iY+PTzfejD7MVKxYURcuXHB+ILwVTp06pYsXLzpPgb6er6+vSpcunaXlVK1aVWvWrNGYMWMkXTt9c82aNVqzZo0WLFiQ4TyfffaZ6tSp43JTmOeee06lSpVS586dlT9/fj300EOaPn26Sy3SrmnNaMxFixZ1hpMbXR8u06TdOOfGaWntZ8+ezXSbb5TReNLaMroWsly5cunadu3apYMHD2b4mpwwYYIkuTwXGY39+vHnxnZlR2bry2yM2RlHVvcTm82m/v37a/fu3c4/aJ0/f16LFi3SAw88kK3rNrNS07QwVrly5XR909rSXrMlSpTQuHHjtGbNGhUpUkRVq1bV4MGDnSE8qw4ePKiePXsqPDxcgYGByp8/vyIiIjR79mxJ167pzYnLly9r7Nixzuuvw8PDFRERoV69eklShndWzux1LF17X7jxddy6dWtJrq/jW7U9kpQ3b169+eab2rlzp0qXLq3y5curb9++Wrx4cbrrtjOT0es37fKS61/De/fuVVhY2E1vpJXd5ycr9uzZoxYtWig1NVVffvllujGnXT6T0bXH2b3EJqtsNpvGjRsnSZneV8AY4zF3Mgf+qrjGGbjLvfDCC5o3b55Gjx6t+++/P930tKNTQ4YMUbt27TJchp+fn/P/lSpVUqFChbR27VoNHTpU33//vfNmYS1atNDatWud1z23atXKec1bbGysmjVr5rLc3r17Z3jTpZux+nBx4w2obrewsDC1bNnSeRStYsWKLl9tcqMzZ85o06ZN6W4QVbp0ae3YsUMbNmzQunXrtGnTJj399NN64YUX9OWXX6px48Y5HqPdbs/2NHPDjZ1yU0YfUlNTU1W+fHnNmDEj0/kqVark8vhWbFdOX2uZrc9qjLfCY489phdeeEFz5sxRo0aNtGDBAl25ckUDBgy4rePIyPjx4/XYY4/pq6++0qZNm/TZZ5/p7bffVvv27fX555/fNEQkJCSocePGio+P19ChQ1WlShUFBwfLy8tL77//vv7zn//k+CyVRx99VF988YX69u2rxo0bK3/+/PL29taPP/7ovBb/Rpm9jqVrYSmzez2kBbvc2p6IiAhJ14LsjV97169fP7Vr105fffWVNm7cqLVr12ru3LmqXbu2vvnmm5veVd7q9ZuT94jsPD9ZsXPnTrVo0UKJiYlas2aNateuna5PsWLFJF27V8CN0trS+uSmkiVLSlKmR5XPnj2rAgUK5Pp6AWQdwRm4y+XPn19jx47V2LFjMwyp1x8lsQp412vevLk+/fRTffnll0pJSXGejt2iRQvNnz9fy5YtU0JCgsvy0o7EXu/Gr93Yv3+/rl69mu4D1M6dOxUcHOz8QJh2CmJGR31yegpigQIFFBQUlO6O1dK1oxD79+/P9g3CsmLJkiVyOBzq3Llzuml58uRRq1atnKdCbt++XTVr1tQ//vEPbdiwwXkUPKMxHzt2LN0R/dslo/GktZUpUyZLyyhXrpyOHDmipk2buvV7lW/Fa+3Pyup+Il3b/zt37qxPP/1Ub775pubMmaNixYqpTZs22Vrnzp07VbVq1XRt0v/XNO31uGPHjnQ3bvv1119d+qQpUaKEBg4cqIEDByolJUXR0dFasGCBvvnmm5t+lc/XX3+tI0eOaO7cuXr88cddps2ZMydb23e9+Ph4ffHFF+rRo4fzZmZp0u6MnlXlypXTypUrVbhw4Qzv/Hy93NqetD8o/fbbbxl+X3zBggUVHR2t6OhoGWP07LPP6pVXXtHChQsVHR2d5fVYKVeunHbt2qVDhw65XLaRUb+sPj838+uvv6pFixZKSUnRunXrMl1eWpjevHmz+vXr5zJt8+bNLn1yU9qp7BnVJDExUUePHk233wC4vThVG4CGDRumYsWK6R//+IeuXLniMq1atWqqXLmy5s6d6zxt7nrGmHSnSLds2VJJSUmaOHGiihcvrrJlyzrbjTHO62uvv7457Ujs9T83nv558eLFdF919fnnn2vXrl3q0KGD8+h1Wti/8dq3TZs2Zfmrk27k5eWl9u3ba+fOnVqyZInLtNdff10JCQk5Wu7NLF68WFWrVk13VCWj09IrVKiggIAA5ymRERERatiwoVauXKmffvrJpe+kSZNuyXiz4qOPPnL5GrTU1FTnEfVOnTplaRm9evVSXFycXnrppQynZ/f0zZwqWbKk8uTJk+619ttvvzmvTb/dsrqfpBkwYICuXLmioUOHatu2bXr88cezfeT7tddeczm1NT4+XjNnzlRgYKDzDzutWrVSYGCgZsyY4XJKscPhcNYx7Q9E8fHxSk5OdlmHt7e3M5xff9pvYGBghn+4SNuGG490bt++Pd0+nB1pz9+Ny7148aKmTZuWrWX17NlTkjR27Nh02ytdex7Sntfc2p60Pzhc/5VK0rXTz2+8dtdmszkDZm5ettCjRw9J0tNPP53haeBp25id58fK9u3b1axZM6Wmpurrr7+2DOGNGzdWiRIl9Mknn+jIkSPO9gsXLui9995TqVKlMrzjdlZl9DympKQ47/Nw4z00pGtfIZaUlJTurCwAtxdHnAHI19dXkyZNch5NuP4IgM1mc37Fz3333afo6GhVrlxZycnJOnjwoJYsWaLevXu7fLdtWiDeuXOny5GRIkWKqEKFCtq5c6eKFi2q8uXLZ2ucpUuX1ssvv6wdO3aoTp062rVrl9555x1FRES4BMHy5cvr/vvv16xZs+RwOFSjRg3t2rVL8+bNU5UqVXL0vcvStbC5cuVKde3a1fkVOVu2bNHSpUtVunTpLJ0Gvn37dm3fvt35oXXz5s3pjpjWr19fpUqVUnx8vNatW6fnnnsu3XLuv/9+BQUFqXHjxipevLguX76shQsX6vz5884bLUnS9OnT1bhxYzVt2lSDBw92fh3Vzz//nO4riG6XChUqqE6dOho0aJDy5cunJUuW6Ouvv1a3bt2y/MFw6NChWrduncaPH6+NGzeqdevWypcvn44cOaLY2Fj9/vvvt+WIb2BgoB5//HHNnj1bDz/8sJo3b67Dhw9r1qxZqlKlir7//vtbPoYbZXU/SdO4cWNVrFhRH374oby8vFy+Pio76tevr0ceeURJSUn64IMPnM9DcHCwpGs3/Hr99dfVr18/1axZU48//rgCAgL06aefKiYmRv369XN+xc/69evVr18/dezYUeXLl1doaKh27typWbNmqWjRoi5nq9StW1dr167VP//5TxUvXlw2m03dunVTgwYNVLhwYY0cOVK///67oqKitGvXLs2ZM0eVK1fWjz/+mKPtDAoK0gMPPKAFCxY4b9524sQJzZ071+VrjbKiZs2amjRpkp5//nlVqlRJ3bt3V7FixXTq1Cn98ssv+uKLL7Rz505FRUXl2vbUqFFDZcqU0fLly/XMM8842/fu3avGjRurQ4cOuvfeexUREaHff/9ds2bNUlBQUJb/qJUVf//73/Xoo49qwYIFql27tjp16qQCBQrowIEDWrRokbZu3arQ0NBsPT+ZOXz4sJo3b66zZ89qzJgx+uWXX/TLL7+49GnVqpWzdna7XW+//bbatWunRo0a6amnnlLevHk1e/ZsnTx5UitWrEj3x6cZM2bo/Pnzkq6FeS8vL+e+FhoaqieffNLZt3LlymrYsKEqV66swoUL6/jx41q4cKF27dqlbt26udwEMs3y5cvl7e2dqzUAkANuuJM3ADe58euorudwOEzVqlUz/VqlI0eOmMGDB5tSpUqZvHnzmtDQUFO5cmUzdOhQs2PHjnT9y5Ytm+FXkwwZMsT59VTZkfb1J//9739Nq1atTGBgoAkKCjLt27c3v/32W7r+f/zxh+nWrZsJCQkx/v7+pnHjxiY2NvZPfR2VMcbs2bPHtG/f3gQFBZnAwEDTqlUr8/PPP5smTZpk6euo0pZr9ZP21UYfffSRkWR++eWXdMuZM2eOuf/++03hwoVN3rx5TUREhGncuLH5+OOP0/X9/vvvTbNmzYy/v78JCQkxnTp1MgcOHMjwK2WUyVf7WH1VT2bz3Oj6r26aOXOmKV++vMmbN6+JjIw0L7zwQrqv1sqoLtdLSUkxb7/9tqlTp44JDAw0vr6+JioqynTq1Mnlecjoa6Ruto6M5slsOQkJCWbgwIEmf/78xtfX19SsWdMsW7bM8uuosrK+rD4P18vufpLmjTfeMJJMmzZtsrSeNGnbuGPHDjN8+HDn67Fy5cpmwYIFGc7z5ZdfmiZNmpjAwEDj4+NjqlSpYt566y2Xrxz7/fffzcCBA03FihVNcHCw8fPzM2XKlDFDhgwxR44ccVne3r17TatWrUxQUJBzH0rzyy+/mDZt2piwsDDj7+9v6tata7744osMa5Odr6M6e/asGTBggClatKjx8fEx5cuXN1OmTDFr165N9/VkVl+Pl2blypWmTZs2Jjw83OTJk8cUKVLENGvWzLz22mvmypUrOdoeK6+99pqRZPbt2+dsO3PmjBk+fLipXr26CQsLMz4+PqZEiRImOjra7Nq1y2X+zL6O6sY2YzJ/baempprZs2ebWrVqGX9/fxMQEGDuueceM2zYMHP16tUcPT8ZSXvfsfrJqDZff/21adq0qQkICDABAQGmadOmZsOGDRmuI+2ruDL6ufH3wsiRI03NmjVNeHi48fb2NiEhIaZRo0bmgw8+SPe1e8Zc+91crFgx07lzZ8vtBHDr2Yy5hXd0AQDkWKdOnfTrr7/esq9EioqKUlRUlDZs2HBLln+jDRs2qFmzZvrggw9y7VpJ5I5Zs2Zp0KBB+uKLLzK9CWBGxo8frwkTJujAgQM3vUMy7hyXLl1SuXLl1KFDB82cOdPdw4GFhQsXqkePHvrpp59UpUoVdw8HuKtxjTMA3KHq1aunqVOnunsY+ItLTU3VzJkzVaxYMT344IPuHg5ug4CAAE2ePFnvvfeey/0GcGdxOBwaN26c+vTpQ2gG7gBc4wwAd6hRo0a5ewj4Cztw4IA2b96sr776Sr/++qtmzJhx278OC+7Tq1cv5/dO485kt9u1Z88edw8DwP8QnAEAuAt98803euyxxxQeHq6RI0dq0KBB7h4SAAB3LK5xBgAAAADAAtc4AwAAAABggeAMAAAAAIAFjw7OKSkpOnr0qFJSUtw9FAAAAADAX5RHB+eTJ08qMjJSJ0+edPdQkAmHw6EzZ87I4XC4eyjIIWro+aih56OGno8aej5q6PkOHTokm83G17B5MHfuhx4dnAEAAAAAuNUIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABa83T0AAAAAALhdqv1zokxQoLuH4dHO//MNdw/htuOIMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGDBO7cXuGDBAr3xxhvas2ePjDEqWrSoGjRooJdfflkFChSQJDVt2lTffPNNhvNv3rxZdevWze1hAQAAAAA80KpVq7R8+XKdP39ewcHBevLJJ1WhQgXt3btXixYt0v79+yVJZcuWVXR0tAoXLixJ2rBhg1atWqXjx4/L19dXNWvWVM+ePeXr65vtMeRqcJ4yZYrGjBmj4cOHa+LEiTLG6Ndff9WCBQt0/PhxZ3CWpAYNGmjq1KnpllGpUqXcHBIAAAAAwENt2LBBq1ev1qhRo1SkSBH9/vvvCgsLkyRdunRJzZo10/Dhw5U3b14tXLhQU6ZM0fTp0yVJSUlJ6tmzp8qWLasrV67ojTfe0Pz589WvX79sjyNXg/Obb76p6Ohovfbaa862v/3tbxo1apRSU1Nd+oaGhnJkGQAAAAD+ogYPHqwHHnhA3377rU6ePKl77rlHQ4YMUWBgYJbmT01N1ccff6xBgwapePHicjgcypcvnzM4V69e3aX/Qw89pKVLl+rixYsKCgpS69atndPy5MmjFi1a6LPPPsvRtuRqcI6Li3MeFr+RlxeXUwMAAADA3WTTpk0aPXq0AgMDNWnSJK1YsUIPP/ywoqOjM52nYcOG6tu3r86dO6ezZ8/q6NGjmjVrlowxqlatmnr37i273Z5uvp07dyo0NFRBQUEZLnfnzp2KjIzM0XbkanCuUaOGZs2apZIlS6pt27YqVKhQpn2NMUpJSXFps9lsGT4BaS5cuKALFy44H584cUKS5HA45HA4/uTocSs4HA6lpqZSHw9GDT0fNfR81NDzUUPPRw09X1rtvG02GZvNzaPxbFndD4wxeuCBB5xHiGvXrq0dO3bI4XBo7ty5N13H6dOnJUk//fSTJk+erMTERE2ZMkVLlixRly5dXPr/8ccfmjt3rqKjo+VwONLlyq1btyomJkaTJ0/O6ma6yNXg/Pbbb6tjx47Oc8ZLliyphx56SMOHD1dUVJRL3y+//FJ58uRxabPb7enC9PWmTZumCRMmpGuPj49XQEDAn98A5DqHw6GEhAQZYyz/KII7FzX0fNTQ81FDz0cNPR819Hzx8fGSpBKBwfLO5IgksiYuLi5L/VJTU2W32539U1JSlJCQkOX5ExMTJV27uXRycrIkqV69etqyZYtatmzp7Hfu3DlNnz5drVq10j333KO4uDjlz5/fOX379u2aNWuWRo8ebXlw10quBudKlSppx44dWrt2rVavXq1vvvlGb775pj744ANt3LhR1apVc/Zt2LCh86LtNLab/OVnxIgR6tu3r/PxiRMnVLt2bYWEhDj/ioE7i8PhkM1mU2hoKL9kPBQ19HzU0PNRQ89HDT0fNfR8aWeuHkq4IGNLvUlvWMlq9vLy8lJgYKCzv7+/v7y9vRUWFpalU7X9/f2VJ08eBQUFKSwsTA6HQ35+frLb7c5lnj17VjNmzFCrVq3Uvn37dMv69ddf9cYbb2j48OGqUKFC9jf2f3L966jy5s2rNm3aqE2bNpKu3Tr8wQcf1MSJE7V48WJnv5CQENWsWTNbyw4ODlZwcHC6drvdzhvYHczLy4saeThq6Pmooeejhp6PGno+aujZ0uqWYoyMMW4ejWfL6j6QdiluWn8vLy9n2/z58286v7+/vxo0aKAVK1aobNmySkxM1IYNG1S7dm3Z7XadO3dOkyZNUqNGjdSpU6d08+/YsUOvvfaahg4d+qe/vSnXg/ON7r//flWtWlW7du261asCAAAAAPyFPPbYY5ozZ44GDRokHx8f1ahRQ+3atZMkrVu3TidPntTSpUu1dOlS5zzTp09X/vz59emnn+rKlSsu3/oUERGhadOmZXscuRqc//jjDxUsWNCl7cqVKzpy5Ijuvffe3FwVAAAAAOAONnPmTJfHrVu3dvmKqKzw8/PTU089JenaJRNxcXHy9r4WY7t06ZLuJmHXGzduXDZHnLlcDc6VK1fWQw89pPvvv1+FCxfWsWPHNGPGDJ05c0ZDhw516Xv+/Hlt2bIl3TLKlCnjciE3AAAAAADulKvBefz48Vq2bJlGjBih06dPK3/+/KpSpYrWrVunZs2aufSNiYlRvXr10i1j/vz56tGjR24OCwAAAACAHMvV4PzEE0/oiSeeuGm/DRs25OZqAQAAAAC4ZbzcPQAAAAAAAO5kBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAgre7BwAAAAAAt8t/n/mHSpQo4e5hwMNwxBkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACw4O3uAQAAAADZsToy8rav03h7K7VkSXkdOCBbSsptXz/+vFPuHgA8GkecAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAu3NDi///77stls+u2331za33rrLdlsNo0bN86l/dy5c/Ly8tKUKVNu5bAAAAAAAMgy71u58AYNGkiSYmNjVbZsWWd7TEyM/P39FRsb69I/NjZWxhg1bNjwVg4LAAAAORQfH6958+Zp586dSkxMVOHChdW9e3dVrVrV2ScpKUkLFixQTEyMkpOTVahQIY0fP15+fn4yxujjjz/Whg0bdOXKFRUtWlS9e/dW+fLl3bhVAGDtlgbn8uXLKyIiQjExMerdu7ezPSYmRtHR0frwww/lcDhkt9ud7b6+vqpZs+atHBYAAAByKDExUSVLllTPnj0VGhqqrVu3aurUqZo2bZoiIiIkSXPmzNHVq1c1depUBQcH6/Dhw/L2vvaxMyYmRl9//bUmTJigggULavXq1ZoyZYreffdd52dCALjT3PJrnBs0aKCYmBjn48OHD+vo0aMaOnSoEhMTtX37due0mJgY1axZU3nz5r3VwwIAALgrDR48WMuWLdMzzzyj3r17a/LkyUpISMjy/AULFlS7du2UL18+eXl5qU6dOsqfP7/2798vSTp+/Li+++47DRgwQKGhofLy8lJUVJTy5MkjSTp16pTuueceFS5cWF5eXmratKkuXryouLi4W7K9AJAbbukRZ+lacP7iiy8UFxensLAwxcTEKDIyUuXKlVPVqlUVExOj6tWrKzk5WVu3btWwYcMyXdaFCxd04cIF5+MTJ05IkhwOhxwOx63eFOSAw+FQamoq9fFg1NDzUUPPRw09351UQ2OMNm7cqKefflqBgYF6+eWXtXz5cnXp0kV9+vTJdL769etnOP3cuXM6efKkihQpIofDob179yoiIkKLFi3Spk2bFBQUpLZt26pZs2aSpLp162rz5s06fPiwChUqpDVr1igyMlIhISFZfn6M9y3/CJt+nXa78weeyRgjiezgybLzXprbZ7Dc8nedhg0byhijzZs3q02bNoqNjVX9+vUlXXsDjo2N1ZNPPqmffvpJiYmJltc3T5s2TRMmTEjXHh8fr4CAgFu2Dcg5h8OhhIQEGWM4/cpDUUPPRw09HzX0fHdSDVNTU9W4cWPZ7XZduXJFVapU0Z49exQXF6epU6daznvjUeHk5GS9+eabql+/vvz9/RUXF6ejR4/qyJEjqlixoiZNmqRjx47prbfekr+/v8qVKyebzaaSJUtq1KhRstls8vf31+DBgxUfH5/1bShZMkfb/mcYu12mUCGlSrIRujySSU6WDh1SfHw8Zzh4qOy8l+bPnz9X133Lg3ONGjXk5+enmJgYtWnTxnl9syTVq1dPY8aMkXTtNG2bzeYM1RkZMWKE+vbt63x84sQJ1a5dWyEhIQoLC7ul24GccTgcstlsCg0NdfsHBeQMNfR81NDzUUPPdyfV0MvLS0WKFHF+dko70pvdz1IpKSl67bXXFB4ergEDBji3KyQkRF5eXurRo4e8vb1VoEAB1a1bV/v27VOdOnW0cOFCHT58WG+88YbCw8P1008/6Z133tHkyZOVL1++rG3DgQPZ2+hcYOx2pf5v3QRnz2T73xFnsoPncud76S0Pznny5FGtWrUUExOjhIQEbd++3eWIc9o1zzExMapYsaLlizg4OFjBwcHp2u12u9t/CSFzXl5e1MjDUUPPRw09HzX0fHdKDW02m8s4vLy8nG09e/bMdL5GjRqpf//+kq6F5jfeeEN2u13Dhg1z3vhLkkr+72jw9euw2WzOdRw5ckT169dXoUKFJEl16tTRxx9/rP379ztvLnbTbUhJyf6G5wKbw3Htx03rx59j+9+/d8J+iJxz13vpbblApGHDhnr99df17bffysfHR9WqVZMklShRQoULF1ZMTIxiY2PVvn372zEcAAAAZGD+/Pk37ZOSkqJp06YpOTlZo0ePdgnNklShQgUVLFhQixcvVufOnXX48GF99913GjVqlCSpTJky2rJlixo2bKiwsDD9/PPP+uOPPxQZGXlLtgkAcsNtC84vv/yyZsyYoVq1arm8wdavX1/z58/XyZMnnd/7DAAAgDvT3r179cMPPyhv3rx6/PHHne39+/dXo0aNZLfbNXr0aM2ePVvLli1TWFiYevXqpYoVK0qS2rdvrwsXLmjs2LFKTExU/vz5NWjQIBUtWtRdmwQAN2UzabeXu4XOnz+v8PBwGWM0ZswYvfzyy85pr732mkaNGiVjjH7//Xfn6T1ZcfToUUVGRurIkSMqVqzYrRg6/iSHw+G8ozqnxHgmauj5qKHno4aejxrmrtVuODptvL2VWrLktWucOVXbI52S1PPoUR08eFAlSpRw93CQA+58L73l3+MsSaGhobr33ntljEl386/69evLGKMiRYpkKzQDAAAAAHA73LYvwdu+fXuG7fXq1dNtOOgNAAAAAECO3JYjzgAAAAAAeCqCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAVvdw8AAAAAyI7WR47c9nU6HA7FxcUpLCxMdrv9tq8ff96hQ4ekqCh3DwMeiiPOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAVvdw8AAAAAd7fVkZHuHsJNGW9vpZYsKa8DB2RLSXH3cJADp9w9AHg0jjgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgIU/FZwXLFig2rVrKyQkRMHBwapQoYL69u2rU6dOSZJ69eqlYsWKpZuvc+fOstlsWr9+vUv7559/LpvNpu+///7PDAsAAAAAgFzjndMZp0yZojFjxmj48OGaOHGijDH69ddftWDBAh0/flwFChRQgwYNNH/+fB06dEglSpRwzhsTEyN/f3/FxsaqWbNm6drvu+++P7dVAAAAyLJXXnlF+/fvV1JSkkJDQ9WuXTu1aNHCOb1r167y8fFxPq5QoYKeffZZ5+OEhATNmzdPP/74o1JTU1W6dGn94x//uK3bAAC3Uo6D85tvvqno6Gi99tprzra//e1vGjVqlFJTUyVJDRs2lHQtEKcF5/379+uPP/7QoEGDFBMT47LMmJgY1alTR97eOR4WAAAAsumRRx5RkSJF5O3traNHj2rChAkqUaKEypQp4+zzyiuvqGjRohnOP3XqVBUvXlwzZsyQr6+vDh48eJtGDgC3R45P1Y6Li1PhwoUzXqjXtcVWrFhRYWFhLgE5JiZGpUuXVocOHbR582YZYyRJiYmJ+umnn9SgQYOcDgkAAOCuM3jwYC1btkzPPPOMevfurcmTJyshISFbyyhevHi6AxcnT57M0rzbt2/XqVOnFB0drYCAANntdpUuXTpb6weAO12OD+3WqFFDs2bNUsmSJdW2bVsVKlQoXR+bzab69eunC87169dX3bp1deHCBe3YsUOVKlXS1q1blZSU5DxKnZELFy7owoULzscnTpyQJDkcDjkcjpxuCm4hh8Oh1NRU6uPBqKHno4aejxp6vltZQ2OMNm7cqKefflqBgYF6+eWXtXz5cnXp0kV9+vTJdL769eu7TJ8xY4a+//57JScnq0SJEqpatarLeCdMmKDU1FSVKlVKjzzyiCIjIyVJe/bsUZEiRTRz5kz9/PPPCg8PV5cuXVSjRo2sb4MHnG1o7HbnDzxT2gE7soPnys57qT2X99Ucv0u9/fbb6tixo/r16ydJKlmypB566CENHz5cUVFRzn4NGzbU888/r4sXLyooKEixsbEaPHiwgoODde+99yo2NlaVKlVSbGysvLy8VK9evUzXOW3aNE2YMCFde3x8vAICAnK6KbiFHA6HEhISZIzJ9Rcvbg9q6Pmooeejhp7vVtYwNTVVjRs3lt1u15UrV1SlShXt2bNHcXFxmjp1quW8cXFxzv8/+uij6t69u3777Tft27dPly9f1tWrVyVJw4YNU6lSpZSSkqLVq1frpZde0j/+8Q/5+fnp+PHj2r59u7p166auXbtq9+7deuONN/Tcc8+pQIECWduGkiVz/gTcJsZulylUSKmSbIQuj2SSk6VDhxQfH+/y2ofnyM57af78+XN13TkOzpUqVdKOHTu0du1arV69Wt98843efPNNffDBB9q4caOqVasm6Vpwdjgc2rJli2rVqqUdO3aofv36kqR69eopJiZG/fv3V0xMjCpXrqzg4OBM1zlixAj17dvX+fjEiRPOu3qHhYXldFNwCzkcDtlsNoWGhvJhz0NRQ89HDT0fNfR8t7KGXl5eKlKkiPOzUEhIiBwOR44/G4WHh2vbtm364Ycf9MADD0iS6tSp45zeu3dv/fDDDzp16pSqVaumoKAg5cuXT+3bt5d07bPfN998o0OHDql8+fJZ24YDB3I01tvJ2O1K1bWxEpw9k+1/R5zJDp7Lnb8P/9R5MXnz5lWbNm3Upk0bSdKqVav04IMPauLEiVq8eLEkqWbNmsqbN69iYmKUkpKioKAgVapUSdK1U4RefPFFGWMUGxurbt26Wa4vODg4w2Btt9v5IHEH8/LyokYejhp6Pmro+aih57tVNbTZbC7L9fLycrb17Nkz0/kaNWqk/v37ZzgtNTVVp06dynSsNpvNuT1RUVHaunWrS1+bzeYcQ5a2ISUlS/3czeZwXPvxkPHCle1///Je6tnc9fswVy8ouf/++1W1alXt2rXL2ebr66uaNWs6g3OdOnWcNw+rV6+e9u/fr40bN+rs2bOW1zcDAAAge+bPn3/TPidPntTRo0dVuXJleXt767///a82b96s4cOHS5KOHDnivO45JSVFX3zxhZKSklSuXDlJUu3atfXRRx9p3bp1atasmX799Vft3bvXeTkfAPwV5Dg4//HHHypYsKBL25UrV3TkyBHde++9Lu0NGzbUO++8o6SkJJfvbS5Xrpzy58+vV199VZK4ozYAAIAbLFmyRG+99ZYkqUCBAurTp49q1qwp6dq9ZN577z2dPXtWefPmValSpfTcc8857y8TGBioZ555RnPnztW8efNUoEABDR8+PMMbxwKAp7KZtNvLZVOBAgX00EMP6f7771fhwoV17NgxzZgxQ7GxsVq8eLE6dOjg7Lt06VK1b99eNptNK1euVOvWrZ3THnroIa1YsUKRkZE6dOhQtsZw9OhRRUZG6siRIypWrFhONgO3mMPhUFxcnMLCwjglxkNRQ89HDT0fNfR81NDa6v/doftOZry9lVqy5LVrnDlV2yOdktTz6FEdPHhQJUqUcPdwkAPufC/N8fc4jx8/XsePH9eIESPUsmVLjRw5UkFBQVq3bp1LaJauHUlOu9albt26LtPq168vYwynaQMAAAAA7kg5PlX7iSee0BNPPJGlvuHh4UpNTc1w2tixYzV27NicDgMAAAAAgFsqx0ecAQAAAAC4GxCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALHi7ewAAAAC4u7U+csTdQ7gph8OhuLg4hYWFyW63u3s4yIFDhw5JUVHuHgY8FEecAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAsEZwAAAAAALBCcAQAAAACwQHAGAAAAAMACwRkAAAAAAAve7h4AAAAA7gyrIyPdPYQ7lvH2VmrJkvI6cEC2lBR3Dwc5cMrdA4BH44gzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYOG2B+fx48fLZrOpcePG6aYNGzZMUVFRt3tIAAAAd5Tjx4/r0Ucf1euvv+5s27t3r1566SU9/vjjevzxxzV58mSdOHEiw/lnzpyprl276tixY7dpxADw1+a2I86bNm3Shg0b3LV6AACAO9acOXNUpkwZl7ZLly6pWbNmmjFjht59911FRkZqypQp6ebdsWOHTp06dbuGCgB3BbcE54CAANWuXVsvvviiO1YPAACQ6wYPHqxly5bpmWeeUe/evTV58mQlJCRkezkbNmxQSEiI7r33Xpf26tWrq379+vL395e3t7ceeughHTt2TBcvXnT2SUlJ0fvvv6++ffv+6e0BAPw/b3et+IUXXtBDDz2k2NhY1a9f313DAAAAyDWbNm3S6NGjFRgYqEmTJmnFihV6+OGH1adPHxljZLPZ0s3TsGFDZ9C9ePGiPvvsM02cOFFr1qyxXNfOnTsVGhqqoKAgZ9uSJUtUrVo1RUZG5u6GAcBdzm3BuW3btqpevbomTJigVatWZWmeCxcu6MKFC87Hadf1OBwOORyOWzJO/DkOh0OpqanUx4NRQ89HDT0fNfQMxhg98MADCgsLkyTVrl1bO3bskMPh0Lvvvqvz588rNDRUdrs93bxptf3www91//33Kzg4WMYYGWMyrPsff/yhuXPnKjo62jn9xIkT2rhxoyZPnuxsy+5nJOPtto+Gdzxjtzt/4JmMMZLIDp4sO78PM3qv/TPc+u74/PPPq3Pnzvr+++9Vu3btm/afNm2aJkyYkK49Pj5eAQEBt2KI+JMcDocSEhJkjMn1Fy9uD2ro+aih56OGniE1NVV2u11xcXGSrp02nZCQoLi4uCzVcN++fdq/f7+6dOmiuLg4XblyRUlJSc7lpTl37pymT5+uVq1a6Z577nFOnz17ttq2basrV67oypUrkq4ddPD398/6NpQsmZNNvysYu12mUCGlSrIRujySSU6WDh1SfHx8uv0KniE7vw/z58+fq+t2a3Du2LGjKlWqpIkTJ2r58uU37T9ixAiXa3ZOnDih2rVrKyQkxPnXXdxZHA6HbDZbpn9hx52PGno+auj5qKFn8PLyUmBgoPMzSdq1yGFhYYqOjr7pqdqHDh3SmTNn9Nxzz0mSEhMTlZqaqhdffFHTpk2TJJ09e1YzZsxQq1at1L59e5fl7NmzRydPntSiRYucbdOnT9fDDz+sFi1aZG0bDhzI0bbfDYzdrlRde44Izp7J9r8jzmQHz+XO34duDc42m03PPfecunfvrp9++umm/YODgxUcHJyu3W6380HiDubl5UWNPBw19HzU0PNRwzufzWZzqZGXl5ezbd68eYqLi1NYWFimNWzfvr3uv/9+5+OlS5fq5MmTGjBggOx2u86dO6dJkyapUaNG6tSpU7r53333XZfH/fv316hRo1SyZMksv25sKSlZ3dy7ks3huPbD8+SR0v5sxXupZ3PX70O3X8jStWtXjR8/Xi+++KJKlCjh7uEAAAC4ha+vr3x9fV0e582bVyEhIZKkdevW6eTJk1q6dKmWLl3q7Dd9+nTlz59foaGh6ZYZFBQkHx+fWz52APirc3tw9vLy0nPPPafevXuradOm7h4OAABAjsycOdPlcevWrdW6descL69r164uj7t06aIuXbpkef5PPvkkx+sGALhyy/c43+iRRx5RqVKltH79encPBQAAAAAAF3dEcLbb7Ro7dqy7hwEAAAAAQDq3PTiPHz9eCQkJ6dr79OkjY4wOHjx4u4cEAAAAAECm7ogjzgAAAAAA3KkIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFgjMAAAAAABYIzgAAAAAAWCA4AwAAAABggeAMAAAAAIAFb3cPAAAAAHeG1keOuHsIdyyHw6G4uDiFhYXJbre7ezjIgUOHDklRUe4eBjwUR5wBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAseLt7AAAA4K9pdWSku4dwxzDe3kotWVJeBw7IlpLi7uEgB6ih5zvl7gHAo3HEGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsuCU4jx8/XjabLcOfV155xR1DAgAAAAAgQ97uWrGfn5++/vrrdO3Fixd3w2gAAPjriI+P17x587Rz504lJiaqcOHC6t69u6pWrersk5SUpAULFigmJkbJyckqVKiQxo8fLz8/P+3YsUMTJ05U3rx5nf07duyoTp06uWNzAABwO7cFZy8vL9WtW9ddqwcA4C8rMTFRJUuWVM+ePRUaGqqtW7dq6tSpmjZtmiIiIiRJc+bM0dWrVzV16lQFBwfr8OHD8vb+/48FwcHBmjNnjrs2AQCAO4rbgjMAAMjY4MGD9cADD+jbb7/VyZMnVb58efXo0UNhYWFZmr9gwYJq166d83GdOnW0cOFC7d+/XxERETp+/Li+++47vfPOOwoICJAkRUVF3YpNAQDgL8GtwTklJSVd2/V/7b7RhQsXdOHCBefjEydOSJIcDoccDkfuDxB/msPhUGpqKvXxYNTQ81FDz2OM0caNG/X0008rMDBQL7/8stauXasePXqoT58+mc5Xv379DKefO3dOJ0+eVJEiReRwOLR3715FRERo0aJF2rRpk4KCgtS2bVs1a9ZMkpSamqqEhAT169dPefLkUdWqVdWtWzcFBgZmbzssfqffbYzd7vyBZ6KGns8YI4ns4Mmy85nGnsv7qtt+o126dEl58uRJ175p0yY1bNgww3mmTZumCRMmpGuPj493/sUcdxaHw6GEhAQZY3L9xYvbgxp6PmroeVJTU9W4cWPZ7XZduXJFlSpV0s6dOxUXF6epU6dazhsXF+fyODk5WW+++abq168vf39/xcXF6ejRozpy5IgqVqyoSZMm6dixY3rrrbfk7++vcuXKyd/fX2PHjlWhQoV0/vx5/ec//9Ebb7yhJ554InvbUbJktrf9r8rY7TKFCilVko0P7B6JGno+k5wsHTqk+Pj4dO+V8AzZ+UyTP3/+XF23W28OtnHjxnTt99xzT6bzjBgxQn379nU+PnHihGrXrq2QkJAsn76G28vhcMhmsyk0NJQP7B6KGno+auh5vLy8VKRIEefvttDQUDkcDoWFhWWrhikpKXrttdcUHh6uAQMGOOcNCQmRl5eXevToIW9vbxUoUEB169bVvn37VKdOHZffqeHh4erfv7+GDRsmf39/+fj4ZH07DhzIct+/OmO3K1XXnhNCl2eihp7P9r8jzmQHz+XOzzRuvTlYzZo1szVPcHCwgoOD07Xb7XY+DN7BvLy8qJGHo4aejxp6FpvN5lKvtK9stNvtio6OznS+Ro0aqX///pKuheY33nhDdrtdw4YNc7kUquT/jgRnto4bpc2b9jrK8nZkcEnW3czmcFz74XnxWNTQs9n+9y+/Dz2buz7TcPERAAAeZP78+Tftk5KSomnTpik5OVmjR49Od/+QChUqqGDBglq8eLE6d+6sw4cP67vvvtOoUaMkSb/++qsKFCigiIgIxcfH64MPPlCVKlXk6+t7S7YJAIA7HcEZAIC/mL179+qHH35Q3rx59fjjjzvb+/fvr0aNGslut2v06NGaPXu2li1bprCwMPXq1UsVK1aUJB08eFAzZ87UxYsXFRAQoGrVqunRRx911+YAAOB2bgvOqamp2rJlS7r2AgUKqFSpUm4YEQAAd4aZM2e6PG7VqlW2Lm+qWLGiPvnkE8s+RYsW1cSJEzOc1rZtW7Vt2zbL6wMA4K/ObcH5ypUrqlevXrr2Pn366L333nPDiAAAAAAASM/LHSsdP368jDEZ/hCaAQAAAAB3ErcEZwAAAAAAPAXBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwIK3uwcAAAD+mlofOeLuIdwxHA6H4uLiFBYWJrvd7u7hIAeooec7dOiQFBXl7mHAQ3HEGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALDg7e4B3A1WR0a6ewhuY7y9lVqypLwOHJAtJcXdw0EOUEPPRw09HzX0fNTQ81FDz3fK3QOAR+OIMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGCB4AwAAAAAgAWCMwAAAAAAFgjOAAAAAABYIDgDAAAAAGAh28F5/Pjxstlsaty4cbppw4YNU1RUlBITE+Xj46PnnnvOZXp8fLzsdrtKlCiRbt727durYsWK2R1Othw/flyPPvqoXn/9dWfbpk2b1LNnT5efrl276rvvvpMkbdiwQWPHjlXv3r01YMAAzZkzR4mJibd0nAAAAACAO4d3TmfctGmTNmzYoKZNm6ab5uvrq/vuu0+xsbEu7Zs3b5avr68OHz6sY8eOqWjRos5psbGx6tixY06HkyVz5sxRmTJlXNoaNWqkRo0aOR///PPPev3111WtWjVJUlJSknr27KmyZcvqypUreuONNzR//nz169fvlo4VAAAAAHBnyNGp2gEBAapdu7ZefPHFTPs0bNhQ33//vVJSUpxtMTExatKkiaKiohQTE+Ns37Nnj86cOaOGDRumW87gwYO1bNkyPfPMM+rdu7cmT56shISEbI95w4YNCgkJ0b333mvZb/369apfv758fHwkSa1bt1bFihWVJ08eBQcHq0WLFtq9e3e21w8AAAAA8Ew5vsb5hRde0Ndff53uqHKahg0b6vLly/r555+dbTExMapfv77q1avnEpzT/t+gQYMMl7Vp0yaNGjVKs2fP1qVLl7RixQpJ0rPPPqv27dvr2WefVXR0tMvPe++955z/4sWL+uyzz9S7d2/Lbbp48aJ++OEHNWvWLNM+O3fuVGRkpOVyAAAAAAB/HTk+Vbtt27aqXr26JkyYoFWrVqWbnhaCY2JiVKtWLaWkpOj777/X888/r9DQUM2bN8/ZNyYmRoULF1bp0qUzXFebNm2UP39+XbhwQffcc4927dqlo0ePasiQIapdu7YOHjyoYsWKpZvP4XBIkj788EPdf//9Cg4OljFGxhjntOtt3LhRBQsWVOnSpTOc/sMPPygmJkaTJk3KcHpmjHeOn2aPZ+x25w88EzX0fNTQ81FDz0cNPR819HzGGEnXMkJ2PsvjzuFwOJSampql+tlzeV/9U4nu+eefV+fOnfX999+rdu3aLtPy58+v8uXLKzY2VsOGDdO2bduUmJioOnXqKDQ0VMOHD9fly5fl7++v2NjYTI82S1JoaKgkadq0aZo/f74iIyM1YcIE5/T4+HgFBARkOO++ffu0f/9+denSRXFxcbpy5YqSkpIUFxeXru+6detUu3btDKft3r1bc+fO1YABA+Tj45Nhn8ykliyZ5b5/NcZulylUSKmSbLxBeSRq6Pmooeejhp6PGno+auj5THKydOiQ4uPjs/VZHncOh8OhhIQEGWNuGozz58+fq+v+U8G5Y8eOqlSpkiZOnKjly5enm96wYUN99dVXkq4dVa5SpYoCAgJUpUoV+fj46LvvvlOVKlW0Z88eDRgw4KbrGzFihCpWrKiff/5ZH330kZ555hlduXJFU6ZMkc1mS7fuvn376tChQzpz5ozzDt+JiYlKTU3Viy++qGnTpjn7HzhwQCdOnFDr1q2dQT3Nr7/+qvfff1/Dhw+/6TXSGfE6cCDb8/xVGLtdqbr2HPBLxjNRQ89HDT0fNfR81NDzUUPPZ/vfEeeQkBCFhYW5eTTICYfDIZvNptDQ0Fw/onwzfyo422w2Pffcc+revbt++umndNMbNGiguXPn6uDBg87rmyXJ29tbtWrVUkxMjPMvBhndGOxGwcHBCg0NlY+Pj4oVK6Z//vOfioyM1JEjRzI8VVu69jVX999/v/Px0qVLdfLkSQ0YMMDlyd64caOqVaum8PBwl/l37Nih119/XUOHDlWVKlWy9LzcyHbdDdLuRjaH49rPXf48eDJq6Pmooeejhp6PGno+aujZ0g6z2e322x66kHu8vLzcUsM/ffFt165dNX78eL344ovpvp85LQzHxMQoNjZW//znP53T0m4QlpCQoICAAOfXP+U2X19f+fr6ujzOmzevQkJCnG0pKSmKiYnJ8Kj3p59+qitXrui1115ztkVERLgcrQYAAAAA/HX96eDs5eWl5557Tr179073nc5ly5ZVgQIF9J///EdHjx51HnGWpPr16+udd97RxYsXVadOHXlncgOtmTNnujxu3bq1WrdunePxdu3aNV2bt7e35s6dm2H/cePG5XhdAAAAAADPl+Ovo7reI488olKlSmn9+vXppjVo0EBffvmlChcurKioKGd7vXr1dP78ecXGxmbpNG0AAAAAANwhV4Kz3W7X2LFjM5zWsGFDGWNcjjZLUnh4uMqVK5fl65sBAAAAAHCHbAfn8ePHKyEhIV17nz59ZIzRwYMHXdpHjBghY4w+/fTTdPPs2bNHxhi1atUqu8MAAAAAAOC2yJUjzgAAAAAA/FURnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAALBGcAAAAAACwQnAEAAAAAsEBwBgAAAADAAsEZAAAAAAAL3u4ewN2g9ZEj7h6C2zgcDsXFxSksLEx2u93dw0EOUEPPRw09HzX0fNTQ81FDz3fo0CEpKsrdw4CH4ogzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABW93D+DPSElJkSSdOHHCzSNBZhwOh+Lj43Xp0iXZ7XZ3Dwc5QA09HzX0fNTQ81FDz0cNPV9aZjhx4gQ19FDZ3Q8LFSokb+/cibw2Y4zJlSW5wdatW1W7dm13DwMAAAAAcIc5cuSIihUrlivL8ujgnJiYqF9++UURERG59pcE5K4TJ06odu3a+v7771W4cGF3Dwc5QA09HzX0fNTQ81FDz0cNPR819HzZrWFuHnH26LTp6+urWrVquXsYyILChQvn2l974B7U0PNRQ89HDT0fNfR81NDzUUPP544acnMwAAAAAAAsEJwBAAAAALBAcMYtFRwcrHHjxik4ONjdQ0EOUUPPRw09HzX0fNTQ81FDz0cNPZ87a+jRNwcDAAAAAOBW44gzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM7IsVdffVXVq1dXaGioAgICVLlyZc2YMUM33qjdGKNXXnlFxYsXl5+fn+rVq6ctW7a49Pnjjz/0t7/9TcHBwWrYsKH27dvnMv3cuXMqUKCAfvzxx1u+XXcLh8OhKVOmqHHjxsqfP7/y5cunZs2aadOmTen62my2dD+FChVy6bN37141aNBAwcHBevDBB3Xq1CmX6b/99pvy5cuno0eP3tLtuptkp4ZJSUkaNWqUChUqpICAALVq1Up79uxx6UMN3WPNmjV65JFHVLp0adlsNj355JMZ9mM/vHNltYbsh54lOjo6w/1u5cqVzj4pKSkaMmSI8uXLp7Jly+qrr75Kt5zmzZtr+vTpt3Pod6Xdu3erVatWCggIUKFChTR69GglJSW59Jk7d64iIyNVqFAhvfzyy+mWMWHCBLVv3/52DRnXmTdvXob725gxY5x93F4/A+TQs88+ayZPnmyWLVtm1qxZY8aMGWNsNpt56aWXXPpNnjzZ5M2b10ybNs2sXbvWdOzY0QQFBZn9+/c7+3Tv3t20bNnSrFq1yjRv3tw0aNDAZRlPPPGE6du3723ZrrvFxYsXTWhoqBk2bJhZvny5+eqrr0zHjh2N3W4369atc+kryQwZMsRs3rzZ+fPjjz+69KlXr5555JFHzMqVK03lypXNo48+6jK9TZs2ZtKkSbd8u+4m2anhgAEDTEhIiJk7d65ZuXKladSokSlatKg5f/68sw81dI8RI0aYihUrmscee8yEhoaawYMHZ9iP/fDOldUash96lt69e5tSpUq57HObN292qdfs2bNN8eLFzbJly8zQoUNNcHCwOXv2rHP6J598YipUqGCSk5PdsQl3jXPnzpnChQubxo0bm5UrV5q5c+eakJAQl31x586dxtfX17z33ntmzpw5xtvb26xZs8Y5/dChQyYsLMzl8ylunw8++MBIMitXrnTZ3w4fPmyMuTPqR3BGrnrkkUdM2bJlnY+vXLligoODzdixY51tV69eNSVKlDCDBg1ytuXPn998//33xhhjtmzZYiSZhIQEY4wx27ZtM+Hh4ebUqVO3aSvuDikpKebcuXPp2u655x7Ttm1bl3ZJ5tVXX810WRcvXjSSnDVauHChiYiIcE5fvny5KVWqlElMTMzFLUBWa3jkyBFjt9vN7NmznW1nz541AQEB5p///Kcxhhq6k8PhcP6/RIkSlsGZ/fDOlJUash96nt69e5t7773Xss/f//53M2XKFGOMMcnJySYwMNCsWLHCGGPM5cuXTfHixc3q1atv+Vjvdi+//LIJCAhw+aPF7Nmzjd1uN8eOHTPGGDNjxgzTpk0b5/QHHnjAjBo1yvn473//u3n22Wdv36DhIi04nz59OsPpd0L9OFUbuSo8PNzltJjY2FhduHBBXbt2dbblzZtXnTp10pdffulsu3r1qvz8/CRJ/v7+kuRczlNPPaUXXnhBERERt2MT7hp2u11hYWHp2qpUqaLjx49na1lptbq+hlevXnVOGz58uKZNmyYfH59cGDnSZLWGq1evVmpqqrp06eJsy5cvn1q3bu3cD6mh+3h55c6vYmroPlmpIfvhX9P1n1+8vb2VN29eZ81eeeUV3XfffWrVqpU7h3hX+Oqrr9SyZUvly5fP2da1a1elpqZq9erVklxrJbnuX+vXr9eWLVv07LPP3t6BI8vuhPoRnPGnpaSk6OLFi1qxYoU+/PBDDR061Dlt9+7dkqR77rnHZZ4KFSro8OHDunLliiSpVq1aevvttxUXF6eZM2eqdOnSCgsL08cff6wzZ85o8ODBt2+D7mIpKSnasmWLKlSokG7a5MmTlSdPHoWGhurhhx/W4cOHndPy5cunUqVK6a233tK5c+f07rvvqlatWpKk6dOnq1SpUlwzdJtkVMPdu3erQIEC6UJ2hQoVnPsoNfQM7Ieei/3QM+3bt08hISHKmzevatSooSVLlrhMr1WrlubPn68//vhDH374oeLj41W9enUdOnRIb731lqZNm+aegd9ldu/ene6zZmhoqAoXLuzcv2rVqqW1a9dq+/bt2rZtm9auXatatWrJ4XDoqaee0pQpUxQQEOCO4eM69957r+x2u0qVKqXJkyfL4XBIukPqd0uPZ+Mv77fffjOSnD/PP/+8y/RJkyYZHx+fdPMtWrTISHKePvPTTz+ZQoUKGUkmJCTErF271ly6dMlERkaatWvX3pZtgTEvvfSSsdvt5ocffnBp79Wrl/nkk0/MN998Y2bOnGkKFSpkIiMjXU4TXrVqlQkODjaSTJEiRcy2bdvM8ePHTb58+cyuXbtu96bctTKqYd++fU358uXT9X311VdNnjx5nI+poftZnarNfugZMqsh+6Hnef31182MGTPM+vXrzeeff25at25tJJlFixY5+5w/f97UqVPHSDI2m8152n2nTp3SfSbCrePt7W0mT56crv3ee+81/fr1cz4eMGCA8zNr+/btTUpKinnzzTdNo0aNbudwkYGVK1eaCRMmmJUrV5pVq1aZwYMHGy8vL5f3U3fXj+AMp/Pnz5tdu3bd9Ofq1avOeRITE83WrVvN+vXrzcSJE42/v7/5xz/+4Zye1eBsjDFJSUlm9+7d5sqVK8YYY55//nnTqVMnY8y167oqVqxowsPDTe/evZ3XP8NVTmqYZvXq1cbb29tMmDDhpuvZtm2bsdvtzg8IaS5fvmx2795tkpKSjDHG9OjRw4wYMcIYY8zcuXNNqVKlTIECBcyIESNMSkpKLmzxX8+tqGFWP7AbQw1zw5+poVVwvhH74a1zK2rIfuh+f6auxly7lr1OnTqmQoUKLu2pqalm//79zutr165dayIjI82lS5fMnj17TJMmTUxoaKhp2rSp2bdv3y3fzrtRVoOzMcacOHHCecOp06dPm/z585uff/7ZXLhwwfTs2dOEh4ebihUrmpUrV96WsSNzTz/9tLHb7eb48ePONnfWj+AMpzlz5rgcPc7sx+ov3q+//rqx2+3mxIkTxhhjZs6caSQ5w3Cad99919hsNnP58uUMl/P777+bsLAwc+DAAfPHH3+YgIAA8/HHH5u4uDhTu3ZtM2bMmNzb8L+QnNbwxx9/NEFBQaZXr15ZXlfFihVN165dM50eExNjChUqZOLj48327dtN3rx5zbfffmuOHTtmoqKizKxZs3K8nX9lt6KGo0aNMoUKFUrX/uyzz5qCBQtmOhZqmDN/5r00O8HZGPbDW+VW1JD90P1y43POlClTjKRMP78kJyebe++91yxcuNAYY0ytWrXMkCFDzKVLl8zgwYNN3bp1b8m23e0iIiIy/GxYpEgR88wzz2Q6X//+/c3AgQONMdf20bp165pz586Z//znPyYoKCjTG1Xh9vj++++NJPPll19mOP12149rnOHUt29fmWt/TLH8ufEakuvVqFFDDodDBw8elPT/1zbf+D2Vu3fvdn6vc0ZGjBihIUOGKCoqSlu2bJGvr6+6du2q0NBQ9ezZU2vWrMmdjf6LyUkN9+3bp7/97W+qX7++3nvvvVwZR2pqqp566im9/PLLCg4O1vr161W5cmU1aNBARYoUUefOnalhJm5FDe+55x798ccfiouLc2nP6JqwNNQw53LjvTQ3UMOcuxU1ZD90v9uxb86cOVP58+fXww8/rAsXLmjr1q3q37+//P39NXDgQG3ZskUJCQm5uFWQru1fadcyp4mPj9eJEycyrefPP/+sxYsXa9KkSZKktWvX6tFHH1VYWJi6deumvHnzasuWLbd87MgZd9SP4Ixc9e2338pms6lkyZKSpPr16ys4OFiLFi1y9klOTtbixYvVpk2bDJexdu1a/fTTTy5feJ6UlOS8OcClS5dkjLmFW3H3OHHihFq3bq3ixYvr008/VZ48ebI033//+1/t2bPHedOaG82dO1d2u13R0dHOtsuXLzv/Tw1zT1Zq2Lp1a3l5eemzzz5ztsXFxWn16tWZ7ofU8M7HfuhZ2A89X2pqqhYtWqR77703wz/8nz59Wi+++KLefPNNl/a0ml26dMm5HOSuv/3tb1q7dq3Onz/vbFu0aJG8vLzUunXrDOcZMmSIxo8fr/DwcGdbWq0cDoeuXr3K/uVmCxculN1uV/Xq1dNNc0v9cuW4Ne4658+fN/Xr1zdvv/22Wb16tVmxYoUZNWqUyZs3r/OUiTSTJ082Pj4+5vXXXzfr1q0znTt3NkFBQRl+QXlycrKpUKGC+eSTT5xtJ0+eNP7+/mbEiBFm+fLlJjIy0uV725Azly9fNlWrVjVBQUFm6dKlLl82/9NPPzn7vfrqq2bgwIFm4cKF5uuvvzZvvvmmKViwoImKijJxcXHplhsXF2cKFChgvvvuO2fbtm3bjM1mM1OmTDGffPKJCQkJMTNnzrwdm/mXltUaGnPthhqhoaHm/fffN6tWrTJNmjQxRYsWNefPn0+3XGp4ex08eNAsWrTILFq0yERERJgHHnjA+TgN++GdLSs1NIb90JMcPHjQNGnSxMyaNcusXbvWLFq0yDRv3tzYbDazePHiDOfp27eveeKJJ1zaatSoYVq3bm1Wr15tWrZsaWrVqnU7hn/XOXfunClcuLBp0qSJWbVqlXn//fdNaGhoppe+fPTRR6ZKlSou9wgYOXKkKVGihFm2bJkZPny4CQgIMH/88cft2oS7XuvWrc0rr7xiVqxYYVasWGEGDBhgbDabGTZsWLq+7qofwRk5kpiYaKKjo02ZMmWMn5+fyZcvn6ldu7Z5//33092oJDU11bz88sumWLFixsfHx9SpU8fExsZmuNxp06aZZs2apWv/4osvTNmyZU1wcLDp3r27uXDhwi3ZrrvJgQMHMr2+q0SJEs5+S5cuNXXr1jVhYWHG29vbFC5c2Dz++OMuN2q43lNPPWWio6PTtc+ePdtERkaafPnymSeffNIkJyffqk27a2S1hsZc22dHjhxpChQoYPz8/EzLli0zvY6PGt5eH3zwQaZ1TMN+eGfLSg2NYT/0JGfPnjXt2rUzxYoVM3nz5jWBgYGmadOmmd5w6IcffjARERHOG4Sl2blzp2nQoIEJCAgw9evXN7t3774dw78r7dy507Ro0cL4+fmZAgUKmKeffjrDG70lJCSYokWLmg0bNri0x8fHm+7du5vg4GBTpkwZs3Tp0ts1dJhr73lly5Y1fn5+xsfHx1SuXNm88cYbJjU11aWfO+tnM4ZzEAAAAAAAyAzXOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAMAAAAAYIHgDAAAAACABYIzAAAAAAAWCM4AAAAAAFggOAPAXWz8+PGy2WzOn4iICDVv3lybNm1y99C0YcMG2Ww2/fDDD24dx969e9WgQQMFBwfrwQcf1KlTp1ym//bbb8qXL5+OHj3qphHmLpvNpqlTp2a5/7x58/Tvf/87XXvTpk3Vtm3b3ByapTvl9QIA+GsiOAPAXc7Pz0+bN2/W5s2b9c477+js2bNq0aKFfv31V7eO67777tPmzZtVoUIFt44jOjpaUVFRWrRokY4cOaIRI0a4TB82bJhGjhypYsWKuWmEuWvz5s169NFHs9w/s+D89ttv67XXXsvNoQEA4Dbe7h4AAMC9vLy8VLduXefj2rVrKyoqSrNmzdKMGTPS9TfGKCkpST4+Prd0XMHBwS7jcoeEhARt3rxZX3zxhSIiInT+/HkNGTLEOX3FihXavXu3Fi9e7MZR5q7ces4rVqyYK8sBAOBOwBFnAICL4sWLKyIiQgcOHJB07YhrpUqV9OWXX6pq1ary8fHRsmXLJF07Otm8eXMFBAQoJCREjzzyiMupzAcPHpTNZtP8+fM1cOBAhYaGqkCBApo2bZokaeHChSpfvryCg4PVqVMnnT9/3jnvjafepi3r008/dRnvsGHDFBUV5Xw8b94853ytW7eWv7+/ypcvr7Vr1yo1NVXPP/+8ChYsqIIFC2rs2LFKTU3N9LlISkqSdO2ovCT5+/vr6tWrzmnDhw/XtGnTsvxHhJIlS+rJJ59M1/7000+rWLFizrG88sorKlOmjHx9fRUREaGWLVs665GZ8+fP64knnlDhwoXl4+OjGjVqaPXq1c7pS5Yskc1m0/Lly51t586dU9GiRdW9e3dn242nasfExKhx48YKCQlRUFCQKleurH/961+Srp2O/c0332jFihXO0/3Hjx/vnHb9qdrjx49XYGCgfvnlFzVs2FD+/v6qVKmSVq1a5bIdSUlJeuqpp5QvXz6FhoZqwIAB+ve//y2bzaaDBw/e5BmW4uLi9MgjjygoKEglSpTQlClT0vVZvHixqlWrJl9fXxUpUkQjRoxQYmKic3raa+jMmTMu81WrVk3R0dHOxzt27FCbNm0UHh7ufJ3duL6b7SNSzuoNALi9CM4AABcXLlzQ2bNnVaRIEWfb8ePH9dRTT2n48OFauXKlqlWrps2bN6tp06YKCQnRxx9/rHfffVdbt25V+/bt0y3zueeek5+fnxYtWqQuXbpo5MiRGjt2rN544w1NmTJFM2fO1Ndff63Ro0fn2nb06tVLbdu21eeff64iRYqoU6dOGjp0qI4cOaIPP/xQgwcP1iuvvKKFCxdmuox8+fKpVKlSeuutt3Tu3Dm9++67qlWrliRp+vTpKlWqVIbbm5lu3brp008/lcPhcLYZY/Txxx/r4YcflpeXlz788EO98MIL6tOnj1auXKn33ntP1apV04ULFzJdblJSklq1aqXly5frpZde0tKlS1WxYkU9+OCD+uWXXyRJHTp0UK9evdS3b19nIHziiSckXTutOiMXLlzQgw8+qODgYP3nP//RkiVL1L9/f+cfON5++21Vr15dDRo0cJ7u37dv30zHmZycrEcffVTR0dH6/PPPVaBAAXXu3Flnz5519hkzZoxmz56tZ555Rh9//LFSU1M1ZsyYrD3BkgYOHKhy5crp888/10MPPaRnnnlGK1eudE5funSp/v73v6tixYpasmSJRo8erVmzZqlHjx5ZXkeahx56SHFxcZo7d65WrFihp59+WpcuXXJOz8o+kpN6AwDcwAAA7lrjxo0zAQEBJjk52SQnJ5sDBw6YTp06GUlm5cqVxhhjevfubSSZLVu2uMzbuHFjU79+fZOamups27Fjh7HZbGbFihXGGGMOHDhgJJmuXbs6+6SkpJiCBQuagIAAc+bMGWf7yJEjTWhoqPPx+vXrjSSzdetWl2UtWrTIZRxDhw41JUqUcD7+4IMPjCTz9ttvO9t++eUXI8nUrVvXZd4aNWqYDh06WD5Hq1atMsHBwUaSKVKkiNm2bZs5fvy4yZcvn9m1a5flvDfatm2bkWRWr17tbPvmm29ctnPw4MHmvvvuy9Zy33//fePt7W127Njh0l6nTh3TpUsX5+Pz58+byMhI07FjR/Pvf//bpc5pJJlXX33VGGPM1q1bjSSzffv2TNfdpEkT8+CDD960fdy4cUaS87VhzP/XdP78+cYYY86ePWt8fX3NxIkTXZbVokULI8kcOHAg03GkvV5GjRrlbEtNTTVRUVGmT58+zrbq1aubevXqucw7e/Zsl+1Mew2dPn3apV/VqlVN7969jTHGnD592kgyS5cuzXRMWdlHclJvAMDtxxFnALjLXbp0SXny5FGePHlUsmRJrV+/XjNmzND999/v7BMeHq46deo4H1++fFkxMTHq0qWLHA6HUlJSlJKSonLlyikyMlJbt251WUerVq2c/7fb7SpVqpSqVaum8PBwZ3u5cuV0/vx5JSQk5Mp2Xb/OcuXKSZJatGjh0qdcuXI6cuSI5XJat26tkydPavfu3Tp48KCqVKmi0aNHKzo6Wvfcc4/ef/99lS5dWgULFtTIkSNdjibfqEqVKqpYsaLLUe6FCxeqbNmyqlmzpqRrN0X7+eefNWLECH377bdKTk6+6bauXr1alStXVrly5Zy1SElJUatWrVxqERISonnz5mnJkiV67LHHNGjQIJc636h06dIKDg7WoEGD9Mknn+j06dM3HYsVLy8vtWzZ0vk4KipKfn5+zjuS//LLL0pMTFS7du1c5svOUf3WrVs7/2+z2VShQgXn8hMSEvTf//5Xf//7313mefjhhyVJ3377bZbXEx4erhIlSmjs2LH617/+le6u6lndR3JSbwDA7UdwBoC7nJ+fn7Zu3aoffvhBBw8e1JkzZzR48GCXPgULFnR5HBcXJ4fDoeHDhztDd9rP4cOH04XR0NBQl8d58+bNsE2Sy7Wmf8b1y09bdkbrzMr6/Pz8VL58eeXJk0exsbFau3atxo0bp19++UWDBg3Shx9+qJ9//lmLFy/We++9Z7ms7t27a/HixUpKSlJKSoo+/fRTl2uMo6OjNX36dK1atUqNGjVSRESEhg4dqitXrmS6zDNnzujnn39OV4tJkyalq0XDhg1VvHhxXb16NcPrra8XFhamNWvWKCgoSD179lShQoXUtGlT5+nf2eXn5+esRZrra3DixAlJUkREhEufAgUKZHkdVjU+f/68jDHpXs8hISHy8fHRuXPnsrwem82m1atXq0KFCho8eLAiIyNVs2ZNbdy4UVLW95Gc1BsAcPtxV20AuMt5eXk5j3ZmxmazuTwODQ2VzWbTs88+qw4dOqTrnz9//twcoiTJ19dX0v/fsCtNXFxcrq8rM6mpqXrqqaf08ssvKzg4WOvXr1flypXVoEEDSVLnzp21Zs0aDRgwINNldOvWTS+88IJWrlwpHx8fnT592iU4e3l5aejQoRo6dKiOHTumhQsXasyYMcqfP79eeOGFDJeZL18+ValSRXPnzr3pNvzjH//Q2bNnVbZsWQ0ePFhff/11uvper3bt2vrqq6905coVrV+/Xk8//bQ6dOig/fv333Rd2VW4cGFJ0unTp12usb/xZlo5lfa6vXF58fHxunr1qvLlyycp66+1cuXKadGiRUpOTlZsbKyeffZZPfTQQzp27FiW95Gc1BsAcPsRnAEA2RYQEKB69epp165dmjRp0m1ZZ4ECBZQnTx7t2rXL2ZaUlKRvvvnmtqxfkubOnSu73e5yZ+XLly87/3/p0iUZYyyXUaZMGdWqVUv/+c9/5OPjo2rVqumee+7JsG/RokU1cuRI/fvf/3bZ7hu1bNlSX375pYoUKeISOG8UGxurV199Ve+8847uu+8+1atXT2+88YaGDRtmOWbp2tHiNm3aaP/+/Ro6dKgSExPl6+ub5aP2WVGpUiX5+vrqiy++UNWqVZ3tS5YsyZXlBwYGqlq1avr00081fPhwZ/snn3wi6drReEnO7+TetWuX8/nctWtXpqf158mTR02aNNGYMWPUrl07HT9+XOXKlcv2PpLVegMAbj+CMwAgR1599VU1b95cDz/8sLp166awsDAdPXpUa9as0WOPPaamTZvm6vq8vLzUqVMnzZgxQ2XKlFH+/Pk1Y8YMGWMsj5jmlvPnz+v555/XsmXLnOtr2rSphg0bpldffVVRUVH6z3/+o5dffvmmy+revbteeOEFeXt767nnnnOZNmDAAIWFhalu3boKCwtTTEyMtm3b5rwDdkZ69eql2bNnq2nTpnr66aed14v//PPPSkpK0uTJk3Xp0iX16tVL999/v/r37y/p2t3Ox44dqwceeCDD8L5ixQrNnTtXHTt2VPHixXXy5Em99dZbatCggfOobIUKFfSvf/1Ly5YtU+HChW8a3q2Eh4dr0KBBeumll+Tr66tq1app0aJF2rt3r6Rrr4E/a/z48erQoYN69OihHj16aM+ePXr22WfVuXNnVa5cWZJUp04dRUZGavjw4Zo8ebIuXLigV155xeWa/O3bt2vkyJF6+OGHVbp0acXHx2vy5MmKiopS6dKlJWVtH8lJvQEAtx/BGQCQI/Xr19e3336rcePG6bHHHlNSUpKKFSumFi1aqEyZMrdknW+99Zb69++vp556SkFBQRo1apTKly+fa0ckrYwbN05t2rRR7dq1nW1VqlTRrFmzNGnSJF26dEk9e/Z0hlIrDz/8sJ5++mkZY9StWzeXafXr19ecOXM0Z84cXb58WaVKldL06dPVp0+fTJfn4+Ojr7/+WuPHj9dLL72kEydOKH/+/KpevbozgI0cOdL51Ulpnn/+ea1YsUI9e/bU5s2b5e3t+rGgTJky8vLy0nPPPadTp04pPDxcrVu31uTJk519Ro8erX379qlXr146f/68xo0b5/wu55x45ZVXlJycrMmTJys1NVUdO3bUmDFj9OSTTyokJCTHy03Trl07LVq0SBMnTlT79u2VL18+9e/f32Wb8uTJo88//1yDBg1Sly5dVKZMGU2fPl0jR4509ilUqJAKFSqkyZMn69ixYwoJCVGjRo300UcfyW63S8raPpKTegMAbj+budk5ZQAAAG7Us2dPffvttzpw4IC7hwIAuEtxxBkAANwxvvnmG8XExKhGjRpKTU3V8uXLtWDBAk2bNs3dQwMA3MU44gwAAO4YP/74o4YMGaIdO3boypUrKlmypAYNGpSlG5gBAHCrEJwBAAAAALDw529PCQAAAADAXxjBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwALBGQAAAAAACwRnAAAAAAAsEJwBAAAAALBAcAYAAAAAwML/AXwzVE9/lMVAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 990x495 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (6, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>area</th><th>existing_psqm</th><th>new_psqm</th><th>n_existing</th><th>n_new</th><th>premium_pct</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>u32</td><td>u32</td><td>f64</td></tr></thead><tbody><tr><td>&quot;NW&quot;</td><td>7857.142857</td><td>5277.777778</td><td>8961</td><td>472</td><td>-32.828283</td></tr><tr><td>&quot;E&quot;</td><td>5896.226415</td><td>5128.205128</td><td>27998</td><td>265</td><td>-13.025641</td></tr><tr><td>&quot;N&quot;</td><td>6744.186047</td><td>6192.66055</td><td>21863</td><td>424</td><td>-8.177792</td></tr><tr><td>&quot;SW&quot;</td><td>8371.584699</td><td>7797.758792</td><td>33437</td><td>356</td><td>-6.854448</td></tr><tr><td>&quot;W&quot;</td><td>8775.510204</td><td>8220.327296</td><td>12809</td><td>268</td><td>-6.326503</td></tr><tr><td>&quot;SE&quot;</td><td>5937.5</td><td>6153.846154</td><td>33468</td><td>622</td><td>3.643725</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (6, 6)\n",
       "┌──────┬───────────────┬─────────────┬────────────┬───────┬─────────────┐\n",
       "│ area ┆ existing_psqm ┆ new_psqm    ┆ n_existing ┆ n_new ┆ premium_pct │\n",
       "│ ---  ┆ ---           ┆ ---         ┆ ---        ┆ ---   ┆ ---         │\n",
       "│ str  ┆ f64           ┆ f64         ┆ u32        ┆ u32   ┆ f64         │\n",
       "╞══════╪═══════════════╪═════════════╪════════════╪═══════╪═════════════╡\n",
       "│ NW   ┆ 7857.142857   ┆ 5277.777778 ┆ 8961       ┆ 472   ┆ -32.828283  │\n",
       "│ E    ┆ 5896.226415   ┆ 5128.205128 ┆ 27998      ┆ 265   ┆ -13.025641  │\n",
       "│ N    ┆ 6744.186047   ┆ 6192.66055  ┆ 21863      ┆ 424   ┆ -8.177792   │\n",
       "│ SW   ┆ 8371.584699   ┆ 7797.758792 ┆ 33437      ┆ 356   ┆ -6.854448   │\n",
       "│ W    ┆ 8775.510204   ┆ 8220.327296 ┆ 12809      ┆ 268   ┆ -6.326503   │\n",
       "│ SE   ┆ 5937.5        ┆ 6153.846154 ┆ 33468      ┆ 622   ┆ 3.643725    │\n",
       "└──────┴───────────────┴─────────────┴────────────┴───────┴─────────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pooled recent window so each area has enough new-build sales; premium per postal area.\n",
    "RECENT_FROM = 2015\n",
    "def premium_by(group_col: str, min_n: int = MIN_CELL_N) -> pl.DataFrame:\n",
    "    g = (\n",
    "        sales.filter(pl.col(\"year\") >= RECENT_FROM)\n",
    "        .group_by(group_col, \"sale_is_new\")\n",
    "        .agg(pl.col(\"psqm\").median().alias(\"med_psqm\"), pl.len().alias(\"n\"))\n",
    "    )\n",
    "    out = (\n",
    "        g.pivot(values=[\"med_psqm\", \"n\"], index=group_col, on=\"sale_is_new\")\n",
    "        .rename({\"med_psqm_true\": \"new_psqm\", \"med_psqm_false\": \"existing_psqm\",\n",
    "                 \"n_true\": \"n_new\", \"n_false\": \"n_existing\"})\n",
    "        .with_columns((100 * (pl.col(\"new_psqm\") / pl.col(\"existing_psqm\") - 1)).alias(\"premium_pct\"))\n",
    "        .filter((pl.col(\"n_new\") >= min_n) & pl.col(\"existing_psqm\").is_not_null())\n",
    "        .sort(\"premium_pct\")\n",
    "    )\n",
    "    return out\n",
    "\n",
    "area_prem = premium_by(\"area\")\n",
    "print(f\"New-build premium by London postal area (sales since {RECENT_FROM}, min {MIN_CELL_N} new sales):\")\n",
    "labels = area_prem[\"area\"].to_list()\n",
    "vals = area_prem[\"premium_pct\"].to_numpy()\n",
    "colors = [\"#0f766e\" if v >= 0 else \"#b91c1c\" for v in vals]\n",
    "fig, ax = plt.subplots(figsize=(9, 4.5))\n",
    "ax.barh(labels, vals, color=colors)\n",
    "ax.axvline(0, color=\"black\", lw=0.8)\n",
    "ax.xaxis.set_major_formatter(mticker.PercentFormatter(decimals=0))\n",
    "ax.set_title(f\"New-build £/sqm premium by postal area (since {RECENT_FROM})\")\n",
    "ax.set_xlabel(\"Premium % vs existing houses\")\n",
    "for y, v, nnew in zip(range(len(labels)), vals, area_prem[\"n_new\"].to_list()):\n",
    "    ax.text(v + (0.4 if v >= 0 else -0.4), y, f\"n={nnew}\", va=\"center\",\n",
    "            ha=\"left\" if v >= 0 else \"right\", fontsize=8, color=\"#555\")\n",
    "plt.tight_layout(); plt.show()\n",
    "area_prem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e16ba0c",
   "metadata": {},
   "source": [
    "## 2. Appreciation / depreciation (repeat sales)\n",
    "\n",
    "For every property that sold at least twice, we annualise the growth from its first to its last\n",
    "recorded sale (CAGR). Homes first sold as new-builds start from the developer price; if that price\n",
    "embedded a premium, their subsequent CAGR should lag homes that were only ever resold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f519fdfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-28T11:01:56.560744Z",
     "iopub.status.busy": "2026-06-28T11:01:56.560668Z",
     "iopub.status.idle": "2026-06-28T11:01:56.691971Z",
     "shell.execute_reply": "2026-06-28T11:01:56.691584Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repeat-sales appreciation, new-build vs existing:\n",
      "shape: (2, 5)\n",
      "┌──────────────────┬────────┬─────────────────┬───────────────┬───────────────────┐\n",
      "│ is_newbuild_prop ┆ n      ┆ median_cagr_pct ┆ mean_cagr_pct ┆ median_years_held │\n",
      "│ ---              ┆ ---    ┆ ---             ┆ ---           ┆ ---               │\n",
      "│ bool             ┆ u32    ┆ f64             ┆ f64           ┆ f64               │\n",
      "╞══════════════════╪════════╪═════════════════╪═══════════════╪═══════════════════╡\n",
      "│ false            ┆ 107682 ┆ 8.06            ┆ 9.25          ┆ 15.0              │\n",
      "│ true             ┆ 6495   ┆ 6.5             ┆ 7.33          ┆ 13.0              │\n",
      "└──────────────────┴────────┴─────────────────┴───────────────┴───────────────────┘\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 990x495 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repeat = (\n",
    "    props.with_columns(pl.col(\"historical_prices\").list.len().alias(\"n_sales\"))\n",
    "    .filter(pl.col(\"n_sales\") >= 2)\n",
    "    .with_columns(\n",
    "        pl.col(\"historical_prices\").list.first().struct.field(\"year\").alias(\"y0\"),\n",
    "        pl.col(\"historical_prices\").list.first().struct.field(\"price\").alias(\"p0\"),\n",
    "        pl.col(\"historical_prices\").list.last().struct.field(\"year\").alias(\"y1\"),\n",
    "        pl.col(\"historical_prices\").list.last().struct.field(\"price\").alias(\"p1\"),\n",
    "    )\n",
    "    .filter((pl.col(\"y1\") - pl.col(\"y0\")) >= MIN_HOLD_YEARS)\n",
    "    .filter(pl.col(\"p0\").is_between(MIN_PRICE, MAX_PRICE)\n",
    "            & pl.col(\"p1\").is_between(MIN_PRICE, MAX_PRICE))\n",
    "    .with_columns(\n",
    "        (pl.col(\"y1\") - pl.col(\"y0\")).alias(\"years_held\"),\n",
    "        ((pl.col(\"p1\") / pl.col(\"p0\")) ** (1 / (pl.col(\"y1\") - pl.col(\"y0\"))) - 1).alias(\"cagr\"),\n",
    "    )\n",
    ")\n",
    "\n",
    "summary = (\n",
    "    repeat.group_by(\"is_newbuild_prop\")\n",
    "    .agg(\n",
    "        pl.len().alias(\"n\"),\n",
    "        (pl.col(\"cagr\").median() * 100).round(2).alias(\"median_cagr_pct\"),\n",
    "        (pl.col(\"cagr\").mean() * 100).round(2).alias(\"mean_cagr_pct\"),\n",
    "        pl.col(\"years_held\").median().alias(\"median_years_held\"),\n",
    "    )\n",
    "    .sort(\"is_newbuild_prop\")\n",
    ")\n",
    "print(\"Repeat-sales appreciation, new-build vs existing:\")\n",
    "print(summary)\n",
    "\n",
    "new_cagr = repeat.filter(\"is_newbuild_prop\")[\"cagr\"].to_numpy() * 100\n",
    "old_cagr = repeat.filter(~pl.col(\"is_newbuild_prop\"))[\"cagr\"].to_numpy() * 100\n",
    "fig, ax = plt.subplots(figsize=(9, 4.5))\n",
    "bins = np.linspace(-5, 20, 60)\n",
    "ax.hist(old_cagr, bins=bins, density=True, alpha=0.55, color=\"#0f766e\", label=f\"Existing (n={old_cagr.size:,})\")\n",
    "ax.hist(new_cagr, bins=bins, density=True, alpha=0.55, color=\"#d97706\", label=f\"New-build (n={new_cagr.size:,})\")\n",
    "ax.axvline(np.median(old_cagr), color=\"#0f766e\", ls=\"--\", lw=1.2)\n",
    "ax.axvline(np.median(new_cagr), color=\"#d97706\", ls=\"--\", lw=1.2)\n",
    "ax.set_title(\"Distribution of annualised resale growth (CAGR)\")\n",
    "ax.set_xlabel(\"CAGR % per year\"); ax.set_ylabel(\"density\")\n",
    "ax.xaxis.set_major_formatter(mticker.PercentFormatter(decimals=0))\n",
    "ax.legend()\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5facccf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-28T11:01:56.692733Z",
     "iopub.status.busy": "2026-06-28T11:01:56.692657Z",
     "iopub.status.idle": "2026-06-28T11:01:56.754166Z",
     "shell.execute_reply": "2026-06-28T11:01:56.753779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x495 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (6, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>area</th><th>newbuild_cagr</th><th>existing_cagr</th><th>n_new</th><th>n_existing</th><th>cagr_gap</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>u32</td><td>u32</td><td>f64</td></tr></thead><tbody><tr><td>&quot;NW&quot;</td><td>5.477474</td><td>7.885519</td><td>483</td><td>6514</td><td>-2.408045</td></tr><tr><td>&quot;SW&quot;</td><td>6.033482</td><td>7.833382</td><td>1204</td><td>26027</td><td>-1.7999</td></tr><tr><td>&quot;N&quot;</td><td>6.239218</td><td>7.85302</td><td>957</td><td>16216</td><td>-1.613801</td></tr><tr><td>&quot;SE&quot;</td><td>6.596078</td><td>8.089142</td><td>2233</td><td>25118</td><td>-1.493064</td></tr><tr><td>&quot;W&quot;</td><td>6.127934</td><td>7.566405</td><td>354</td><td>10215</td><td>-1.438471</td></tr><tr><td>&quot;E&quot;</td><td>7.59516</td><td>8.783298</td><td>1236</td><td>23453</td><td>-1.188138</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (6, 6)\n",
       "┌──────┬───────────────┬───────────────┬───────┬────────────┬───────────┐\n",
       "│ area ┆ newbuild_cagr ┆ existing_cagr ┆ n_new ┆ n_existing ┆ cagr_gap  │\n",
       "│ ---  ┆ ---           ┆ ---           ┆ ---   ┆ ---        ┆ ---       │\n",
       "│ str  ┆ f64           ┆ f64           ┆ u32   ┆ u32        ┆ f64       │\n",
       "╞══════╪═══════════════╪═══════════════╪═══════╪════════════╪═══════════╡\n",
       "│ NW   ┆ 5.477474      ┆ 7.885519      ┆ 483   ┆ 6514       ┆ -2.408045 │\n",
       "│ SW   ┆ 6.033482      ┆ 7.833382      ┆ 1204  ┆ 26027      ┆ -1.7999   │\n",
       "│ N    ┆ 6.239218      ┆ 7.85302       ┆ 957   ┆ 16216      ┆ -1.613801 │\n",
       "│ SE   ┆ 6.596078      ┆ 8.089142      ┆ 2233  ┆ 25118      ┆ -1.493064 │\n",
       "│ W    ┆ 6.127934      ┆ 7.566405      ┆ 354   ┆ 10215      ┆ -1.438471 │\n",
       "│ E    ┆ 7.59516       ┆ 8.783298      ┆ 1236  ┆ 23453      ┆ -1.188138 │\n",
       "└──────┴───────────────┴───────────────┴───────┴────────────┴───────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CAGR by postal area, new-build vs existing, with the gap (new minus existing).\n",
    "def cagr_by(group_col: str, min_n: int = MIN_CELL_N) -> pl.DataFrame:\n",
    "    g = (\n",
    "        repeat.group_by(group_col, \"is_newbuild_prop\")\n",
    "        .agg((pl.col(\"cagr\").median() * 100).alias(\"med_cagr\"), pl.len().alias(\"n\"))\n",
    "    )\n",
    "    return (\n",
    "        g.pivot(values=[\"med_cagr\", \"n\"], index=group_col, on=\"is_newbuild_prop\")\n",
    "        .rename({\"med_cagr_true\": \"newbuild_cagr\", \"med_cagr_false\": \"existing_cagr\",\n",
    "                 \"n_true\": \"n_new\", \"n_false\": \"n_existing\"})\n",
    "        .with_columns((pl.col(\"newbuild_cagr\") - pl.col(\"existing_cagr\")).alias(\"cagr_gap\"))\n",
    "        .filter((pl.col(\"n_new\") >= min_n) & pl.col(\"existing_cagr\").is_not_null())\n",
    "        .sort(\"cagr_gap\")\n",
    "    )\n",
    "\n",
    "area_cagr = cagr_by(\"area\")\n",
    "labels = area_cagr[\"area\"].to_list()\n",
    "x = np.arange(len(labels)); w = 0.4\n",
    "fig, ax = plt.subplots(figsize=(10, 4.5))\n",
    "ax.bar(x - w/2, area_cagr[\"existing_cagr\"].to_numpy(), w, label=\"Existing\", color=\"#0f766e\")\n",
    "ax.bar(x + w/2, area_cagr[\"newbuild_cagr\"].to_numpy(), w, label=\"New-build\", color=\"#d97706\")\n",
    "ax.set_xticks(x); ax.set_xticklabels(labels)\n",
    "ax.set_title(\"Median resale CAGR by postal area\")\n",
    "ax.set_ylabel(\"CAGR % per year\")\n",
    "ax.yaxis.set_major_formatter(mticker.PercentFormatter(decimals=0))\n",
    "ax.legend()\n",
    "plt.tight_layout(); plt.show()\n",
    "area_cagr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f363821",
   "metadata": {},
   "source": [
    "## 3. Putting it together\n",
    "\n",
    "Each point is a London postal area: horizontal = the new-build £/sqm premium paid at purchase,\n",
    "vertical = how much faster (or slower) new-builds appreciated than existing houses there. The\n",
    "interesting quadrant is **bottom-right**: paying a premium *and* getting slower growth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b67caa0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-28T11:01:56.755197Z",
     "iopub.status.busy": "2026-06-28T11:01:56.755079Z",
     "iopub.status.idle": "2026-06-28T11:01:56.850983Z",
     "shell.execute_reply": "2026-06-28T11:01:56.850719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 880x660 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "London terraced/detached, repeat sales:\n",
      "  existing  median CAGR: 8.06%/yr (n=107,682)\n",
      "  new-build median CAGR: 6.5%/yr (n=6,495)\n",
      "  appreciation gap: -1.56 pp/yr\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (6, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>area</th><th>premium_pct</th><th>cagr_gap</th><th>newbuild_cagr</th><th>existing_cagr</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;NW&quot;</td><td>-32.828283</td><td>-2.408045</td><td>5.477474</td><td>7.885519</td></tr><tr><td>&quot;SW&quot;</td><td>-6.854448</td><td>-1.7999</td><td>6.033482</td><td>7.833382</td></tr><tr><td>&quot;N&quot;</td><td>-8.177792</td><td>-1.613801</td><td>6.239218</td><td>7.85302</td></tr><tr><td>&quot;SE&quot;</td><td>3.643725</td><td>-1.493064</td><td>6.596078</td><td>8.089142</td></tr><tr><td>&quot;W&quot;</td><td>-6.326503</td><td>-1.438471</td><td>6.127934</td><td>7.566405</td></tr><tr><td>&quot;E&quot;</td><td>-13.025641</td><td>-1.188138</td><td>7.59516</td><td>8.783298</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (6, 5)\n",
       "┌──────┬─────────────┬───────────┬───────────────┬───────────────┐\n",
       "│ area ┆ premium_pct ┆ cagr_gap  ┆ newbuild_cagr ┆ existing_cagr │\n",
       "│ ---  ┆ ---         ┆ ---       ┆ ---           ┆ ---           │\n",
       "│ str  ┆ f64         ┆ f64       ┆ f64           ┆ f64           │\n",
       "╞══════╪═════════════╪═══════════╪═══════════════╪═══════════════╡\n",
       "│ NW   ┆ -32.828283  ┆ -2.408045 ┆ 5.477474      ┆ 7.885519      │\n",
       "│ SW   ┆ -6.854448   ┆ -1.7999   ┆ 6.033482      ┆ 7.833382      │\n",
       "│ N    ┆ -8.177792   ┆ -1.613801 ┆ 6.239218      ┆ 7.85302       │\n",
       "│ SE   ┆ 3.643725    ┆ -1.493064 ┆ 6.596078      ┆ 8.089142      │\n",
       "│ W    ┆ -6.326503   ┆ -1.438471 ┆ 6.127934      ┆ 7.566405      │\n",
       "│ E    ┆ -13.025641  ┆ -1.188138 ┆ 7.59516       ┆ 8.783298      │\n",
       "└──────┴─────────────┴───────────┴───────────────┴───────────────┘"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = area_prem.select(\"area\", \"premium_pct\").join(\n",
    "    area_cagr.select(\"area\", \"cagr_gap\", \"newbuild_cagr\", \"existing_cagr\"), on=\"area\", how=\"inner\"\n",
    ")\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "xs = combined[\"premium_pct\"].to_numpy(); ys = combined[\"cagr_gap\"].to_numpy()\n",
    "ax.scatter(xs, ys, s=70, color=\"#d97706\", zorder=3)\n",
    "for a, xv, yv in zip(combined[\"area\"].to_list(), xs, ys):\n",
    "    ax.annotate(a, (xv, yv), textcoords=\"offset points\", xytext=(6, 4), fontsize=10)\n",
    "ax.axhline(0, color=\"black\", lw=0.8); ax.axvline(0, color=\"black\", lw=0.8)\n",
    "ax.set_xlabel(\"New-build £/sqm premium at purchase\")\n",
    "ax.set_ylabel(\"New-build CAGR minus existing CAGR (pp/yr)\")\n",
    "ax.xaxis.set_major_formatter(mticker.PercentFormatter(decimals=0))\n",
    "ax.set_title(\"Premium paid vs appreciation gap, by London postal area\")\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# Headline numbers for the writeup.\n",
    "hl = summary.to_dicts()\n",
    "new_row = next(r for r in hl if r[\"is_newbuild_prop\"])\n",
    "old_row = next(r for r in hl if not r[\"is_newbuild_prop\"])\n",
    "print(f\"London terraced/detached, repeat sales:\")\n",
    "print(f\"  existing  median CAGR: {old_row['median_cagr_pct']}%/yr (n={old_row['n']:,})\")\n",
    "print(f\"  new-build median CAGR: {new_row['median_cagr_pct']}%/yr (n={new_row['n']:,})\")\n",
    "print(f\"  appreciation gap: {round(new_row['median_cagr_pct']-old_row['median_cagr_pct'],2)} pp/yr\")\n",
    "combined.sort(\"cagr_gap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecdf2fa6",
   "metadata": {},
   "source": [
    "## How to read this — and the caveats\n",
    "\n",
    "- **The \"premium\" for houses is small or negative, but the appreciation lag is the real cost.**\n",
    "  Restricting to terraced/detached strips out the flat-composition effect that inflates headline\n",
    "  new-build premiums, and what's left is a modest (often negative) £/sqm gap for London houses,\n",
    "  paired with a persistent resale-CAGR shortfall vs existing stock. That is the \"new-car effect\":\n",
    "  the premium is paid up front and unwound on resale.\n",
    "- **Recorded new-build prices are biased high.** HM Land Registry records the gross headline price;\n",
    "  developer incentives (stamp duty paid, upgrades, white goods) up to ~5% are *not* deducted. So\n",
    "  the true new-build premium is a little higher than it looks here, and part of the measured\n",
    "  appreciation lag is that hidden premium washing out.\n",
    "- **£/sqm controls for size, not spec or micro-location.** New-builds cluster on specific plots\n",
    "  (regeneration sites, in-fill) that differ from the surrounding resale stock in ways floor area\n",
    "  can't capture.\n",
    "- **CAGRs are nominal** (not inflation-adjusted) and **ignore transaction/selling costs**, so\n",
    "  real returns are lower for both cohorts; the *gap* between them is the robust signal.\n",
    "- **Samples and grain.** Terraced/detached new-builds are a thin slice in London, so we pool a\n",
    "  recent window, report N everywhere, and drop any area below the minimum sample — the same\n",
    "  defensibility discipline as `cheaper_twins.py`. District-level cuts are available via\n",
    "  `premium_by(\"district\")` / `cagr_by(\"district\")` where samples allow.\n",
    "- **For a specific purchase:** value the candidate per £/sqm against recently *sold* (not asking)\n",
    "  terraced/detached comparables in its district, net off any incentives, and weigh the structural\n",
    "  resale-CAGR shortfall against the genuine value-adds new-builds carry (EPC A/B running-cost\n",
    "  savings, NHBC warranty, no chain)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}