{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6947fe33-dd74-4235-90d7-af68aae454ce", "metadata": {}, "outputs": [], "source": [ "%load_ext lab_black" ] }, { "cell_type": "code", "execution_count": 2, "id": "1a572123-f84e-4c61-933d-3f1cede2af8e", "metadata": {}, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import gzip\n", "import json\n", "import re\n", "import datetime\n", "import pathlib\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "id": "7e71aa7f-4d8f-446c-9f58-41ec77ca039e", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "id": "ef0bb3ba-081d-4f16-a4e1-34fd636f9951", "metadata": {}, "outputs": [], "source": [ "pd.set_option(\"display.max_rows\", None)" ] }, { "cell_type": "code", "execution_count": 5, "id": "9c4dd76b-c2e5-454d-98d2-0c5d1f826fb7", "metadata": {}, "outputs": [], "source": [ "TIME_STRING = \"%Y%m%dT%H%M%SZ\"" ] }, { "cell_type": "code", "execution_count": 6, "id": "a837900c-6a6d-4f13-8c6c-25c14cfaaf06", "metadata": {}, "outputs": [], "source": [ "DIRECTORY = \"/home/agude/Projects/blog_projects/zillow_redfin/data/\"" ] }, { "cell_type": "code", "execution_count": 7, "id": "e5f714d3-ca36-4823-b7af-eac742e84185", "metadata": {}, "outputs": [], "source": [ "def get_date_from_file(file_name: str, time_string: str = TIME_STRING) -> datetime:\n", " return datetime.datetime.strptime(\n", " file_name.split(\"_\")[-1].split(\".\")[0], time_string\n", " )" ] }, { "cell_type": "markdown", "id": "b0543217-3baf-4536-923b-9400e9379d68", "metadata": { "tags": [] }, "source": [ "# Zillow" ] }, { "cell_type": "code", "execution_count": 8, "id": "9e2dd087-95b1-4edb-baed-0ad065b057b5", "metadata": {}, "outputs": [], "source": [ "def get_zillow_price(file_name: str):\n", "\n", " with gzip.open(file_name) as fp:\n", " soup = BeautifulSoup(fp, \"html.parser\")\n", "\n", " item = soup.select_one(\"script#hdpApolloPreloadedData\").text\n", " api_cache_string = json.loads(item)[\"apiCache\"]\n", " zillow_data_json = json.loads(api_cache_string)\n", "\n", " for key in zillow_data_json.keys():\n", " targets = zillow_data_json[key][\"property\"].get(\"adTargets\")\n", " if targets is not None:\n", " price = targets.get(\"zestimate\")\n", " break\n", "\n", " return price" ] }, { "cell_type": "code", "execution_count": 9, "id": "0753acaa-4316-46e5-b1fb-4c7117f0875e", "metadata": {}, "outputs": [], "source": [ "price_data = {\"date\": [], \"amount\": []}\n", "\n", "for path in pathlib.Path(DIRECTORY).iterdir():\n", " if path.is_file() and \"zillow_1381\" in str(path):\n", " path_str = str(path)\n", "\n", " price_data[\"date\"].append(get_date_from_file(path_str))\n", " price_data[\"amount\"].append(get_zillow_price(path_str))" ] }, { "cell_type": "code", "execution_count": 10, "id": "8de78aee-1b7e-4494-8729-cc818ee9fbe1", "metadata": {}, "outputs": [], "source": [ "zillow_df = (\n", " pd.DataFrame.from_dict(price_data).sort_values(\"date\").reset_index(drop=True)\n", ")\n", "\n", "zillow_df[\"amount\"] = zillow_df[\"amount\"].astype(\"float\")\n", "\n", "zillow_df[\"website\"] = \"zillow\"" ] }, { "cell_type": "code", "execution_count": 11, "id": "7e704146-8089-49f6-b15d-8919c9cd0404", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateamountwebsite
02022-04-11 01:24:321027400.0zillow
12022-04-11 07:03:451035800.0zillow
22022-04-12 07:04:051035800.0zillow
32022-04-13 07:00:491035800.0zillow
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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(zillow_df[\"date\"], zillow_df[\"amount\"], \".\", color=\"blue\")" ] }, { "cell_type": "markdown", "id": "c4f17000-14c9-424e-becf-6a51e8392f5a", "metadata": {}, "source": [ "# Redfin" ] }, { "cell_type": "code", "execution_count": 13, "id": "516c8a93-60fe-4bc1-a30e-32e8de7a2d4a", "metadata": {}, "outputs": [], "source": [ "def get_redfin_price(file_name: str) -> float:\n", "\n", " date = get_date_from_file(file_name)\n", "\n", " with gzip.open(file_name) as fp:\n", " file_text = str(fp.read())\n", "\n", " return float(file_text.split('predictedValue\\\\\\\\\":')[1].split(\"}\")[0])" ] }, { "cell_type": "code", "execution_count": 14, "id": "b4402eea-b253-482d-9850-b1e02d53b06c", "metadata": {}, "outputs": [], "source": [ "redfin_price_data = {\"date\": [], \"amount\": []}\n", "\n", "for path in pathlib.Path(DIRECTORY).iterdir():\n", " if path.is_file() and \"redfin_1381\" in str(path):\n", " path_str = str(path)\n", "\n", " redfin_price_data[\"date\"].append(get_date_from_file(path_str))\n", " redfin_price_data[\"amount\"].append(get_redfin_price(path_str))" ] }, { "cell_type": "code", "execution_count": 15, "id": "023543c1-2cd8-4807-944c-ebb41e5f36c3", "metadata": {}, "outputs": [], "source": [ "redfin_df = (\n", " pd.DataFrame.from_dict(redfin_price_data).sort_values(\"date\").reset_index(drop=True)\n", ")\n", "\n", "redfin_df[\"amount\"] = redfin_df[\"amount\"].astype(\"float\")\n", "\n", "redfin_df[\"website\"] = \"redfin\"" ] }, { "cell_type": "code", "execution_count": 16, "id": "8b0db34f-53b2-4d05-92f6-8e2668b35739", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateamountwebsite
02022-04-11 01:24:361156960.86redfin
12022-04-11 07:03:491156960.86redfin
22022-04-12 07:04:091156960.86redfin
32022-04-13 07:00:531156960.86redfin
42022-04-14 07:02:361188334.45redfin
52022-04-15 07:01:361188334.45redfin
62022-04-16 07:01:431188334.45redfin
72022-04-17 07:00:371188334.45redfin
82022-04-18 07:02:411188334.45redfin
92022-04-19 07:02:341188334.45redfin
102022-04-20 07:02:471188334.45redfin
112022-04-21 07:03:001188334.45redfin
122022-04-22 07:00:511169268.70redfin
132022-04-23 07:03:321169268.70redfin
142022-04-24 07:05:071169268.70redfin
152022-04-25 07:00:591169268.70redfin
162022-04-26 07:02:121234551.86redfin
172022-04-27 07:03:521253357.70redfin
182022-04-28 07:02:341259087.09redfin
192022-04-29 07:04:121256868.63redfin
202022-04-30 07:02:361256868.63redfin
212022-05-01 07:02:051262774.23redfin
222022-05-02 07:03:491262993.79redfin
232022-05-03 07:22:241262296.79redfin
242022-05-04 07:02:491262292.24redfin
252022-05-05 07:01:201262936.89redfin
262022-05-06 07:02:211262936.89redfin
272022-05-07 07:04:211265774.12redfin
282022-05-08 07:04:541264471.54redfin
292022-05-09 07:02:511263528.52redfin
302022-05-10 07:04:541263528.52redfin
312022-05-11 07:03:391263528.52redfin
322022-05-12 07:01:171265244.53redfin
332022-05-13 07:01:401266800.85redfin
342022-05-14 07:03:301265878.84redfin
352022-05-15 07:01:141265878.84redfin
362022-05-16 07:05:251265878.84redfin
372022-05-17 07:04:541265878.84redfin
382022-05-18 07:02:241265878.84redfin
392022-05-19 07:16:481263741.10redfin
402022-05-20 07:02:361263713.04redfin
412022-05-21 07:02:081263713.04redfin
422022-05-22 07:02:091263297.58redfin
432022-05-23 07:01:051263615.05redfin
442022-05-24 07:01:021263455.00redfin
452022-05-25 07:04:251263455.00redfin
462022-05-26 07:04:371262117.12redfin
472022-05-27 07:05:131262117.12redfin
482022-05-28 07:01:261262117.12redfin
492022-05-29 07:02:071262117.12redfin
502022-05-30 07:05:111262117.12redfin
512022-05-31 07:02:551262117.12redfin
522022-06-01 07:04:311262117.12redfin
532022-06-02 07:05:231259206.48redfin
542022-06-03 07:01:241259206.48redfin
552022-06-04 07:04:131259206.48redfin
562022-06-05 07:01:321252787.11redfin
572022-06-06 07:01:201252787.11redfin
582022-06-08 07:01:591200096.93redfin
592022-06-09 07:05:101200096.93redfin
602022-06-10 07:01:001200096.93redfin
612022-06-11 07:01:191200096.93redfin
622022-06-12 07:03:141200096.93redfin
632022-06-13 07:02:411200096.93redfin
642022-06-14 07:03:381200096.93redfin
652022-06-15 07:04:071200096.93redfin
662022-06-16 07:02:391200096.93redfin
672022-06-17 07:04:271200096.93redfin
682022-06-18 07:04:151200096.93redfin
\n", "
" ], "text/plain": [ " date amount website\n", "0 2022-04-11 01:24:36 1156960.86 redfin\n", "1 2022-04-11 07:03:49 1156960.86 redfin\n", "2 2022-04-12 07:04:09 1156960.86 redfin\n", "3 2022-04-13 07:00:53 1156960.86 redfin\n", "4 2022-04-14 07:02:36 1188334.45 redfin\n", "5 2022-04-15 07:01:36 1188334.45 redfin\n", "6 2022-04-16 07:01:43 1188334.45 redfin\n", "7 2022-04-17 07:00:37 1188334.45 redfin\n", "8 2022-04-18 07:02:41 1188334.45 redfin\n", "9 2022-04-19 07:02:34 1188334.45 redfin\n", "10 2022-04-20 07:02:47 1188334.45 redfin\n", "11 2022-04-21 07:03:00 1188334.45 redfin\n", "12 2022-04-22 07:00:51 1169268.70 redfin\n", "13 2022-04-23 07:03:32 1169268.70 redfin\n", "14 2022-04-24 07:05:07 1169268.70 redfin\n", "15 2022-04-25 07:00:59 1169268.70 redfin\n", "16 2022-04-26 07:02:12 1234551.86 redfin\n", "17 2022-04-27 07:03:52 1253357.70 redfin\n", "18 2022-04-28 07:02:34 1259087.09 redfin\n", "19 2022-04-29 07:04:12 1256868.63 redfin\n", "20 2022-04-30 07:02:36 1256868.63 redfin\n", "21 2022-05-01 07:02:05 1262774.23 redfin\n", "22 2022-05-02 07:03:49 1262993.79 redfin\n", "23 2022-05-03 07:22:24 1262296.79 redfin\n", "24 2022-05-04 07:02:49 1262292.24 redfin\n", "25 2022-05-05 07:01:20 1262936.89 redfin\n", "26 2022-05-06 07:02:21 1262936.89 redfin\n", "27 2022-05-07 07:04:21 1265774.12 redfin\n", "28 2022-05-08 07:04:54 1264471.54 redfin\n", "29 2022-05-09 07:02:51 1263528.52 redfin\n", "30 2022-05-10 07:04:54 1263528.52 redfin\n", "31 2022-05-11 07:03:39 1263528.52 redfin\n", "32 2022-05-12 07:01:17 1265244.53 redfin\n", "33 2022-05-13 07:01:40 1266800.85 redfin\n", "34 2022-05-14 07:03:30 1265878.84 redfin\n", "35 2022-05-15 07:01:14 1265878.84 redfin\n", "36 2022-05-16 07:05:25 1265878.84 redfin\n", "37 2022-05-17 07:04:54 1265878.84 redfin\n", "38 2022-05-18 07:02:24 1265878.84 redfin\n", "39 2022-05-19 07:16:48 1263741.10 redfin\n", "40 2022-05-20 07:02:36 1263713.04 redfin\n", "41 2022-05-21 07:02:08 1263713.04 redfin\n", "42 2022-05-22 07:02:09 1263297.58 redfin\n", "43 2022-05-23 07:01:05 1263615.05 redfin\n", "44 2022-05-24 07:01:02 1263455.00 redfin\n", "45 2022-05-25 07:04:25 1263455.00 redfin\n", "46 2022-05-26 07:04:37 1262117.12 redfin\n", "47 2022-05-27 07:05:13 1262117.12 redfin\n", "48 2022-05-28 07:01:26 1262117.12 redfin\n", "49 2022-05-29 07:02:07 1262117.12 redfin\n", "50 2022-05-30 07:05:11 1262117.12 redfin\n", "51 2022-05-31 07:02:55 1262117.12 redfin\n", "52 2022-06-01 07:04:31 1262117.12 redfin\n", "53 2022-06-02 07:05:23 1259206.48 redfin\n", "54 2022-06-03 07:01:24 1259206.48 redfin\n", "55 2022-06-04 07:04:13 1259206.48 redfin\n", "56 2022-06-05 07:01:32 1252787.11 redfin\n", "57 2022-06-06 07:01:20 1252787.11 redfin\n", "58 2022-06-08 07:01:59 1200096.93 redfin\n", "59 2022-06-09 07:05:10 1200096.93 redfin\n", "60 2022-06-10 07:01:00 1200096.93 redfin\n", "61 2022-06-11 07:01:19 1200096.93 redfin\n", "62 2022-06-12 07:03:14 1200096.93 redfin\n", "63 2022-06-13 07:02:41 1200096.93 redfin\n", "64 2022-06-14 07:03:38 1200096.93 redfin\n", "65 2022-06-15 07:04:07 1200096.93 redfin\n", "66 2022-06-16 07:02:39 1200096.93 redfin\n", "67 2022-06-17 07:04:27 1200096.93 redfin\n", "68 2022-06-18 07:04:15 1200096.93 redfin" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "redfin_df" ] }, { "cell_type": "code", "execution_count": 17, "id": "db93c5ea-e25a-48d4-b8b2-1bd7c150ecf1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(redfin_df[\"date\"], redfin_df[\"amount\"], \".\", color=\"red\")\n", "plt.plot(zillow_df[\"date\"], zillow_df[\"amount\"], \".\", color=\"blue\")" ] }, { "cell_type": "markdown", "id": "afc6164e-8ed9-4e6d-8f8f-60544fc556bc", "metadata": {}, "source": [ "# realtor.com" ] }, { "cell_type": "code", "execution_count": 18, "id": "0f526dad-81b7-4ada-929b-3ec66a0b6686", "metadata": {}, "outputs": [], "source": [ "def get_realtorcom_price(file_name: str):\n", "\n", " with gzip.open(file_name) as fp:\n", " soup = BeautifulSoup(fp, \"html.parser\")\n", "\n", " item = soup.select_one(\"script#__NEXT_DATA__\").text\n", " api_cache_string = json.loads(item)\n", "\n", " try:\n", " estimates = api_cache_string[\"props\"][\"pageProps\"][\"initialPropertyDetails\"][\n", " \"home\"\n", " ][\"estimates\"][\"current_values\"][0]\n", "\n", " except KeyError:\n", " return None\n", "\n", " high, mid, low, source, date, source = (\n", " estimates[\"estimate_high\"],\n", " estimates[\"estimate\"],\n", " estimates[\"estimate_low\"],\n", " estimates[\"estimate_low\"],\n", " estimates[\"date\"],\n", " estimates[\"source\"][\"type\"],\n", " )\n", "\n", " return high, mid, low, source, date, source" ] }, { "cell_type": "code", "execution_count": 19, "id": "f563c9a7-7fa5-40fe-8014-85d9267d3e25", "metadata": {}, "outputs": [], "source": [ "realtorcom_price_data = {\n", " \"date\": [],\n", " \"prediction_date\": [],\n", " \"amount\": [],\n", " \"amount_high\": [],\n", " \"amount_low\": [],\n", " \"source\": [],\n", "}\n", "\n", "for path in pathlib.Path(DIRECTORY).iterdir():\n", " if path.is_file() and \"realtorcom_1381\" in str(path):\n", " path_str = str(path)\n", "\n", " try:\n", " high, mid, low, source, date, source = get_realtorcom_price(path_str)\n", "\n", " except TypeError: # Returned none\n", " continue\n", "\n", " model_date = datetime.datetime.strptime(date, \"%Y-%m-%d\")\n", " read_date = get_date_from_file(path_str)\n", "\n", " realtorcom_price_data[\"date\"].append(read_date)\n", " realtorcom_price_data[\"prediction_date\"].append(model_date)\n", " realtorcom_price_data[\"amount\"].append(mid)\n", " realtorcom_price_data[\"amount_high\"].append(high)\n", " realtorcom_price_data[\"amount_low\"].append(low)\n", " realtorcom_price_data[\"source\"].append(source)" ] }, { "cell_type": "code", "execution_count": 20, "id": "32cd5885-c58f-45b5-8015-2dd7d1f2692e", "metadata": {}, "outputs": [], "source": [ "realtor_df = (pd.DataFrame.from_dict(realtorcom_price_data)).drop_duplicates()\n", "\n", "start_date = datetime.date(2022, 4, 22)\n", "end_date = datetime.date(2022, 5, 27)\n", "\n", "dates = []\n", "for n in range(int((end_date - start_date).days)):\n", " n_date = start_date + datetime.timedelta(n)\n", " dates.append([n_date, n_date, None, None, None, None])\n", "\n", "append_df = pd.DataFrame(\n", " columns=realtor_df.columns,\n", " data=dates,\n", ")\n", "\n", "realtor_df = pd.concat([realtor_df, append_df])\n", "\n", "# Cast Columns as nullable int\n", "cast_cols = [\"amount\", \"amount_high\", \"amount_low\"]\n", "realtor_df[cast_cols] = realtor_df[cast_cols].astype(\"float\")\n", "\n", "date_cols = [\"date\", \"prediction_date\"]\n", "realtor_df[date_cols] = realtor_df[date_cols].astype(\"datetime64[ns]\")\n", "\n", "realtor_df[\"error_low\"] = realtor_df[\"amount\"] - realtor_df[\"amount_low\"]\n", "realtor_df[\"error_high\"] = realtor_df[\"amount_high\"] - realtor_df[\"amount\"]\n", "\n", "\n", "realtor_df = realtor_df.sort_values(\"date\").reset_index(drop=True)\n", "\n", "realtor_df[\"website\"] = \"realtor.com\"" ] }, { "cell_type": "code", "execution_count": 21, "id": "babff326-eeb7-4fd9-bf4e-485f59fb31d8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dateprediction_dateamountamount_highamount_lowsourceerror_lowerror_highwebsite
02022-04-11 01:24:402022-03-311104300.01268500.0940200.0corelogic164100.0164200.0realtor.com
12022-04-11 07:03:532022-03-311104300.01268500.0940200.0corelogic164100.0164200.0realtor.com
22022-04-12 07:04:142022-03-311104300.01268500.0940200.0corelogic164100.0164200.0realtor.com
32022-04-13 07:00:572022-03-311104300.01268500.0940200.0corelogic164100.0164200.0realtor.com
42022-04-14 07:02:412022-03-311104300.01268500.0940200.0corelogic164100.0164200.0realtor.com
\n", "
" ], "text/plain": [ " date prediction_date amount amount_high amount_low \\\n", "0 2022-04-11 01:24:40 2022-03-31 1104300.0 1268500.0 940200.0 \n", "1 2022-04-11 07:03:53 2022-03-31 1104300.0 1268500.0 940200.0 \n", "2 2022-04-12 07:04:14 2022-03-31 1104300.0 1268500.0 940200.0 \n", "3 2022-04-13 07:00:57 2022-03-31 1104300.0 1268500.0 940200.0 \n", "4 2022-04-14 07:02:41 2022-03-31 1104300.0 1268500.0 940200.0 \n", "\n", " source error_low error_high website \n", "0 corelogic 164100.0 164200.0 realtor.com \n", "1 corelogic 164100.0 164200.0 realtor.com \n", "2 corelogic 164100.0 164200.0 realtor.com \n", "3 corelogic 164100.0 164200.0 realtor.com \n", "4 corelogic 164100.0 164200.0 realtor.com " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "realtor_df.head()" ] }, { "cell_type": "code", "execution_count": 22, "id": "1904969d-79a8-40fd-8c78-8cb547ae3ce6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(redfin_df[\"date\"], redfin_df[\"amount\"], \".\", color=\"red\")\n", "plt.plot(zillow_df[\"date\"], zillow_df[\"amount\"], \".\", color=\"blue\")\n", "plt.plot(realtor_df[\"date\"], realtor_df[\"amount\"], \".\", color=\"purple\")" ] }, { "cell_type": "markdown", "id": "1d89832e-ec8a-478c-8b1d-d257b1c5041a", "metadata": {}, "source": [ "# Combine" ] }, { "cell_type": "code", "execution_count": 23, "id": "563c69be-a10f-499a-b31b-d7af998cbc9c", "metadata": {}, "outputs": [], "source": [ "combined_df = pd.concat(\n", " [\n", " realtor_df,\n", " redfin_df,\n", " zillow_df,\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": 24, "id": "c6984926-805e-4ecd-9b3a-28ddcdb60f25", "metadata": {}, "outputs": [], "source": [ "combined_df.to_json(\"./home_price_estimate_20220701.json\", orient=\"table\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.9" } }, "nbformat": 4, "nbformat_minor": 5 }