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Datasets:
misikoff
/
zillow

Tasks:
Tabular Regression
Time Series Forecasting
Languages:
English
License:
Dataset card Files Community
misikoff commited on
Commit
b35ac2b
•
1 Parent(s): 62c73eb

fix: update region types, dates, and home types to match each other

Browse files
Files changed (11) hide show
  1. README.md +2 -2
  2. checker.ipynb +106 -130
  3. processors/days_on_market.ipynb +293 -2
  4. processors/for_sale_listings.ipynb +12 -12
  5. processors/home_values.ipynb +150 -500
  6. processors/home_values_forecasts.ipynb +360 -43
  7. processors/new_construction.ipynb +12 -12
  8. processors/rentals.ipynb +50 -37
  9. processors/sales.ipynb +9 -9
  10. test-sales.parquet +3 -0
  11. zillow.py +32 -24
README.md CHANGED
@@ -39,7 +39,7 @@ dataset_info:
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  class_label:
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  names:
41
  '0': SFR
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- '1': all homes (SFR + Condo)
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  - name: Date
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  dtype: string
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  id: Date
@@ -257,7 +257,7 @@ dataset_info:
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  names:
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  '0': SFR
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  '1': all homes
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- '2': condo/co-op only
261
  - name: Date
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  dtype: string
263
  id: Date
 
39
  class_label:
40
  names:
41
  '0': SFR
42
+ '1': all homes
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  - name: Date
44
  dtype: string
45
  id: Date
 
257
  names:
258
  '0': SFR
259
  '1': all homes
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+ '2': condo/co-op
261
  - name: Date
262
  dtype: string
263
  id: Date
checker.ipynb CHANGED
@@ -12,7 +12,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 30,
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  "metadata": {},
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  "outputs": [
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  {
@@ -388,7 +388,7 @@
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  "[255024 rows x 18 columns]"
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  ]
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  },
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- "execution_count": 30,
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  "metadata": {},
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  "output_type": "execute_result"
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  }
@@ -463,95 +463,18 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 34,
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  "metadata": {},
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- "outputs": [
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "/Users/misikoff/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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- " from .autonotebook import tqdm as notebook_tqdm\n"
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- ]
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- }
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- ],
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  "source": [
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  "from datasets import load_dataset"
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  ]
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 35,
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  "metadata": {},
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  "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "home_values_forecasts\n"
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- ]
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- },
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 15.9MB/s]\n",
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- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 19.4MB/s]\n",
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- "Downloading data: 100%|██████████| 14.1M/14.1M [00:01<00:00, 11.0MB/s]\n",
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- "Generating train split: 31854 examples [00:01, 27592.53 examples/s]\n"
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- ]
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- },
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "new_construction\n"
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- ]
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- },
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 7.13MB/s]\n",
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- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 10.4MB/s]\n",
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- "Downloading data: 100%|██████████| 10.9M/10.9M [00:01<00:00, 5.63MB/s]\n",
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- "Generating train split: 49487 examples [00:01, 42744.19 examples/s]\n"
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- ]
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- },
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "for_sale_listings\n"
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- ]
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- },
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 5.22MB/s]\n",
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- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 4.72MB/s]\n",
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- "Downloading data: 100%|██████████| 180M/180M [00:06<00:00, 28.9MB/s] \n",
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- "Generating train split: 578653 examples [00:16, 34651.20 examples/s]\n"
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- ]
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- },
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "rentals\n"
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- ]
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- },
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 6.18MB/s]\n",
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- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 13.3MB/s]\n",
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- "Downloading data: 100%|██████████| 446M/446M [00:11<00:00, 38.2MB/s] \n",
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- "Generating train split: 1258740 examples [00:28, 44189.96 examples/s]\n"
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- ]
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- },
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  {
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  "name": "stdout",
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  "output_type": "stream",
@@ -563,44 +486,10 @@
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  "name": "stderr",
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  "output_type": "stream",
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  "text": [
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- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 12.7MB/s]\n",
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- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 13.5MB/s]\n",
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- "Downloading data: 100%|██████████| 139M/139M [00:04<00:00, 34.2MB/s] \n",
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- "Generating train split: 255024 examples [00:09, 26686.54 examples/s]\n"
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- ]
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- },
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "home_values\n"
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- ]
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- },
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 2.18MB/s]\n",
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- "Downloading readme: 100%|██████████| 13.2k/13.2k [00:00<00:00, 8.86MB/s]\n",
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- "Downloading data: 100%|██████████| 42.1M/42.1M [00:01<00:00, 32.4MB/s]\n",
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- "Generating train split: 117912 examples [00:03, 37382.19 examples/s]\n"
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- ]
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- },
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "days_on_market\n"
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- ]
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- },
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 12.7MB/s]\n",
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- "Downloading readme: 100%|████████���█| 13.2k/13.2k [00:00<00:00, 8.86MB/s]\n",
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- "Downloading data: 100%|██████████| 233M/233M [00:06<00:00, 34.9MB/s] \n",
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- "Generating train split: 586714 examples [00:17, 34104.98 examples/s]\n"
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  ]
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  }
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  ],
@@ -608,13 +497,13 @@
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  "dataset_dict = {}\n",
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  "\n",
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  "configs = [\n",
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- " \"home_values_forecasts\",\n",
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- " \"new_construction\",\n",
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- " \"for_sale_listings\",\n",
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- " \"rentals\",\n",
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  " \"sales\",\n",
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- " \"home_values\",\n",
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- " \"days_on_market\",\n",
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  "]\n",
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  "for config in configs:\n",
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  " print(config)\n",
@@ -623,19 +512,106 @@
623
  " config,\n",
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  " trust_remote_code=True,\n",
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  " download_mode=\"force_redownload\",\n",
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- " cache_dir=\"~/desktop/cache\",\n",
627
  " )"
628
  ]
629
  },
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  {
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  "cell_type": "code",
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- "execution_count": null,
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  "metadata": {},
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- "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
635
  "source": [
 
 
 
636
  "df = pd.read_feather(\n",
637
  " \"~/desktop/cache/misikoff___zillow/sales/1.1.0/c70d9545e9cef7612b795e19b5393a565f297e17856ab372df6f4026ecc498ae/zillow-train.arrow\"\n",
638
- ")"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
639
  ]
640
  }
641
  ],
 
12
  },
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  {
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  "cell_type": "code",
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+ "execution_count": 2,
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  "metadata": {},
17
  "outputs": [
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  {
 
388
  "[255024 rows x 18 columns]"
389
  ]
390
  },
391
+ "execution_count": 2,
392
  "metadata": {},
393
  "output_type": "execute_result"
394
  }
 
463
  },
464
  {
465
  "cell_type": "code",
466
+ "execution_count": 8,
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  "metadata": {},
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+ "outputs": [],
 
 
 
 
 
 
 
 
 
469
  "source": [
470
  "from datasets import load_dataset"
471
  ]
472
  },
473
  {
474
  "cell_type": "code",
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+ "execution_count": 19,
476
  "metadata": {},
477
  "outputs": [
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
478
  {
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  "name": "stdout",
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  "output_type": "stream",
 
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  "name": "stderr",
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  "output_type": "stream",
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  "text": [
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+ "Downloading builder script: 100%|██████████| 26.8k/26.8k [00:00<00:00, 14.2MB/s]\n",
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+ "Downloading readme: 100%|██████████| 21.7k/21.7k [00:00<00:00, 3.80MB/s]\n",
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+ "Downloading data: 100%|██████████| 139M/139M [00:04<00:00, 32.2MB/s] \n",
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+ "Generating train split: 100%|██████████| 255024/255024 [00:10<00:00, 24068.33 examples/s]\n"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
493
  ]
494
  }
495
  ],
 
497
  "dataset_dict = {}\n",
498
  "\n",
499
  "configs = [\n",
500
+ " # \"home_values_forecasts\",\n",
501
+ " # \"new_construction\",\n",
502
+ " # \"for_sale_listings\",\n",
503
+ " # \"rentals\",\n",
504
  " \"sales\",\n",
505
+ " # \"home_values\",\n",
506
+ " # \"days_on_market\",\n",
507
  "]\n",
508
  "for config in configs:\n",
509
  " print(config)\n",
 
512
  " config,\n",
513
  " trust_remote_code=True,\n",
514
  " download_mode=\"force_redownload\",\n",
515
+ " cache_dir=\"./cache\",\n",
516
  " )"
517
  ]
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 40,
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  "metadata": {},
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+ "outputs": [
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+ {
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+ "ename": "ArrowInvalid",
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+ "evalue": "Not a Feather V1 or Arrow IPC file",
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+ "output_type": "error",
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+ "traceback": [
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[0;31mArrowInvalid\u001b[0m Traceback (most recent call last)",
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+ "Cell \u001b[0;32mIn[40], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyarrow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpa\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_feather\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m~/desktop/cache/misikoff___zillow/sales/1.1.0/c70d9545e9cef7612b795e19b5393a565f297e17856ab372df6f4026ecc498ae/zillow-train.arrow\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 6\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m df\n",
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+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pandas/io/feather_format.py:124\u001b[0m, in \u001b[0;36mread_feather\u001b[0;34m(path, columns, use_threads, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m get_handle(\n\u001b[1;32m 121\u001b[0m path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m, storage_options\u001b[38;5;241m=\u001b[39mstorage_options, is_text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 122\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_backend \u001b[38;5;129;01mis\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m using_pyarrow_string_dtype():\n\u001b[0;32m--> 124\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfeather\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_feather\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 125\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandles\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43muse_threads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 126\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 128\u001b[0m pa_table \u001b[38;5;241m=\u001b[39m feather\u001b[38;5;241m.\u001b[39mread_table(\n\u001b[1;32m 129\u001b[0m handles\u001b[38;5;241m.\u001b[39mhandle, columns\u001b[38;5;241m=\u001b[39mcolumns, use_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mbool\u001b[39m(use_threads)\n\u001b[1;32m 130\u001b[0m )\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_backend \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy_nullable\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
533
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/feather.py:226\u001b[0m, in \u001b[0;36mread_feather\u001b[0;34m(source, columns, use_threads, memory_map, **kwargs)\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_feather\u001b[39m(source, columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, use_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 200\u001b[0m memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 201\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;124;03m Read a pandas.DataFrame from Feather format. To read as pyarrow.Table use\u001b[39;00m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;124;03m feather.read_table.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;124;03m The contents of the Feather file as a pandas.DataFrame\u001b[39;00m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[43mread_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 227\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemory_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 228\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_threads\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto_pandas(use_threads\u001b[38;5;241m=\u001b[39muse_threads, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs))\n",
534
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/feather.py:252\u001b[0m, in \u001b[0;36mread_table\u001b[0;34m(source, columns, memory_map, use_threads)\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_table\u001b[39m(source, columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, use_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 232\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;124;03m Read a pyarrow.Table from Feather format\u001b[39;00m\n\u001b[1;32m 234\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[38;5;124;03m The contents of the Feather file as a pyarrow.Table\u001b[39;00m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 252\u001b[0m reader \u001b[38;5;241m=\u001b[39m \u001b[43m_feather\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFeatherReader\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_memory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemory_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_threads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reader\u001b[38;5;241m.\u001b[39mread()\n",
535
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/_feather.pyx:79\u001b[0m, in \u001b[0;36mpyarrow._feather.FeatherReader.__cinit__\u001b[0;34m()\u001b[0m\n",
536
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/error.pxi:154\u001b[0m, in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[0;34m()\u001b[0m\n",
537
+ "File \u001b[0;32m~/opt/anaconda3/envs/sta663/lib/python3.12/site-packages/pyarrow/error.pxi:91\u001b[0m, in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n",
538
+ "\u001b[0;31mArrowInvalid\u001b[0m: Not a Feather V1 or Arrow IPC file"
539
+ ]
540
+ }
541
+ ],
542
  "source": [
543
+ "import pyarrow as pa\n",
544
+ "\n",
545
+ "\n",
546
  "df = pd.read_feather(\n",
547
  " \"~/desktop/cache/misikoff___zillow/sales/1.1.0/c70d9545e9cef7612b795e19b5393a565f297e17856ab372df6f4026ecc498ae/zillow-train.arrow\"\n",
548
+ ")\n",
549
+ "df"
550
+ ]
551
+ },
552
+ {
553
+ "cell_type": "code",
554
+ "execution_count": 20,
555
+ "metadata": {},
556
+ "outputs": [
557
+ {
558
+ "name": "stderr",
559
+ "output_type": "stream",
560
+ "text": [
561
+ "Creating parquet from Arrow format: 100%|██████████| 256/256 [00:00<00:00, 738.39ba/s]\n"
562
+ ]
563
+ },
564
+ {
565
+ "data": {
566
+ "text/plain": [
567
+ "27088039"
568
+ ]
569
+ },
570
+ "execution_count": 20,
571
+ "metadata": {},
572
+ "output_type": "execute_result"
573
+ }
574
+ ],
575
+ "source": [
576
+ "dataset_dict[config][\"train\"].to_parquet(\"test-sales.parquet\")"
577
+ ]
578
+ },
579
+ {
580
+ "cell_type": "code",
581
+ "execution_count": 32,
582
+ "metadata": {},
583
+ "outputs": [
584
+ {
585
+ "data": {
586
+ "text/plain": [
587
+ "{'Region ID': '102001',\n",
588
+ " 'Size Rank': 0,\n",
589
+ " 'Region': 'United States',\n",
590
+ " 'Region Type': 0,\n",
591
+ " 'State': None,\n",
592
+ " 'Home Type': 0,\n",
593
+ " 'Date': datetime.datetime(2008, 2, 2, 0, 0),\n",
594
+ " 'Mean Sale to List Ratio (Smoothed)': None,\n",
595
+ " 'Median Sale to List Ratio': None,\n",
596
+ " 'Median Sale Price': 172000.0,\n",
597
+ " 'Median Sale Price (Smoothed) (Seasonally Adjusted)': None,\n",
598
+ " 'Median Sale Price (Smoothed)': None,\n",
599
+ " 'Median Sale to List Ratio (Smoothed)': None,\n",
600
+ " '% Sold Below List': None,\n",
601
+ " '% Sold Below List (Smoothed)': None,\n",
602
+ " '% Sold Above List': None,\n",
603
+ " '% Sold Above List (Smoothed)': None,\n",
604
+ " 'Mean Sale to List Ratio': None}"
605
+ ]
606
+ },
607
+ "execution_count": 32,
608
+ "metadata": {},
609
+ "output_type": "execute_result"
610
+ }
611
+ ],
612
+ "source": [
613
+ "gen = iter(dataset_dict[config][\"train\"])\n",
614
+ "next(gen)"
615
  ]
616
  }
617
  ],
processors/days_on_market.ipynb CHANGED
@@ -91,6 +91,297 @@
91
  "processing Metro_mean_listings_price_cut_amt_uc_sfr_month.csv\n",
92
  "processing Metro_mean_doz_pending_uc_sfrcondo_month.csv\n"
93
  ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
94
  }
95
  ],
96
  "source": [
@@ -148,7 +439,7 @@
148
  },
149
  {
150
  "cell_type": "code",
151
- "execution_count": 4,
152
  "metadata": {},
153
  "outputs": [
154
  {
@@ -438,7 +729,7 @@
438
  "[586714 rows x 13 columns]"
439
  ]
440
  },
441
- "execution_count": 4,
442
  "metadata": {},
443
  "output_type": "execute_result"
444
  }
 
91
  "processing Metro_mean_listings_price_cut_amt_uc_sfr_month.csv\n",
92
  "processing Metro_mean_doz_pending_uc_sfrcondo_month.csv\n"
93
  ]
94
+ },
95
+ {
96
+ "data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>RegionID</th>\n",
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+ " <th>SizeRank</th>\n",
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+ " <th>RegionName</th>\n",
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+ " <th>RegionType</th>\n",
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+ " <th>StateName</th>\n",
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+ " <th>Home Type</th>\n",
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+ " <th>Date</th>\n",
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+ " <th>Percent Listings Price Cut</th>\n",
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+ " <th>Mean Listings Price Cut Amount</th>\n",
125
+ " <th>Percent Listings Price Cut (Smoothed)</th>\n",
126
+ " <th>Mean Listings Price Cut Amount (Smoothed)</th>\n",
127
+ " <th>Median Days on Pending (Smoothed)</th>\n",
128
+ " <th>Median Days on Pending</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>102001</td>\n",
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+ " <td>0</td>\n",
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+ " <td>United States</td>\n",
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+ " <td>country</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>SFR</td>\n",
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+ " <td>2018-01-06</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>13508.368375</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " </tr>\n",
148
+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>102001</td>\n",
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+ " <td>0</td>\n",
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+ " <td>United States</td>\n",
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+ " <td>country</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>SFR</td>\n",
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+ " <td>2018-01-13</td>\n",
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+ " <td>0.049042</td>\n",
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+ " <td>14114.788383</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " </tr>\n",
164
+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>102001</td>\n",
167
+ " <td>0</td>\n",
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+ " <td>United States</td>\n",
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+ " <td>country</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>SFR</td>\n",
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+ " <td>2018-01-20</td>\n",
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+ " <td>0.044740</td>\n",
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+ " <td>14326.128956</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " </tr>\n",
180
+ " <tr>\n",
181
+ " <th>3</th>\n",
182
+ " <td>102001</td>\n",
183
+ " <td>0</td>\n",
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+ " <td>United States</td>\n",
185
+ " <td>country</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>SFR</td>\n",
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+ " <td>2018-01-27</td>\n",
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+ " <td>0.047930</td>\n",
190
+ " <td>13998.585612</td>\n",
191
+ " <td>NaN</td>\n",
192
+ " <td>13998.585612</td>\n",
193
+ " <td>NaN</td>\n",
194
+ " <td>NaN</td>\n",
195
+ " </tr>\n",
196
+ " <tr>\n",
197
+ " <th>4</th>\n",
198
+ " <td>102001</td>\n",
199
+ " <td>0</td>\n",
200
+ " <td>United States</td>\n",
201
+ " <td>country</td>\n",
202
+ " <td>NaN</td>\n",
203
+ " <td>SFR</td>\n",
204
+ " <td>2018-02-03</td>\n",
205
+ " <td>0.047622</td>\n",
206
+ " <td>14120.035549</td>\n",
207
+ " <td>0.047622</td>\n",
208
+ " <td>14120.035549</td>\n",
209
+ " <td>NaN</td>\n",
210
+ " <td>NaN</td>\n",
211
+ " </tr>\n",
212
+ " <tr>\n",
213
+ " <th>...</th>\n",
214
+ " <td>...</td>\n",
215
+ " <td>...</td>\n",
216
+ " <td>...</td>\n",
217
+ " <td>...</td>\n",
218
+ " <td>...</td>\n",
219
+ " <td>...</td>\n",
220
+ " <td>...</td>\n",
221
+ " <td>...</td>\n",
222
+ " <td>...</td>\n",
223
+ " <td>...</td>\n",
224
+ " <td>...</td>\n",
225
+ " <td>...</td>\n",
226
+ " <td>...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
229
+ " <th>586709</th>\n",
230
+ " <td>845172</td>\n",
231
+ " <td>769</td>\n",
232
+ " <td>Winfield, KS</td>\n",
233
+ " <td>msa</td>\n",
234
+ " <td>KS</td>\n",
235
+ " <td>all homes</td>\n",
236
+ " <td>2024-01-06</td>\n",
237
+ " <td>0.094017</td>\n",
238
+ " <td>NaN</td>\n",
239
+ " <td>0.037378</td>\n",
240
+ " <td>NaN</td>\n",
241
+ " <td>NaN</td>\n",
242
+ " <td>NaN</td>\n",
243
+ " </tr>\n",
244
+ " <tr>\n",
245
+ " <th>586710</th>\n",
246
+ " <td>845172</td>\n",
247
+ " <td>769</td>\n",
248
+ " <td>Winfield, KS</td>\n",
249
+ " <td>msa</td>\n",
250
+ " <td>KS</td>\n",
251
+ " <td>all homes</td>\n",
252
+ " <td>2024-01-13</td>\n",
253
+ " <td>0.070175</td>\n",
254
+ " <td>NaN</td>\n",
255
+ " <td>0.043203</td>\n",
256
+ " <td>NaN</td>\n",
257
+ " <td>NaN</td>\n",
258
+ " <td>NaN</td>\n",
259
+ " </tr>\n",
260
+ " <tr>\n",
261
+ " <th>586711</th>\n",
262
+ " <td>845172</td>\n",
263
+ " <td>769</td>\n",
264
+ " <td>Winfield, KS</td>\n",
265
+ " <td>msa</td>\n",
266
+ " <td>KS</td>\n",
267
+ " <td>all homes</td>\n",
268
+ " <td>2024-01-20</td>\n",
269
+ " <td>0.043478</td>\n",
270
+ " <td>NaN</td>\n",
271
+ " <td>0.054073</td>\n",
272
+ " <td>NaN</td>\n",
273
+ " <td>NaN</td>\n",
274
+ " <td>NaN</td>\n",
275
+ " </tr>\n",
276
+ " <tr>\n",
277
+ " <th>586712</th>\n",
278
+ " <td>845172</td>\n",
279
+ " <td>769</td>\n",
280
+ " <td>Winfield, KS</td>\n",
281
+ " <td>msa</td>\n",
282
+ " <td>KS</td>\n",
283
+ " <td>all homes</td>\n",
284
+ " <td>2024-01-27</td>\n",
285
+ " <td>0.036697</td>\n",
286
+ " <td>NaN</td>\n",
287
+ " <td>0.061092</td>\n",
288
+ " <td>NaN</td>\n",
289
+ " <td>NaN</td>\n",
290
+ " <td>NaN</td>\n",
291
+ " </tr>\n",
292
+ " <tr>\n",
293
+ " <th>586713</th>\n",
294
+ " <td>845172</td>\n",
295
+ " <td>769</td>\n",
296
+ " <td>Winfield, KS</td>\n",
297
+ " <td>msa</td>\n",
298
+ " <td>KS</td>\n",
299
+ " <td>all homes</td>\n",
300
+ " <td>2024-02-03</td>\n",
301
+ " <td>0.077670</td>\n",
302
+ " <td>NaN</td>\n",
303
+ " <td>0.057005</td>\n",
304
+ " <td>NaN</td>\n",
305
+ " <td>NaN</td>\n",
306
+ " <td>NaN</td>\n",
307
+ " </tr>\n",
308
+ " </tbody>\n",
309
+ "</table>\n",
310
+ "<p>586714 rows × 13 columns</p>\n",
311
+ "</div>"
312
+ ],
313
+ "text/plain": [
314
+ " RegionID SizeRank RegionName RegionType StateName Home Type \\\n",
315
+ "0 102001 0 United States country NaN SFR \n",
316
+ "1 102001 0 United States country NaN SFR \n",
317
+ "2 102001 0 United States country NaN SFR \n",
318
+ "3 102001 0 United States country NaN SFR \n",
319
+ "4 102001 0 United States country NaN SFR \n",
320
+ "... ... ... ... ... ... ... \n",
321
+ "586709 845172 769 Winfield, KS msa KS all homes \n",
322
+ "586710 845172 769 Winfield, KS msa KS all homes \n",
323
+ "586711 845172 769 Winfield, KS msa KS all homes \n",
324
+ "586712 845172 769 Winfield, KS msa KS all homes \n",
325
+ "586713 845172 769 Winfield, KS msa KS all homes \n",
326
+ "\n",
327
+ " Date Percent Listings Price Cut \\\n",
328
+ "0 2018-01-06 NaN \n",
329
+ "1 2018-01-13 0.049042 \n",
330
+ "2 2018-01-20 0.044740 \n",
331
+ "3 2018-01-27 0.047930 \n",
332
+ "4 2018-02-03 0.047622 \n",
333
+ "... ... ... \n",
334
+ "586709 2024-01-06 0.094017 \n",
335
+ "586710 2024-01-13 0.070175 \n",
336
+ "586711 2024-01-20 0.043478 \n",
337
+ "586712 2024-01-27 0.036697 \n",
338
+ "586713 2024-02-03 0.077670 \n",
339
+ "\n",
340
+ " Mean Listings Price Cut Amount Percent Listings Price Cut (Smoothed) \\\n",
341
+ "0 13508.368375 NaN \n",
342
+ "1 14114.788383 NaN \n",
343
+ "2 14326.128956 NaN \n",
344
+ "3 13998.585612 NaN \n",
345
+ "4 14120.035549 0.047622 \n",
346
+ "... ... ... \n",
347
+ "586709 NaN 0.037378 \n",
348
+ "586710 NaN 0.043203 \n",
349
+ "586711 NaN 0.054073 \n",
350
+ "586712 NaN 0.061092 \n",
351
+ "586713 NaN 0.057005 \n",
352
+ "\n",
353
+ " Mean Listings Price Cut Amount (Smoothed) \\\n",
354
+ "0 NaN \n",
355
+ "1 NaN \n",
356
+ "2 NaN \n",
357
+ "3 13998.585612 \n",
358
+ "4 14120.035549 \n",
359
+ "... ... \n",
360
+ "586709 NaN \n",
361
+ "586710 NaN \n",
362
+ "586711 NaN \n",
363
+ "586712 NaN \n",
364
+ "586713 NaN \n",
365
+ "\n",
366
+ " Median Days on Pending (Smoothed) Median Days on Pending \n",
367
+ "0 NaN NaN \n",
368
+ "1 NaN NaN \n",
369
+ "2 NaN NaN \n",
370
+ "3 NaN NaN \n",
371
+ "4 NaN NaN \n",
372
+ "... ... ... \n",
373
+ "586709 NaN NaN \n",
374
+ "586710 NaN NaN \n",
375
+ "586711 NaN NaN \n",
376
+ "586712 NaN NaN \n",
377
+ "586713 NaN NaN \n",
378
+ "\n",
379
+ "[586714 rows x 13 columns]"
380
+ ]
381
+ },
382
+ "execution_count": 8,
383
+ "metadata": {},
384
+ "output_type": "execute_result"
385
  }
386
  ],
387
  "source": [
 
439
  },
440
  {
441
  "cell_type": "code",
442
+ "execution_count": 9,
443
  "metadata": {},
444
  "outputs": [
445
  {
 
729
  "[586714 rows x 13 columns]"
730
  ]
731
  },
732
+ "execution_count": 9,
733
  "metadata": {},
734
  "output_type": "execute_result"
735
  }
processors/for_sale_listings.ipynb CHANGED
@@ -632,18 +632,18 @@
632
  "578651 845172 769 Winfield, KS msa KS all homes \n",
633
  "578652 845172 769 Winfield, KS msa KS all homes \n",
634
  "\n",
635
- " Date Median Listing Price Median Listing Price (Smoothed) \\\n",
636
- "0 2018-01-13 259000.0 NaN \n",
637
- "1 2018-01-20 259900.0 NaN \n",
638
- "2 2018-01-27 259900.0 NaN \n",
639
- "3 2018-02-03 260000.0 259700.0 \n",
640
- "4 2018-02-10 264900.0 261175.0 \n",
641
- "... ... ... ... \n",
642
- "578648 2023-12-09 134950.0 138913.0 \n",
643
- "578649 2023-12-16 120000.0 133938.0 \n",
644
- "578650 2023-12-23 111000.0 126463.0 \n",
645
- "578651 2023-12-30 126950.0 123225.0 \n",
646
- "578652 2024-01-06 128000.0 121488.0 \n",
647
  "\n",
648
  " New Pending (Smoothed) New Listings New Listings (Smoothed) \\\n",
649
  "0 NaN NaN NaN \n",
 
632
  "578651 845172 769 Winfield, KS msa KS all homes \n",
633
  "578652 845172 769 Winfield, KS msa KS all homes \n",
634
  "\n",
635
+ " Date Median Listing Price Median Listing Price (Smoothed) \\\n",
636
+ "0 2018-01-13 259000.0 NaN \n",
637
+ "1 2018-01-20 259900.0 NaN \n",
638
+ "2 2018-01-27 259900.0 NaN \n",
639
+ "3 2018-02-03 260000.0 259700.0 \n",
640
+ "4 2018-02-10 264900.0 261175.0 \n",
641
+ "... ... ... ... \n",
642
+ "578648 2023-12-09 134950.0 138913.0 \n",
643
+ "578649 2023-12-16 120000.0 133938.0 \n",
644
+ "578650 2023-12-23 111000.0 126463.0 \n",
645
+ "578651 2023-12-30 126950.0 123225.0 \n",
646
+ "578652 2024-01-06 128000.0 121488.0 \n",
647
  "\n",
648
  " New Pending (Smoothed) New Listings New Listings (Smoothed) \\\n",
649
  "0 NaN NaN NaN \n",
processors/home_values.ipynb CHANGED
@@ -32,7 +32,7 @@
32
  },
33
  {
34
  "cell_type": "code",
35
- "execution_count": 5,
36
  "metadata": {},
37
  "outputs": [
38
  {
@@ -140,7 +140,7 @@
140
  " <td>state</td>\n",
141
  " <td>nan</td>\n",
142
  " <td>1-Bedroom</td>\n",
143
- " <td>all homes (SFR/condo)</td>\n",
144
  " <td>2000-01-31</td>\n",
145
  " <td>81310.639504</td>\n",
146
  " <td>NaN</td>\n",
@@ -154,7 +154,7 @@
154
  " <td>state</td>\n",
155
  " <td>nan</td>\n",
156
  " <td>1-Bedroom</td>\n",
157
- " <td>all homes (SFR/condo)</td>\n",
158
  " <td>2000-02-29</td>\n",
159
  " <td>80419.761984</td>\n",
160
  " <td>NaN</td>\n",
@@ -168,7 +168,7 @@
168
  " <td>state</td>\n",
169
  " <td>nan</td>\n",
170
  " <td>1-Bedroom</td>\n",
171
- " <td>all homes (SFR/condo)</td>\n",
172
  " <td>2000-03-31</td>\n",
173
  " <td>80480.449461</td>\n",
174
  " <td>NaN</td>\n",
@@ -182,7 +182,7 @@
182
  " <td>state</td>\n",
183
  " <td>nan</td>\n",
184
  " <td>1-Bedroom</td>\n",
185
- " <td>all homes (SFR/condo)</td>\n",
186
  " <td>2000-04-30</td>\n",
187
  " <td>79799.206525</td>\n",
188
  " <td>NaN</td>\n",
@@ -196,7 +196,7 @@
196
  " <td>state</td>\n",
197
  " <td>nan</td>\n",
198
  " <td>1-Bedroom</td>\n",
199
- " <td>all homes (SFR/condo)</td>\n",
200
  " <td>2000-05-31</td>\n",
201
  " <td>79666.469861</td>\n",
202
  " <td>NaN</td>\n",
@@ -224,7 +224,7 @@
224
  " <td>state</td>\n",
225
  " <td>nan</td>\n",
226
  " <td>All Bedrooms</td>\n",
227
- " <td>condo</td>\n",
228
  " <td>2023-09-30</td>\n",
229
  " <td>486974.735908</td>\n",
230
  " <td>NaN</td>\n",
@@ -238,7 +238,7 @@
238
  " <td>state</td>\n",
239
  " <td>nan</td>\n",
240
  " <td>All Bedrooms</td>\n",
241
- " <td>condo</td>\n",
242
  " <td>2023-10-31</td>\n",
243
  " <td>485847.539614</td>\n",
244
  " <td>NaN</td>\n",
@@ -252,7 +252,7 @@
252
  " <td>state</td>\n",
253
  " <td>nan</td>\n",
254
  " <td>All Bedrooms</td>\n",
255
- " <td>condo</td>\n",
256
  " <td>2023-11-30</td>\n",
257
  " <td>484223.885775</td>\n",
258
  " <td>NaN</td>\n",
@@ -266,7 +266,7 @@
266
  " <td>state</td>\n",
267
  " <td>nan</td>\n",
268
  " <td>All Bedrooms</td>\n",
269
- " <td>condo</td>\n",
270
  " <td>2023-12-31</td>\n",
271
  " <td>481522.403338</td>\n",
272
  " <td>NaN</td>\n",
@@ -280,7 +280,7 @@
280
  " <td>state</td>\n",
281
  " <td>nan</td>\n",
282
  " <td>All Bedrooms</td>\n",
283
- " <td>condo</td>\n",
284
  " <td>2024-01-31</td>\n",
285
  " <td>481181.718200</td>\n",
286
  " <td>NaN</td>\n",
@@ -305,18 +305,18 @@
305
  "117910 62 51 Wyoming state nan All Bedrooms \n",
306
  "117911 62 51 Wyoming state nan All Bedrooms \n",
307
  "\n",
308
- " Home Type Date \\\n",
309
- "0 all homes (SFR/condo) 2000-01-31 \n",
310
- "1 all homes (SFR/condo) 2000-02-29 \n",
311
- "2 all homes (SFR/condo) 2000-03-31 \n",
312
- "3 all homes (SFR/condo) 2000-04-30 \n",
313
- "4 all homes (SFR/condo) 2000-05-31 \n",
314
- "... ... ... \n",
315
- "117907 condo 2023-09-30 \n",
316
- "117908 condo 2023-10-31 \n",
317
- "117909 condo 2023-11-30 \n",
318
- "117910 condo 2023-12-31 \n",
319
- "117911 condo 2024-01-31 \n",
320
  "\n",
321
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
322
  "0 81310.639504 \n",
@@ -360,7 +360,7 @@
360
  "[117912 rows x 11 columns]"
361
  ]
362
  },
363
- "execution_count": 5,
364
  "metadata": {},
365
  "output_type": "execute_result"
366
  }
@@ -466,7 +466,7 @@
466
  },
467
  {
468
  "cell_type": "code",
469
- "execution_count": 11,
470
  "metadata": {},
471
  "outputs": [
472
  {
@@ -499,15 +499,8 @@
499
  " <th>Home Type</th>\n",
500
  " <th>Date</th>\n",
501
  " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
502
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1</th>\n",
503
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2</th>\n",
504
  " <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
505
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4</th>\n",
506
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5</th>\n",
507
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6</th>\n",
508
  " <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
509
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8</th>\n",
510
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9</th>\n",
511
  " </tr>\n",
512
  " </thead>\n",
513
  " <tbody>\n",
@@ -518,19 +511,12 @@
518
  " <td>Alaska</td>\n",
519
  " <td>state</td>\n",
520
  " <td>Alaska</td>\n",
521
- " <td>1-Bedrooms</td>\n",
522
- " <td>all homes (SFR/condo)</td>\n",
523
  " <td>2000-01-31</td>\n",
 
524
  " <td>NaN</td>\n",
525
  " <td>NaN</td>\n",
526
- " <td>NaN</td>\n",
527
- " <td>NaN</td>\n",
528
- " <td>NaN</td>\n",
529
- " <td>NaN</td>\n",
530
- " <td>NaN</td>\n",
531
- " <td>NaN</td>\n",
532
- " <td>NaN</td>\n",
533
- " <td>81310.639504</td>\n",
534
  " </tr>\n",
535
  " <tr>\n",
536
  " <th>1</th>\n",
@@ -539,19 +525,12 @@
539
  " <td>Alaska</td>\n",
540
  " <td>state</td>\n",
541
  " <td>Alaska</td>\n",
542
- " <td>1-Bedrooms</td>\n",
543
- " <td>all homes (SFR/condo)</td>\n",
544
  " <td>2000-02-29</td>\n",
 
545
  " <td>NaN</td>\n",
546
  " <td>NaN</td>\n",
547
- " <td>NaN</td>\n",
548
- " <td>NaN</td>\n",
549
- " <td>NaN</td>\n",
550
- " <td>NaN</td>\n",
551
- " <td>NaN</td>\n",
552
- " <td>NaN</td>\n",
553
- " <td>NaN</td>\n",
554
- " <td>80419.761984</td>\n",
555
  " </tr>\n",
556
  " <tr>\n",
557
  " <th>2</th>\n",
@@ -560,19 +539,12 @@
560
  " <td>Alaska</td>\n",
561
  " <td>state</td>\n",
562
  " <td>Alaska</td>\n",
563
- " <td>1-Bedrooms</td>\n",
564
- " <td>all homes (SFR/condo)</td>\n",
565
  " <td>2000-03-31</td>\n",
 
566
  " <td>NaN</td>\n",
567
  " <td>NaN</td>\n",
568
- " <td>NaN</td>\n",
569
- " <td>NaN</td>\n",
570
- " <td>NaN</td>\n",
571
- " <td>NaN</td>\n",
572
- " <td>NaN</td>\n",
573
- " <td>NaN</td>\n",
574
- " <td>NaN</td>\n",
575
- " <td>80480.449461</td>\n",
576
  " </tr>\n",
577
  " <tr>\n",
578
  " <th>3</th>\n",
@@ -581,19 +553,12 @@
581
  " <td>Alaska</td>\n",
582
  " <td>state</td>\n",
583
  " <td>Alaska</td>\n",
584
- " <td>1-Bedrooms</td>\n",
585
- " <td>all homes (SFR/condo)</td>\n",
586
  " <td>2000-04-30</td>\n",
 
587
  " <td>NaN</td>\n",
588
  " <td>NaN</td>\n",
589
- " <td>NaN</td>\n",
590
- " <td>NaN</td>\n",
591
- " <td>NaN</td>\n",
592
- " <td>NaN</td>\n",
593
- " <td>NaN</td>\n",
594
- " <td>NaN</td>\n",
595
- " <td>NaN</td>\n",
596
- " <td>79799.206525</td>\n",
597
  " </tr>\n",
598
  " <tr>\n",
599
  " <th>4</th>\n",
@@ -602,19 +567,12 @@
602
  " <td>Alaska</td>\n",
603
  " <td>state</td>\n",
604
  " <td>Alaska</td>\n",
605
- " <td>1-Bedrooms</td>\n",
606
- " <td>all homes (SFR/condo)</td>\n",
607
  " <td>2000-05-31</td>\n",
 
608
  " <td>NaN</td>\n",
609
  " <td>NaN</td>\n",
610
- " <td>NaN</td>\n",
611
- " <td>NaN</td>\n",
612
- " <td>NaN</td>\n",
613
- " <td>NaN</td>\n",
614
- " <td>NaN</td>\n",
615
- " <td>NaN</td>\n",
616
- " <td>NaN</td>\n",
617
- " <td>79666.469861</td>\n",
618
  " </tr>\n",
619
  " <tr>\n",
620
  " <th>...</th>\n",
@@ -629,13 +587,6 @@
629
  " <td>...</td>\n",
630
  " <td>...</td>\n",
631
  " <td>...</td>\n",
632
- " <td>...</td>\n",
633
- " <td>...</td>\n",
634
- " <td>...</td>\n",
635
- " <td>...</td>\n",
636
- " <td>...</td>\n",
637
- " <td>...</td>\n",
638
- " <td>...</td>\n",
639
  " </tr>\n",
640
  " <tr>\n",
641
  " <th>117907</th>\n",
@@ -645,18 +596,11 @@
645
  " <td>state</td>\n",
646
  " <td>Wyoming</td>\n",
647
  " <td>All Bedrooms</td>\n",
648
- " <td>condo</td>\n",
649
  " <td>2023-09-30</td>\n",
650
- " <td>NaN</td>\n",
651
- " <td>NaN</td>\n",
652
- " <td>NaN</td>\n",
653
- " <td>NaN</td>\n",
654
- " <td>NaN</td>\n",
655
  " <td>486974.735908</td>\n",
656
  " <td>NaN</td>\n",
657
  " <td>NaN</td>\n",
658
- " <td>NaN</td>\n",
659
- " <td>NaN</td>\n",
660
  " </tr>\n",
661
  " <tr>\n",
662
  " <th>117908</th>\n",
@@ -666,18 +610,11 @@
666
  " <td>state</td>\n",
667
  " <td>Wyoming</td>\n",
668
  " <td>All Bedrooms</td>\n",
669
- " <td>condo</td>\n",
670
  " <td>2023-10-31</td>\n",
671
- " <td>NaN</td>\n",
672
- " <td>NaN</td>\n",
673
- " <td>NaN</td>\n",
674
- " <td>NaN</td>\n",
675
- " <td>NaN</td>\n",
676
  " <td>485847.539614</td>\n",
677
  " <td>NaN</td>\n",
678
  " <td>NaN</td>\n",
679
- " <td>NaN</td>\n",
680
- " <td>NaN</td>\n",
681
  " </tr>\n",
682
  " <tr>\n",
683
  " <th>117909</th>\n",
@@ -687,18 +624,11 @@
687
  " <td>state</td>\n",
688
  " <td>Wyoming</td>\n",
689
  " <td>All Bedrooms</td>\n",
690
- " <td>condo</td>\n",
691
  " <td>2023-11-30</td>\n",
692
- " <td>NaN</td>\n",
693
- " <td>NaN</td>\n",
694
- " <td>NaN</td>\n",
695
- " <td>NaN</td>\n",
696
- " <td>NaN</td>\n",
697
  " <td>484223.885775</td>\n",
698
  " <td>NaN</td>\n",
699
  " <td>NaN</td>\n",
700
- " <td>NaN</td>\n",
701
- " <td>NaN</td>\n",
702
  " </tr>\n",
703
  " <tr>\n",
704
  " <th>117910</th>\n",
@@ -708,18 +638,11 @@
708
  " <td>state</td>\n",
709
  " <td>Wyoming</td>\n",
710
  " <td>All Bedrooms</td>\n",
711
- " <td>condo</td>\n",
712
  " <td>2023-12-31</td>\n",
713
- " <td>NaN</td>\n",
714
- " <td>NaN</td>\n",
715
- " <td>NaN</td>\n",
716
- " <td>NaN</td>\n",
717
- " <td>NaN</td>\n",
718
  " <td>481522.403338</td>\n",
719
  " <td>NaN</td>\n",
720
  " <td>NaN</td>\n",
721
- " <td>NaN</td>\n",
722
- " <td>NaN</td>\n",
723
  " </tr>\n",
724
  " <tr>\n",
725
  " <th>117911</th>\n",
@@ -729,31 +652,24 @@
729
  " <td>state</td>\n",
730
  " <td>Wyoming</td>\n",
731
  " <td>All Bedrooms</td>\n",
732
- " <td>condo</td>\n",
733
  " <td>2024-01-31</td>\n",
734
- " <td>NaN</td>\n",
735
- " <td>NaN</td>\n",
736
- " <td>NaN</td>\n",
737
- " <td>NaN</td>\n",
738
- " <td>NaN</td>\n",
739
  " <td>481181.718200</td>\n",
740
  " <td>NaN</td>\n",
741
  " <td>NaN</td>\n",
742
- " <td>NaN</td>\n",
743
- " <td>NaN</td>\n",
744
  " </tr>\n",
745
  " </tbody>\n",
746
  "</table>\n",
747
- "<p>117912 rows × 18 columns</p>\n",
748
  "</div>"
749
  ],
750
  "text/plain": [
751
  " RegionID SizeRank RegionName RegionType StateName Bedroom Count \\\n",
752
- "0 3 48 Alaska state Alaska 1-Bedrooms \n",
753
- "1 3 48 Alaska state Alaska 1-Bedrooms \n",
754
- "2 3 48 Alaska state Alaska 1-Bedrooms \n",
755
- "3 3 48 Alaska state Alaska 1-Bedrooms \n",
756
- "4 3 48 Alaska state Alaska 1-Bedrooms \n",
757
  "... ... ... ... ... ... ... \n",
758
  "117907 62 51 Wyoming state Wyoming All Bedrooms \n",
759
  "117908 62 51 Wyoming state Wyoming All Bedrooms \n",
@@ -761,57 +677,31 @@
761
  "117910 62 51 Wyoming state Wyoming All Bedrooms \n",
762
  "117911 62 51 Wyoming state Wyoming All Bedrooms \n",
763
  "\n",
764
- " Home Type Date \\\n",
765
- "0 all homes (SFR/condo) 2000-01-31 \n",
766
- "1 all homes (SFR/condo) 2000-02-29 \n",
767
- "2 all homes (SFR/condo) 2000-03-31 \n",
768
- "3 all homes (SFR/condo) 2000-04-30 \n",
769
- "4 all homes (SFR/condo) 2000-05-31 \n",
770
- "... ... ... \n",
771
- "117907 condo 2023-09-30 \n",
772
- "117908 condo 2023-10-31 \n",
773
- "117909 condo 2023-11-30 \n",
774
- "117910 condo 2023-12-31 \n",
775
- "117911 condo 2024-01-31 \n",
776
  "\n",
777
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
778
- "0 NaN \n",
779
- "1 NaN \n",
780
- "2 NaN \n",
781
- "3 NaN \n",
782
- "4 NaN \n",
783
  "... ... \n",
784
- "117907 NaN \n",
785
- "117908 NaN \n",
786
- "117909 NaN \n",
787
- "117910 NaN \n",
788
- "117911 NaN \n",
789
- "\n",
790
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1 \\\n",
791
- "0 NaN \n",
792
- "1 NaN \n",
793
- "2 NaN \n",
794
- "3 NaN \n",
795
- "4 NaN \n",
796
- "... ... \n",
797
- "117907 NaN \n",
798
- "117908 NaN \n",
799
- "117909 NaN \n",
800
- "117910 NaN \n",
801
- "117911 NaN \n",
802
- "\n",
803
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2 \\\n",
804
- "0 NaN \n",
805
- "1 NaN \n",
806
- "2 NaN \n",
807
- "3 NaN \n",
808
- "4 NaN \n",
809
- "... ... \n",
810
- "117907 NaN \n",
811
- "117908 NaN \n",
812
- "117909 NaN \n",
813
- "117910 NaN \n",
814
- "117911 NaN \n",
815
  "\n",
816
  " Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
817
  "0 NaN \n",
@@ -826,88 +716,23 @@
826
  "117910 NaN \n",
827
  "117911 NaN \n",
828
  "\n",
829
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4 \\\n",
830
- "0 NaN \n",
831
- "1 NaN \n",
832
- "2 NaN \n",
833
- "3 NaN \n",
834
- "4 NaN \n",
835
- "... ... \n",
836
- "117907 NaN \n",
837
- "117908 NaN \n",
838
- "117909 NaN \n",
839
- "117910 NaN \n",
840
- "117911 NaN \n",
841
- "\n",
842
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5 \\\n",
843
- "0 NaN \n",
844
- "1 NaN \n",
845
- "2 NaN \n",
846
- "3 NaN \n",
847
- "4 NaN \n",
848
- "... ... \n",
849
- "117907 486974.735908 \n",
850
- "117908 485847.539614 \n",
851
- "117909 484223.885775 \n",
852
- "117910 481522.403338 \n",
853
- "117911 481181.718200 \n",
854
- "\n",
855
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6 \\\n",
856
- "0 NaN \n",
857
- "1 NaN \n",
858
- "2 NaN \n",
859
- "3 NaN \n",
860
- "4 NaN \n",
861
- "... ... \n",
862
- "117907 NaN \n",
863
- "117908 NaN \n",
864
- "117909 NaN \n",
865
- "117910 NaN \n",
866
- "117911 NaN \n",
867
- "\n",
868
- " Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
869
- "0 NaN \n",
870
- "1 NaN \n",
871
- "2 NaN \n",
872
- "3 NaN \n",
873
- "4 NaN \n",
874
- "... ... \n",
875
- "117907 NaN \n",
876
- "117908 NaN \n",
877
- "117909 NaN \n",
878
- "117910 NaN \n",
879
- "117911 NaN \n",
880
- "\n",
881
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8 \\\n",
882
- "0 NaN \n",
883
- "1 NaN \n",
884
- "2 NaN \n",
885
- "3 NaN \n",
886
- "4 NaN \n",
887
- "... ... \n",
888
- "117907 NaN \n",
889
- "117908 NaN \n",
890
- "117909 NaN \n",
891
- "117910 NaN \n",
892
- "117911 NaN \n",
893
- "\n",
894
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9 \n",
895
- "0 81310.639504 \n",
896
- "1 80419.761984 \n",
897
- "2 80480.449461 \n",
898
- "3 79799.206525 \n",
899
- "4 79666.469861 \n",
900
- "... ... \n",
901
- "117907 NaN \n",
902
- "117908 NaN \n",
903
- "117909 NaN \n",
904
- "117910 NaN \n",
905
- "117911 NaN \n",
906
  "\n",
907
- "[117912 rows x 18 columns]"
908
  ]
909
  },
910
- "execution_count": 11,
911
  "metadata": {},
912
  "output_type": "execute_result"
913
  }
@@ -938,7 +763,7 @@
938
  },
939
  {
940
  "cell_type": "code",
941
- "execution_count": 12,
942
  "metadata": {},
943
  "outputs": [
944
  {
@@ -971,15 +796,8 @@
971
  " <th>Home Type</th>\n",
972
  " <th>Date</th>\n",
973
  " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
974
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1</th>\n",
975
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2</th>\n",
976
  " <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
977
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4</th>\n",
978
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5</th>\n",
979
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6</th>\n",
980
  " <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
981
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8</th>\n",
982
- " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9</th>\n",
983
  " </tr>\n",
984
  " </thead>\n",
985
  " <tbody>\n",
@@ -990,19 +808,12 @@
990
  " <td>Alaska</td>\n",
991
  " <td>state</td>\n",
992
  " <td>Alaska</td>\n",
993
- " <td>1-Bedrooms</td>\n",
994
- " <td>all homes (SFR/condo)</td>\n",
995
  " <td>2000-01-31</td>\n",
 
996
  " <td>NaN</td>\n",
997
  " <td>NaN</td>\n",
998
- " <td>NaN</td>\n",
999
- " <td>NaN</td>\n",
1000
- " <td>NaN</td>\n",
1001
- " <td>NaN</td>\n",
1002
- " <td>NaN</td>\n",
1003
- " <td>NaN</td>\n",
1004
- " <td>NaN</td>\n",
1005
- " <td>81310.639504</td>\n",
1006
  " </tr>\n",
1007
  " <tr>\n",
1008
  " <th>1</th>\n",
@@ -1011,19 +822,12 @@
1011
  " <td>Alaska</td>\n",
1012
  " <td>state</td>\n",
1013
  " <td>Alaska</td>\n",
1014
- " <td>1-Bedrooms</td>\n",
1015
- " <td>all homes (SFR/condo)</td>\n",
1016
  " <td>2000-02-29</td>\n",
 
1017
  " <td>NaN</td>\n",
1018
  " <td>NaN</td>\n",
1019
- " <td>NaN</td>\n",
1020
- " <td>NaN</td>\n",
1021
- " <td>NaN</td>\n",
1022
- " <td>NaN</td>\n",
1023
- " <td>NaN</td>\n",
1024
- " <td>NaN</td>\n",
1025
- " <td>NaN</td>\n",
1026
- " <td>80419.761984</td>\n",
1027
  " </tr>\n",
1028
  " <tr>\n",
1029
  " <th>2</th>\n",
@@ -1032,19 +836,12 @@
1032
  " <td>Alaska</td>\n",
1033
  " <td>state</td>\n",
1034
  " <td>Alaska</td>\n",
1035
- " <td>1-Bedrooms</td>\n",
1036
- " <td>all homes (SFR/condo)</td>\n",
1037
  " <td>2000-03-31</td>\n",
 
1038
  " <td>NaN</td>\n",
1039
  " <td>NaN</td>\n",
1040
- " <td>NaN</td>\n",
1041
- " <td>NaN</td>\n",
1042
- " <td>NaN</td>\n",
1043
- " <td>NaN</td>\n",
1044
- " <td>NaN</td>\n",
1045
- " <td>NaN</td>\n",
1046
- " <td>NaN</td>\n",
1047
- " <td>80480.449461</td>\n",
1048
  " </tr>\n",
1049
  " <tr>\n",
1050
  " <th>3</th>\n",
@@ -1053,19 +850,12 @@
1053
  " <td>Alaska</td>\n",
1054
  " <td>state</td>\n",
1055
  " <td>Alaska</td>\n",
1056
- " <td>1-Bedrooms</td>\n",
1057
- " <td>all homes (SFR/condo)</td>\n",
1058
  " <td>2000-04-30</td>\n",
 
1059
  " <td>NaN</td>\n",
1060
  " <td>NaN</td>\n",
1061
- " <td>NaN</td>\n",
1062
- " <td>NaN</td>\n",
1063
- " <td>NaN</td>\n",
1064
- " <td>NaN</td>\n",
1065
- " <td>NaN</td>\n",
1066
- " <td>NaN</td>\n",
1067
- " <td>NaN</td>\n",
1068
- " <td>79799.206525</td>\n",
1069
  " </tr>\n",
1070
  " <tr>\n",
1071
  " <th>4</th>\n",
@@ -1074,19 +864,12 @@
1074
  " <td>Alaska</td>\n",
1075
  " <td>state</td>\n",
1076
  " <td>Alaska</td>\n",
1077
- " <td>1-Bedrooms</td>\n",
1078
- " <td>all homes (SFR/condo)</td>\n",
1079
  " <td>2000-05-31</td>\n",
 
1080
  " <td>NaN</td>\n",
1081
  " <td>NaN</td>\n",
1082
- " <td>NaN</td>\n",
1083
- " <td>NaN</td>\n",
1084
- " <td>NaN</td>\n",
1085
- " <td>NaN</td>\n",
1086
- " <td>NaN</td>\n",
1087
- " <td>NaN</td>\n",
1088
- " <td>NaN</td>\n",
1089
- " <td>79666.469861</td>\n",
1090
  " </tr>\n",
1091
  " <tr>\n",
1092
  " <th>...</th>\n",
@@ -1101,13 +884,6 @@
1101
  " <td>...</td>\n",
1102
  " <td>...</td>\n",
1103
  " <td>...</td>\n",
1104
- " <td>...</td>\n",
1105
- " <td>...</td>\n",
1106
- " <td>...</td>\n",
1107
- " <td>...</td>\n",
1108
- " <td>...</td>\n",
1109
- " <td>...</td>\n",
1110
- " <td>...</td>\n",
1111
  " </tr>\n",
1112
  " <tr>\n",
1113
  " <th>117907</th>\n",
@@ -1117,18 +893,11 @@
1117
  " <td>state</td>\n",
1118
  " <td>Wyoming</td>\n",
1119
  " <td>All Bedrooms</td>\n",
1120
- " <td>condo</td>\n",
1121
  " <td>2023-09-30</td>\n",
1122
- " <td>NaN</td>\n",
1123
- " <td>NaN</td>\n",
1124
- " <td>NaN</td>\n",
1125
- " <td>NaN</td>\n",
1126
- " <td>NaN</td>\n",
1127
  " <td>486974.735908</td>\n",
1128
  " <td>NaN</td>\n",
1129
  " <td>NaN</td>\n",
1130
- " <td>NaN</td>\n",
1131
- " <td>NaN</td>\n",
1132
  " </tr>\n",
1133
  " <tr>\n",
1134
  " <th>117908</th>\n",
@@ -1138,18 +907,11 @@
1138
  " <td>state</td>\n",
1139
  " <td>Wyoming</td>\n",
1140
  " <td>All Bedrooms</td>\n",
1141
- " <td>condo</td>\n",
1142
  " <td>2023-10-31</td>\n",
1143
- " <td>NaN</td>\n",
1144
- " <td>NaN</td>\n",
1145
- " <td>NaN</td>\n",
1146
- " <td>NaN</td>\n",
1147
- " <td>NaN</td>\n",
1148
  " <td>485847.539614</td>\n",
1149
  " <td>NaN</td>\n",
1150
  " <td>NaN</td>\n",
1151
- " <td>NaN</td>\n",
1152
- " <td>NaN</td>\n",
1153
  " </tr>\n",
1154
  " <tr>\n",
1155
  " <th>117909</th>\n",
@@ -1159,18 +921,11 @@
1159
  " <td>state</td>\n",
1160
  " <td>Wyoming</td>\n",
1161
  " <td>All Bedrooms</td>\n",
1162
- " <td>condo</td>\n",
1163
  " <td>2023-11-30</td>\n",
1164
- " <td>NaN</td>\n",
1165
- " <td>NaN</td>\n",
1166
- " <td>NaN</td>\n",
1167
- " <td>NaN</td>\n",
1168
- " <td>NaN</td>\n",
1169
  " <td>484223.885775</td>\n",
1170
  " <td>NaN</td>\n",
1171
  " <td>NaN</td>\n",
1172
- " <td>NaN</td>\n",
1173
- " <td>NaN</td>\n",
1174
  " </tr>\n",
1175
  " <tr>\n",
1176
  " <th>117910</th>\n",
@@ -1180,18 +935,11 @@
1180
  " <td>state</td>\n",
1181
  " <td>Wyoming</td>\n",
1182
  " <td>All Bedrooms</td>\n",
1183
- " <td>condo</td>\n",
1184
  " <td>2023-12-31</td>\n",
1185
- " <td>NaN</td>\n",
1186
- " <td>NaN</td>\n",
1187
- " <td>NaN</td>\n",
1188
- " <td>NaN</td>\n",
1189
- " <td>NaN</td>\n",
1190
  " <td>481522.403338</td>\n",
1191
  " <td>NaN</td>\n",
1192
  " <td>NaN</td>\n",
1193
- " <td>NaN</td>\n",
1194
- " <td>NaN</td>\n",
1195
  " </tr>\n",
1196
  " <tr>\n",
1197
  " <th>117911</th>\n",
@@ -1201,31 +949,24 @@
1201
  " <td>state</td>\n",
1202
  " <td>Wyoming</td>\n",
1203
  " <td>All Bedrooms</td>\n",
1204
- " <td>condo</td>\n",
1205
  " <td>2024-01-31</td>\n",
1206
- " <td>NaN</td>\n",
1207
- " <td>NaN</td>\n",
1208
- " <td>NaN</td>\n",
1209
- " <td>NaN</td>\n",
1210
- " <td>NaN</td>\n",
1211
  " <td>481181.718200</td>\n",
1212
  " <td>NaN</td>\n",
1213
  " <td>NaN</td>\n",
1214
- " <td>NaN</td>\n",
1215
- " <td>NaN</td>\n",
1216
  " </tr>\n",
1217
  " </tbody>\n",
1218
  "</table>\n",
1219
- "<p>117912 rows × 18 columns</p>\n",
1220
  "</div>"
1221
  ],
1222
  "text/plain": [
1223
  " Region ID Size Rank Region Region Type State Bedroom Count \\\n",
1224
- "0 3 48 Alaska state Alaska 1-Bedrooms \n",
1225
- "1 3 48 Alaska state Alaska 1-Bedrooms \n",
1226
- "2 3 48 Alaska state Alaska 1-Bedrooms \n",
1227
- "3 3 48 Alaska state Alaska 1-Bedrooms \n",
1228
- "4 3 48 Alaska state Alaska 1-Bedrooms \n",
1229
  "... ... ... ... ... ... ... \n",
1230
  "117907 62 51 Wyoming state Wyoming All Bedrooms \n",
1231
  "117908 62 51 Wyoming state Wyoming All Bedrooms \n",
@@ -1233,57 +974,31 @@
1233
  "117910 62 51 Wyoming state Wyoming All Bedrooms \n",
1234
  "117911 62 51 Wyoming state Wyoming All Bedrooms \n",
1235
  "\n",
1236
- " Home Type Date \\\n",
1237
- "0 all homes (SFR/condo) 2000-01-31 \n",
1238
- "1 all homes (SFR/condo) 2000-02-29 \n",
1239
- "2 all homes (SFR/condo) 2000-03-31 \n",
1240
- "3 all homes (SFR/condo) 2000-04-30 \n",
1241
- "4 all homes (SFR/condo) 2000-05-31 \n",
1242
- "... ... ... \n",
1243
- "117907 condo 2023-09-30 \n",
1244
- "117908 condo 2023-10-31 \n",
1245
- "117909 condo 2023-11-30 \n",
1246
- "117910 condo 2023-12-31 \n",
1247
- "117911 condo 2024-01-31 \n",
1248
  "\n",
1249
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
1250
- "0 NaN \n",
1251
- "1 NaN \n",
1252
- "2 NaN \n",
1253
- "3 NaN \n",
1254
- "4 NaN \n",
1255
  "... ... \n",
1256
- "117907 NaN \n",
1257
- "117908 NaN \n",
1258
- "117909 NaN \n",
1259
- "117910 NaN \n",
1260
- "117911 NaN \n",
1261
- "\n",
1262
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_1 \\\n",
1263
- "0 NaN \n",
1264
- "1 NaN \n",
1265
- "2 NaN \n",
1266
- "3 NaN \n",
1267
- "4 NaN \n",
1268
- "... ... \n",
1269
- "117907 NaN \n",
1270
- "117908 NaN \n",
1271
- "117909 NaN \n",
1272
- "117910 NaN \n",
1273
- "117911 NaN \n",
1274
- "\n",
1275
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_2 \\\n",
1276
- "0 NaN \n",
1277
- "1 NaN \n",
1278
- "2 NaN \n",
1279
- "3 NaN \n",
1280
- "4 NaN \n",
1281
- "... ... \n",
1282
- "117907 NaN \n",
1283
- "117908 NaN \n",
1284
- "117909 NaN \n",
1285
- "117910 NaN \n",
1286
- "117911 NaN \n",
1287
  "\n",
1288
  " Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
1289
  "0 NaN \n",
@@ -1298,88 +1013,23 @@
1298
  "117910 NaN \n",
1299
  "117911 NaN \n",
1300
  "\n",
1301
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_4 \\\n",
1302
- "0 NaN \n",
1303
- "1 NaN \n",
1304
- "2 NaN \n",
1305
- "3 NaN \n",
1306
- "4 NaN \n",
1307
- "... ... \n",
1308
- "117907 NaN \n",
1309
- "117908 NaN \n",
1310
- "117909 NaN \n",
1311
- "117910 NaN \n",
1312
- "117911 NaN \n",
1313
- "\n",
1314
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_5 \\\n",
1315
- "0 NaN \n",
1316
- "1 NaN \n",
1317
- "2 NaN \n",
1318
- "3 NaN \n",
1319
- "4 NaN \n",
1320
- "... ... \n",
1321
- "117907 486974.735908 \n",
1322
- "117908 485847.539614 \n",
1323
- "117909 484223.885775 \n",
1324
- "117910 481522.403338 \n",
1325
- "117911 481181.718200 \n",
1326
- "\n",
1327
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_6 \\\n",
1328
- "0 NaN \n",
1329
- "1 NaN \n",
1330
- "2 NaN \n",
1331
- "3 NaN \n",
1332
- "4 NaN \n",
1333
- "... ... \n",
1334
- "117907 NaN \n",
1335
- "117908 NaN \n",
1336
- "117909 NaN \n",
1337
- "117910 NaN \n",
1338
- "117911 NaN \n",
1339
- "\n",
1340
- " Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
1341
- "0 NaN \n",
1342
- "1 NaN \n",
1343
- "2 NaN \n",
1344
- "3 NaN \n",
1345
- "4 NaN \n",
1346
- "... ... \n",
1347
- "117907 NaN \n",
1348
- "117908 NaN \n",
1349
- "117909 NaN \n",
1350
- "117910 NaN \n",
1351
- "117911 NaN \n",
1352
- "\n",
1353
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_8 \\\n",
1354
- "0 NaN \n",
1355
- "1 NaN \n",
1356
- "2 NaN \n",
1357
- "3 NaN \n",
1358
- "4 NaN \n",
1359
- "... ... \n",
1360
- "117907 NaN \n",
1361
- "117908 NaN \n",
1362
- "117909 NaN \n",
1363
- "117910 NaN \n",
1364
- "117911 NaN \n",
1365
- "\n",
1366
- " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)_9 \n",
1367
- "0 81310.639504 \n",
1368
- "1 80419.761984 \n",
1369
- "2 80480.449461 \n",
1370
- "3 79799.206525 \n",
1371
- "4 79666.469861 \n",
1372
- "... ... \n",
1373
- "117907 NaN \n",
1374
- "117908 NaN \n",
1375
- "117909 NaN \n",
1376
- "117910 NaN \n",
1377
- "117911 NaN \n",
1378
  "\n",
1379
- "[117912 rows x 18 columns]"
1380
  ]
1381
  },
1382
- "execution_count": 12,
1383
  "metadata": {},
1384
  "output_type": "execute_result"
1385
  }
 
32
  },
33
  {
34
  "cell_type": "code",
35
+ "execution_count": 3,
36
  "metadata": {},
37
  "outputs": [
38
  {
 
140
  " <td>state</td>\n",
141
  " <td>nan</td>\n",
142
  " <td>1-Bedroom</td>\n",
143
+ " <td>all homes</td>\n",
144
  " <td>2000-01-31</td>\n",
145
  " <td>81310.639504</td>\n",
146
  " <td>NaN</td>\n",
 
154
  " <td>state</td>\n",
155
  " <td>nan</td>\n",
156
  " <td>1-Bedroom</td>\n",
157
+ " <td>all homes</td>\n",
158
  " <td>2000-02-29</td>\n",
159
  " <td>80419.761984</td>\n",
160
  " <td>NaN</td>\n",
 
168
  " <td>state</td>\n",
169
  " <td>nan</td>\n",
170
  " <td>1-Bedroom</td>\n",
171
+ " <td>all homes</td>\n",
172
  " <td>2000-03-31</td>\n",
173
  " <td>80480.449461</td>\n",
174
  " <td>NaN</td>\n",
 
182
  " <td>state</td>\n",
183
  " <td>nan</td>\n",
184
  " <td>1-Bedroom</td>\n",
185
+ " <td>all homes</td>\n",
186
  " <td>2000-04-30</td>\n",
187
  " <td>79799.206525</td>\n",
188
  " <td>NaN</td>\n",
 
196
  " <td>state</td>\n",
197
  " <td>nan</td>\n",
198
  " <td>1-Bedroom</td>\n",
199
+ " <td>all homes</td>\n",
200
  " <td>2000-05-31</td>\n",
201
  " <td>79666.469861</td>\n",
202
  " <td>NaN</td>\n",
 
224
  " <td>state</td>\n",
225
  " <td>nan</td>\n",
226
  " <td>All Bedrooms</td>\n",
227
+ " <td>condo/co-op</td>\n",
228
  " <td>2023-09-30</td>\n",
229
  " <td>486974.735908</td>\n",
230
  " <td>NaN</td>\n",
 
238
  " <td>state</td>\n",
239
  " <td>nan</td>\n",
240
  " <td>All Bedrooms</td>\n",
241
+ " <td>condo/co-op</td>\n",
242
  " <td>2023-10-31</td>\n",
243
  " <td>485847.539614</td>\n",
244
  " <td>NaN</td>\n",
 
252
  " <td>state</td>\n",
253
  " <td>nan</td>\n",
254
  " <td>All Bedrooms</td>\n",
255
+ " <td>condo/co-op</td>\n",
256
  " <td>2023-11-30</td>\n",
257
  " <td>484223.885775</td>\n",
258
  " <td>NaN</td>\n",
 
266
  " <td>state</td>\n",
267
  " <td>nan</td>\n",
268
  " <td>All Bedrooms</td>\n",
269
+ " <td>condo/co-op</td>\n",
270
  " <td>2023-12-31</td>\n",
271
  " <td>481522.403338</td>\n",
272
  " <td>NaN</td>\n",
 
280
  " <td>state</td>\n",
281
  " <td>nan</td>\n",
282
  " <td>All Bedrooms</td>\n",
283
+ " <td>condo/co-op</td>\n",
284
  " <td>2024-01-31</td>\n",
285
  " <td>481181.718200</td>\n",
286
  " <td>NaN</td>\n",
 
305
  "117910 62 51 Wyoming state nan All Bedrooms \n",
306
  "117911 62 51 Wyoming state nan All Bedrooms \n",
307
  "\n",
308
+ " Home Type Date \\\n",
309
+ "0 all homes 2000-01-31 \n",
310
+ "1 all homes 2000-02-29 \n",
311
+ "2 all homes 2000-03-31 \n",
312
+ "3 all homes 2000-04-30 \n",
313
+ "4 all homes 2000-05-31 \n",
314
+ "... ... ... \n",
315
+ "117907 condo/co-op 2023-09-30 \n",
316
+ "117908 condo/co-op 2023-10-31 \n",
317
+ "117909 condo/co-op 2023-11-30 \n",
318
+ "117910 condo/co-op 2023-12-31 \n",
319
+ "117911 condo/co-op 2024-01-31 \n",
320
  "\n",
321
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
322
  "0 81310.639504 \n",
 
360
  "[117912 rows x 11 columns]"
361
  ]
362
  },
363
+ "execution_count": 3,
364
  "metadata": {},
365
  "output_type": "execute_result"
366
  }
 
466
  },
467
  {
468
  "cell_type": "code",
469
+ "execution_count": 4,
470
  "metadata": {},
471
  "outputs": [
472
  {
 
499
  " <th>Home Type</th>\n",
500
  " <th>Date</th>\n",
501
  " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
 
 
502
  " <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
 
 
 
503
  " <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
 
 
504
  " </tr>\n",
505
  " </thead>\n",
506
  " <tbody>\n",
 
511
  " <td>Alaska</td>\n",
512
  " <td>state</td>\n",
513
  " <td>Alaska</td>\n",
514
+ " <td>1-Bedroom</td>\n",
515
+ " <td>all homes</td>\n",
516
  " <td>2000-01-31</td>\n",
517
+ " <td>81310.639504</td>\n",
518
  " <td>NaN</td>\n",
519
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
520
  " </tr>\n",
521
  " <tr>\n",
522
  " <th>1</th>\n",
 
525
  " <td>Alaska</td>\n",
526
  " <td>state</td>\n",
527
  " <td>Alaska</td>\n",
528
+ " <td>1-Bedroom</td>\n",
529
+ " <td>all homes</td>\n",
530
  " <td>2000-02-29</td>\n",
531
+ " <td>80419.761984</td>\n",
532
  " <td>NaN</td>\n",
533
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
534
  " </tr>\n",
535
  " <tr>\n",
536
  " <th>2</th>\n",
 
539
  " <td>Alaska</td>\n",
540
  " <td>state</td>\n",
541
  " <td>Alaska</td>\n",
542
+ " <td>1-Bedroom</td>\n",
543
+ " <td>all homes</td>\n",
544
  " <td>2000-03-31</td>\n",
545
+ " <td>80480.449461</td>\n",
546
  " <td>NaN</td>\n",
547
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
548
  " </tr>\n",
549
  " <tr>\n",
550
  " <th>3</th>\n",
 
553
  " <td>Alaska</td>\n",
554
  " <td>state</td>\n",
555
  " <td>Alaska</td>\n",
556
+ " <td>1-Bedroom</td>\n",
557
+ " <td>all homes</td>\n",
558
  " <td>2000-04-30</td>\n",
559
+ " <td>79799.206525</td>\n",
560
  " <td>NaN</td>\n",
561
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
562
  " </tr>\n",
563
  " <tr>\n",
564
  " <th>4</th>\n",
 
567
  " <td>Alaska</td>\n",
568
  " <td>state</td>\n",
569
  " <td>Alaska</td>\n",
570
+ " <td>1-Bedroom</td>\n",
571
+ " <td>all homes</td>\n",
572
  " <td>2000-05-31</td>\n",
573
+ " <td>79666.469861</td>\n",
574
  " <td>NaN</td>\n",
575
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
576
  " </tr>\n",
577
  " <tr>\n",
578
  " <th>...</th>\n",
 
587
  " <td>...</td>\n",
588
  " <td>...</td>\n",
589
  " <td>...</td>\n",
 
 
 
 
 
 
 
590
  " </tr>\n",
591
  " <tr>\n",
592
  " <th>117907</th>\n",
 
596
  " <td>state</td>\n",
597
  " <td>Wyoming</td>\n",
598
  " <td>All Bedrooms</td>\n",
599
+ " <td>condo/co-op</td>\n",
600
  " <td>2023-09-30</td>\n",
 
 
 
 
 
601
  " <td>486974.735908</td>\n",
602
  " <td>NaN</td>\n",
603
  " <td>NaN</td>\n",
 
 
604
  " </tr>\n",
605
  " <tr>\n",
606
  " <th>117908</th>\n",
 
610
  " <td>state</td>\n",
611
  " <td>Wyoming</td>\n",
612
  " <td>All Bedrooms</td>\n",
613
+ " <td>condo/co-op</td>\n",
614
  " <td>2023-10-31</td>\n",
 
 
 
 
 
615
  " <td>485847.539614</td>\n",
616
  " <td>NaN</td>\n",
617
  " <td>NaN</td>\n",
 
 
618
  " </tr>\n",
619
  " <tr>\n",
620
  " <th>117909</th>\n",
 
624
  " <td>state</td>\n",
625
  " <td>Wyoming</td>\n",
626
  " <td>All Bedrooms</td>\n",
627
+ " <td>condo/co-op</td>\n",
628
  " <td>2023-11-30</td>\n",
 
 
 
 
 
629
  " <td>484223.885775</td>\n",
630
  " <td>NaN</td>\n",
631
  " <td>NaN</td>\n",
 
 
632
  " </tr>\n",
633
  " <tr>\n",
634
  " <th>117910</th>\n",
 
638
  " <td>state</td>\n",
639
  " <td>Wyoming</td>\n",
640
  " <td>All Bedrooms</td>\n",
641
+ " <td>condo/co-op</td>\n",
642
  " <td>2023-12-31</td>\n",
 
 
 
 
 
643
  " <td>481522.403338</td>\n",
644
  " <td>NaN</td>\n",
645
  " <td>NaN</td>\n",
 
 
646
  " </tr>\n",
647
  " <tr>\n",
648
  " <th>117911</th>\n",
 
652
  " <td>state</td>\n",
653
  " <td>Wyoming</td>\n",
654
  " <td>All Bedrooms</td>\n",
655
+ " <td>condo/co-op</td>\n",
656
  " <td>2024-01-31</td>\n",
 
 
 
 
 
657
  " <td>481181.718200</td>\n",
658
  " <td>NaN</td>\n",
659
  " <td>NaN</td>\n",
 
 
660
  " </tr>\n",
661
  " </tbody>\n",
662
  "</table>\n",
663
+ "<p>117912 rows × 11 columns</p>\n",
664
  "</div>"
665
  ],
666
  "text/plain": [
667
  " RegionID SizeRank RegionName RegionType StateName Bedroom Count \\\n",
668
+ "0 3 48 Alaska state Alaska 1-Bedroom \n",
669
+ "1 3 48 Alaska state Alaska 1-Bedroom \n",
670
+ "2 3 48 Alaska state Alaska 1-Bedroom \n",
671
+ "3 3 48 Alaska state Alaska 1-Bedroom \n",
672
+ "4 3 48 Alaska state Alaska 1-Bedroom \n",
673
  "... ... ... ... ... ... ... \n",
674
  "117907 62 51 Wyoming state Wyoming All Bedrooms \n",
675
  "117908 62 51 Wyoming state Wyoming All Bedrooms \n",
 
677
  "117910 62 51 Wyoming state Wyoming All Bedrooms \n",
678
  "117911 62 51 Wyoming state Wyoming All Bedrooms \n",
679
  "\n",
680
+ " Home Type Date \\\n",
681
+ "0 all homes 2000-01-31 \n",
682
+ "1 all homes 2000-02-29 \n",
683
+ "2 all homes 2000-03-31 \n",
684
+ "3 all homes 2000-04-30 \n",
685
+ "4 all homes 2000-05-31 \n",
686
+ "... ... ... \n",
687
+ "117907 condo/co-op 2023-09-30 \n",
688
+ "117908 condo/co-op 2023-10-31 \n",
689
+ "117909 condo/co-op 2023-11-30 \n",
690
+ "117910 condo/co-op 2023-12-31 \n",
691
+ "117911 condo/co-op 2024-01-31 \n",
692
  "\n",
693
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
694
+ "0 81310.639504 \n",
695
+ "1 80419.761984 \n",
696
+ "2 80480.449461 \n",
697
+ "3 79799.206525 \n",
698
+ "4 79666.469861 \n",
699
  "... ... \n",
700
+ "117907 486974.735908 \n",
701
+ "117908 485847.539614 \n",
702
+ "117909 484223.885775 \n",
703
+ "117910 481522.403338 \n",
704
+ "117911 481181.718200 \n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
705
  "\n",
706
  " Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
707
  "0 NaN \n",
 
716
  "117910 NaN \n",
717
  "117911 NaN \n",
718
  "\n",
719
+ " Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \n",
720
+ "0 NaN \n",
721
+ "1 NaN \n",
722
+ "2 NaN \n",
723
+ "3 NaN \n",
724
+ "4 NaN \n",
725
+ "... ... \n",
726
+ "117907 NaN \n",
727
+ "117908 NaN \n",
728
+ "117909 NaN \n",
729
+ "117910 NaN \n",
730
+ "117911 NaN \n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
731
  "\n",
732
+ "[117912 rows x 11 columns]"
733
  ]
734
  },
735
+ "execution_count": 4,
736
  "metadata": {},
737
  "output_type": "execute_result"
738
  }
 
763
  },
764
  {
765
  "cell_type": "code",
766
+ "execution_count": 5,
767
  "metadata": {},
768
  "outputs": [
769
  {
 
796
  " <th>Home Type</th>\n",
797
  " <th>Date</th>\n",
798
  " <th>Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
 
 
799
  " <th>Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
 
 
 
800
  " <th>Top Tier ZHVI (Smoothed) (Seasonally Adjusted)</th>\n",
 
 
801
  " </tr>\n",
802
  " </thead>\n",
803
  " <tbody>\n",
 
808
  " <td>Alaska</td>\n",
809
  " <td>state</td>\n",
810
  " <td>Alaska</td>\n",
811
+ " <td>1-Bedroom</td>\n",
812
+ " <td>all homes</td>\n",
813
  " <td>2000-01-31</td>\n",
814
+ " <td>81310.639504</td>\n",
815
  " <td>NaN</td>\n",
816
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
817
  " </tr>\n",
818
  " <tr>\n",
819
  " <th>1</th>\n",
 
822
  " <td>Alaska</td>\n",
823
  " <td>state</td>\n",
824
  " <td>Alaska</td>\n",
825
+ " <td>1-Bedroom</td>\n",
826
+ " <td>all homes</td>\n",
827
  " <td>2000-02-29</td>\n",
828
+ " <td>80419.761984</td>\n",
829
  " <td>NaN</td>\n",
830
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
831
  " </tr>\n",
832
  " <tr>\n",
833
  " <th>2</th>\n",
 
836
  " <td>Alaska</td>\n",
837
  " <td>state</td>\n",
838
  " <td>Alaska</td>\n",
839
+ " <td>1-Bedroom</td>\n",
840
+ " <td>all homes</td>\n",
841
  " <td>2000-03-31</td>\n",
842
+ " <td>80480.449461</td>\n",
843
  " <td>NaN</td>\n",
844
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
845
  " </tr>\n",
846
  " <tr>\n",
847
  " <th>3</th>\n",
 
850
  " <td>Alaska</td>\n",
851
  " <td>state</td>\n",
852
  " <td>Alaska</td>\n",
853
+ " <td>1-Bedroom</td>\n",
854
+ " <td>all homes</td>\n",
855
  " <td>2000-04-30</td>\n",
856
+ " <td>79799.206525</td>\n",
857
  " <td>NaN</td>\n",
858
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
859
  " </tr>\n",
860
  " <tr>\n",
861
  " <th>4</th>\n",
 
864
  " <td>Alaska</td>\n",
865
  " <td>state</td>\n",
866
  " <td>Alaska</td>\n",
867
+ " <td>1-Bedroom</td>\n",
868
+ " <td>all homes</td>\n",
869
  " <td>2000-05-31</td>\n",
870
+ " <td>79666.469861</td>\n",
871
  " <td>NaN</td>\n",
872
  " <td>NaN</td>\n",
 
 
 
 
 
 
 
 
873
  " </tr>\n",
874
  " <tr>\n",
875
  " <th>...</th>\n",
 
884
  " <td>...</td>\n",
885
  " <td>...</td>\n",
886
  " <td>...</td>\n",
 
 
 
 
 
 
 
887
  " </tr>\n",
888
  " <tr>\n",
889
  " <th>117907</th>\n",
 
893
  " <td>state</td>\n",
894
  " <td>Wyoming</td>\n",
895
  " <td>All Bedrooms</td>\n",
896
+ " <td>condo/co-op</td>\n",
897
  " <td>2023-09-30</td>\n",
 
 
 
 
 
898
  " <td>486974.735908</td>\n",
899
  " <td>NaN</td>\n",
900
  " <td>NaN</td>\n",
 
 
901
  " </tr>\n",
902
  " <tr>\n",
903
  " <th>117908</th>\n",
 
907
  " <td>state</td>\n",
908
  " <td>Wyoming</td>\n",
909
  " <td>All Bedrooms</td>\n",
910
+ " <td>condo/co-op</td>\n",
911
  " <td>2023-10-31</td>\n",
 
 
 
 
 
912
  " <td>485847.539614</td>\n",
913
  " <td>NaN</td>\n",
914
  " <td>NaN</td>\n",
 
 
915
  " </tr>\n",
916
  " <tr>\n",
917
  " <th>117909</th>\n",
 
921
  " <td>state</td>\n",
922
  " <td>Wyoming</td>\n",
923
  " <td>All Bedrooms</td>\n",
924
+ " <td>condo/co-op</td>\n",
925
  " <td>2023-11-30</td>\n",
 
 
 
 
 
926
  " <td>484223.885775</td>\n",
927
  " <td>NaN</td>\n",
928
  " <td>NaN</td>\n",
 
 
929
  " </tr>\n",
930
  " <tr>\n",
931
  " <th>117910</th>\n",
 
935
  " <td>state</td>\n",
936
  " <td>Wyoming</td>\n",
937
  " <td>All Bedrooms</td>\n",
938
+ " <td>condo/co-op</td>\n",
939
  " <td>2023-12-31</td>\n",
 
 
 
 
 
940
  " <td>481522.403338</td>\n",
941
  " <td>NaN</td>\n",
942
  " <td>NaN</td>\n",
 
 
943
  " </tr>\n",
944
  " <tr>\n",
945
  " <th>117911</th>\n",
 
949
  " <td>state</td>\n",
950
  " <td>Wyoming</td>\n",
951
  " <td>All Bedrooms</td>\n",
952
+ " <td>condo/co-op</td>\n",
953
  " <td>2024-01-31</td>\n",
 
 
 
 
 
954
  " <td>481181.718200</td>\n",
955
  " <td>NaN</td>\n",
956
  " <td>NaN</td>\n",
 
 
957
  " </tr>\n",
958
  " </tbody>\n",
959
  "</table>\n",
960
+ "<p>117912 rows × 11 columns</p>\n",
961
  "</div>"
962
  ],
963
  "text/plain": [
964
  " Region ID Size Rank Region Region Type State Bedroom Count \\\n",
965
+ "0 3 48 Alaska state Alaska 1-Bedroom \n",
966
+ "1 3 48 Alaska state Alaska 1-Bedroom \n",
967
+ "2 3 48 Alaska state Alaska 1-Bedroom \n",
968
+ "3 3 48 Alaska state Alaska 1-Bedroom \n",
969
+ "4 3 48 Alaska state Alaska 1-Bedroom \n",
970
  "... ... ... ... ... ... ... \n",
971
  "117907 62 51 Wyoming state Wyoming All Bedrooms \n",
972
  "117908 62 51 Wyoming state Wyoming All Bedrooms \n",
 
974
  "117910 62 51 Wyoming state Wyoming All Bedrooms \n",
975
  "117911 62 51 Wyoming state Wyoming All Bedrooms \n",
976
  "\n",
977
+ " Home Type Date \\\n",
978
+ "0 all homes 2000-01-31 \n",
979
+ "1 all homes 2000-02-29 \n",
980
+ "2 all homes 2000-03-31 \n",
981
+ "3 all homes 2000-04-30 \n",
982
+ "4 all homes 2000-05-31 \n",
983
+ "... ... ... \n",
984
+ "117907 condo/co-op 2023-09-30 \n",
985
+ "117908 condo/co-op 2023-10-31 \n",
986
+ "117909 condo/co-op 2023-11-30 \n",
987
+ "117910 condo/co-op 2023-12-31 \n",
988
+ "117911 condo/co-op 2024-01-31 \n",
989
  "\n",
990
  " Mid Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
991
+ "0 81310.639504 \n",
992
+ "1 80419.761984 \n",
993
+ "2 80480.449461 \n",
994
+ "3 79799.206525 \n",
995
+ "4 79666.469861 \n",
996
  "... ... \n",
997
+ "117907 486974.735908 \n",
998
+ "117908 485847.539614 \n",
999
+ "117909 484223.885775 \n",
1000
+ "117910 481522.403338 \n",
1001
+ "117911 481181.718200 \n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1002
  "\n",
1003
  " Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted) \\\n",
1004
  "0 NaN \n",
 
1013
  "117910 NaN \n",
1014
  "117911 NaN \n",
1015
  "\n",
1016
+ " Top Tier ZHVI (Smoothed) (Seasonally Adjusted) \n",
1017
+ "0 NaN \n",
1018
+ "1 NaN \n",
1019
+ "2 NaN \n",
1020
+ "3 NaN \n",
1021
+ "4 NaN \n",
1022
+ "... ... \n",
1023
+ "117907 NaN \n",
1024
+ "117908 NaN \n",
1025
+ "117909 NaN \n",
1026
+ "117910 NaN \n",
1027
+ "117911 NaN \n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1028
  "\n",
1029
+ "[117912 rows x 11 columns]"
1030
  ]
1031
  },
1032
+ "execution_count": 5,
1033
  "metadata": {},
1034
  "output_type": "execute_result"
1035
  }
processors/home_values_forecasts.ipynb CHANGED
@@ -322,43 +322,43 @@
322
  "31853 North Port-Sarasota-Bradenton, FL Sarasota County \n",
323
  "\n",
324
  " BaseDate Month Over Month % (Smoothed) (Seasonally Adjusted) \\\n",
325
- "0 2023-12-31 NaN \n",
326
- "1 2023-12-31 NaN \n",
327
- "2 2023-12-31 0.4 \n",
328
- "3 2023-12-31 0.2 \n",
329
- "4 2023-12-31 NaN \n",
330
- "... ... ... \n",
331
- "31849 2023-12-31 NaN \n",
332
- "31850 2023-12-31 NaN \n",
333
- "31851 2023-12-31 NaN \n",
334
- "31852 2023-12-31 NaN \n",
335
- "31853 2023-12-31 NaN \n",
336
  "\n",
337
  " Quarter Over Quarter % (Smoothed) (Seasonally Adjusted) \\\n",
338
- "0 NaN \n",
339
- "1 NaN \n",
340
- "2 0.9 \n",
341
- "3 0.7 \n",
342
- "4 NaN \n",
343
- "... ... \n",
344
- "31849 NaN \n",
345
- "31850 NaN \n",
346
- "31851 NaN \n",
347
- "31852 NaN \n",
348
- "31853 NaN \n",
349
  "\n",
350
  " Year Over Year % (Smoothed) (Seasonally Adjusted) Month Over Month % \\\n",
351
- "0 NaN -0.7 \n",
352
- "1 NaN -0.7 \n",
353
- "2 3.2 -0.6 \n",
354
- "3 2.7 -0.6 \n",
355
- "4 NaN -0.7 \n",
356
- "... ... ... \n",
357
- "31849 NaN -0.7 \n",
358
- "31850 NaN -0.7 \n",
359
- "31851 NaN -1.0 \n",
360
- "31852 NaN -0.4 \n",
361
- "31853 NaN -0.9 \n",
362
  "\n",
363
  " Quarter Over Quarter % Year Over Year % \n",
364
  "0 -0.9 0.6 \n",
@@ -420,19 +420,336 @@
420
  },
421
  {
422
  "cell_type": "code",
423
- "execution_count": 1,
424
  "metadata": {},
425
  "outputs": [
426
  {
427
- "ename": "NameError",
428
- "evalue": "name 'combined_df' is not defined",
429
- "output_type": "error",
430
- "traceback": [
431
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
432
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
433
- "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Adjust columns\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m final_df \u001b[38;5;241m=\u001b[39m \u001b[43mcombined_df\u001b[49m\n\u001b[1;32m 3\u001b[0m final_df \u001b[38;5;241m=\u001b[39m combined_df\u001b[38;5;241m.\u001b[39mdrop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStateName\u001b[39m\u001b[38;5;124m\"\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 4\u001b[0m final_df \u001b[38;5;241m=\u001b[39m final_df\u001b[38;5;241m.\u001b[39mrename(\n\u001b[1;32m 5\u001b[0m columns\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCountyName\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCounty\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11\u001b[0m }\n\u001b[1;32m 12\u001b[0m )\n",
434
- "\u001b[0;31mNameError\u001b[0m: name 'combined_df' is not defined"
435
- ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
436
  }
437
  ],
438
  "source": [
 
322
  "31853 North Port-Sarasota-Bradenton, FL Sarasota County \n",
323
  "\n",
324
  " BaseDate Month Over Month % (Smoothed) (Seasonally Adjusted) \\\n",
325
+ "0 2023-12-31 NaN \n",
326
+ "1 2023-12-31 NaN \n",
327
+ "2 2023-12-31 0.4 \n",
328
+ "3 2023-12-31 0.2 \n",
329
+ "4 2023-12-31 NaN \n",
330
+ "... ... ... \n",
331
+ "31849 2023-12-31 NaN \n",
332
+ "31850 2023-12-31 NaN \n",
333
+ "31851 2023-12-31 NaN \n",
334
+ "31852 2023-12-31 NaN \n",
335
+ "31853 2023-12-31 NaN \n",
336
  "\n",
337
  " Quarter Over Quarter % (Smoothed) (Seasonally Adjusted) \\\n",
338
+ "0 NaN \n",
339
+ "1 NaN \n",
340
+ "2 0.9 \n",
341
+ "3 0.7 \n",
342
+ "4 NaN \n",
343
+ "... ... \n",
344
+ "31849 NaN \n",
345
+ "31850 NaN \n",
346
+ "31851 NaN \n",
347
+ "31852 NaN \n",
348
+ "31853 NaN \n",
349
  "\n",
350
  " Year Over Year % (Smoothed) (Seasonally Adjusted) Month Over Month % \\\n",
351
+ "0 NaN -0.7 \n",
352
+ "1 NaN -0.7 \n",
353
+ "2 3.2 -0.6 \n",
354
+ "3 2.7 -0.6 \n",
355
+ "4 NaN -0.7 \n",
356
+ "... ... ... \n",
357
+ "31849 NaN -0.7 \n",
358
+ "31850 NaN -0.7 \n",
359
+ "31851 NaN -1.0 \n",
360
+ "31852 NaN -0.4 \n",
361
+ "31853 NaN -0.9 \n",
362
  "\n",
363
  " Quarter Over Quarter % Year Over Year % \n",
364
  "0 -0.9 0.6 \n",
 
420
  },
421
  {
422
  "cell_type": "code",
423
+ "execution_count": 4,
424
  "metadata": {},
425
  "outputs": [
426
  {
427
+ "data": {
428
+ "text/html": [
429
+ "<div>\n",
430
+ "<style scoped>\n",
431
+ " .dataframe tbody tr th:only-of-type {\n",
432
+ " vertical-align: middle;\n",
433
+ " }\n",
434
+ "\n",
435
+ " .dataframe tbody tr th {\n",
436
+ " vertical-align: top;\n",
437
+ " }\n",
438
+ "\n",
439
+ " .dataframe thead th {\n",
440
+ " text-align: right;\n",
441
+ " }\n",
442
+ "</style>\n",
443
+ "<table border=\"1\" class=\"dataframe\">\n",
444
+ " <thead>\n",
445
+ " <tr style=\"text-align: right;\">\n",
446
+ " <th></th>\n",
447
+ " <th>Region ID</th>\n",
448
+ " <th>Size Rank</th>\n",
449
+ " <th>Region</th>\n",
450
+ " <th>Region Type</th>\n",
451
+ " <th>State</th>\n",
452
+ " <th>City</th>\n",
453
+ " <th>Metro</th>\n",
454
+ " <th>County</th>\n",
455
+ " <th>Date</th>\n",
456
+ " <th>Month Over Month % (Smoothed) (Seasonally Adjusted)</th>\n",
457
+ " <th>Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)</th>\n",
458
+ " <th>Year Over Year % (Smoothed) (Seasonally Adjusted)</th>\n",
459
+ " <th>Month Over Month %</th>\n",
460
+ " <th>Quarter Over Quarter %</th>\n",
461
+ " <th>Year Over Year %</th>\n",
462
+ " </tr>\n",
463
+ " </thead>\n",
464
+ " <tbody>\n",
465
+ " <tr>\n",
466
+ " <th>0</th>\n",
467
+ " <td>58001</td>\n",
468
+ " <td>30490</td>\n",
469
+ " <td>501</td>\n",
470
+ " <td>zip</td>\n",
471
+ " <td>NY</td>\n",
472
+ " <td>Holtsville</td>\n",
473
+ " <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
474
+ " <td>Suffolk County</td>\n",
475
+ " <td>2023-12-31</td>\n",
476
+ " <td>NaN</td>\n",
477
+ " <td>NaN</td>\n",
478
+ " <td>NaN</td>\n",
479
+ " <td>-0.7</td>\n",
480
+ " <td>-0.9</td>\n",
481
+ " <td>0.6</td>\n",
482
+ " </tr>\n",
483
+ " <tr>\n",
484
+ " <th>1</th>\n",
485
+ " <td>58002</td>\n",
486
+ " <td>30490</td>\n",
487
+ " <td>544</td>\n",
488
+ " <td>zip</td>\n",
489
+ " <td>NY</td>\n",
490
+ " <td>Holtsville</td>\n",
491
+ " <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
492
+ " <td>Suffolk County</td>\n",
493
+ " <td>2023-12-31</td>\n",
494
+ " <td>NaN</td>\n",
495
+ " <td>NaN</td>\n",
496
+ " <td>NaN</td>\n",
497
+ " <td>-0.7</td>\n",
498
+ " <td>-0.9</td>\n",
499
+ " <td>0.6</td>\n",
500
+ " </tr>\n",
501
+ " <tr>\n",
502
+ " <th>2</th>\n",
503
+ " <td>58196</td>\n",
504
+ " <td>7440</td>\n",
505
+ " <td>1001</td>\n",
506
+ " <td>zip</td>\n",
507
+ " <td>MA</td>\n",
508
+ " <td>Agawam</td>\n",
509
+ " <td>Springfield, MA</td>\n",
510
+ " <td>Hampden County</td>\n",
511
+ " <td>2023-12-31</td>\n",
512
+ " <td>0.4</td>\n",
513
+ " <td>0.9</td>\n",
514
+ " <td>3.2</td>\n",
515
+ " <td>-0.6</td>\n",
516
+ " <td>0.0</td>\n",
517
+ " <td>3.0</td>\n",
518
+ " </tr>\n",
519
+ " <tr>\n",
520
+ " <th>3</th>\n",
521
+ " <td>58197</td>\n",
522
+ " <td>3911</td>\n",
523
+ " <td>1002</td>\n",
524
+ " <td>zip</td>\n",
525
+ " <td>MA</td>\n",
526
+ " <td>Amherst</td>\n",
527
+ " <td>Springfield, MA</td>\n",
528
+ " <td>Hampshire County</td>\n",
529
+ " <td>2023-12-31</td>\n",
530
+ " <td>0.2</td>\n",
531
+ " <td>0.7</td>\n",
532
+ " <td>2.7</td>\n",
533
+ " <td>-0.6</td>\n",
534
+ " <td>0.0</td>\n",
535
+ " <td>2.9</td>\n",
536
+ " </tr>\n",
537
+ " <tr>\n",
538
+ " <th>4</th>\n",
539
+ " <td>58198</td>\n",
540
+ " <td>8838</td>\n",
541
+ " <td>1003</td>\n",
542
+ " <td>zip</td>\n",
543
+ " <td>MA</td>\n",
544
+ " <td>Amherst</td>\n",
545
+ " <td>Springfield, MA</td>\n",
546
+ " <td>Hampshire County</td>\n",
547
+ " <td>2023-12-31</td>\n",
548
+ " <td>NaN</td>\n",
549
+ " <td>NaN</td>\n",
550
+ " <td>NaN</td>\n",
551
+ " <td>-0.7</td>\n",
552
+ " <td>0.0</td>\n",
553
+ " <td>3.4</td>\n",
554
+ " </tr>\n",
555
+ " <tr>\n",
556
+ " <th>...</th>\n",
557
+ " <td>...</td>\n",
558
+ " <td>...</td>\n",
559
+ " <td>...</td>\n",
560
+ " <td>...</td>\n",
561
+ " <td>...</td>\n",
562
+ " <td>...</td>\n",
563
+ " <td>...</td>\n",
564
+ " <td>...</td>\n",
565
+ " <td>...</td>\n",
566
+ " <td>...</td>\n",
567
+ " <td>...</td>\n",
568
+ " <td>...</td>\n",
569
+ " <td>...</td>\n",
570
+ " <td>...</td>\n",
571
+ " <td>...</td>\n",
572
+ " </tr>\n",
573
+ " <tr>\n",
574
+ " <th>31849</th>\n",
575
+ " <td>827279</td>\n",
576
+ " <td>7779</td>\n",
577
+ " <td>72405</td>\n",
578
+ " <td>zip</td>\n",
579
+ " <td>AR</td>\n",
580
+ " <td>Jonesboro</td>\n",
581
+ " <td>Jonesboro, AR</td>\n",
582
+ " <td>Craighead County</td>\n",
583
+ " <td>2023-12-31</td>\n",
584
+ " <td>NaN</td>\n",
585
+ " <td>NaN</td>\n",
586
+ " <td>NaN</td>\n",
587
+ " <td>-0.7</td>\n",
588
+ " <td>0.0</td>\n",
589
+ " <td>2.5</td>\n",
590
+ " </tr>\n",
591
+ " <tr>\n",
592
+ " <th>31850</th>\n",
593
+ " <td>834213</td>\n",
594
+ " <td>30490</td>\n",
595
+ " <td>11437</td>\n",
596
+ " <td>zip</td>\n",
597
+ " <td>NY</td>\n",
598
+ " <td>New York</td>\n",
599
+ " <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
600
+ " <td>Queens County</td>\n",
601
+ " <td>2023-12-31</td>\n",
602
+ " <td>NaN</td>\n",
603
+ " <td>NaN</td>\n",
604
+ " <td>NaN</td>\n",
605
+ " <td>-0.7</td>\n",
606
+ " <td>-0.9</td>\n",
607
+ " <td>0.6</td>\n",
608
+ " </tr>\n",
609
+ " <tr>\n",
610
+ " <th>31851</th>\n",
611
+ " <td>845914</td>\n",
612
+ " <td>6361</td>\n",
613
+ " <td>85288</td>\n",
614
+ " <td>zip</td>\n",
615
+ " <td>AZ</td>\n",
616
+ " <td>Tempe</td>\n",
617
+ " <td>Phoenix-Mesa-Chandler, AZ</td>\n",
618
+ " <td>Maricopa County</td>\n",
619
+ " <td>2023-12-31</td>\n",
620
+ " <td>NaN</td>\n",
621
+ " <td>NaN</td>\n",
622
+ " <td>NaN</td>\n",
623
+ " <td>-1.0</td>\n",
624
+ " <td>0.0</td>\n",
625
+ " <td>4.5</td>\n",
626
+ " </tr>\n",
627
+ " <tr>\n",
628
+ " <th>31852</th>\n",
629
+ " <td>847854</td>\n",
630
+ " <td>39992</td>\n",
631
+ " <td>20598</td>\n",
632
+ " <td>zip</td>\n",
633
+ " <td>VA</td>\n",
634
+ " <td>Arlington</td>\n",
635
+ " <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
636
+ " <td>Arlington County</td>\n",
637
+ " <td>2023-12-31</td>\n",
638
+ " <td>NaN</td>\n",
639
+ " <td>NaN</td>\n",
640
+ " <td>NaN</td>\n",
641
+ " <td>-0.4</td>\n",
642
+ " <td>0.9</td>\n",
643
+ " <td>1.2</td>\n",
644
+ " </tr>\n",
645
+ " <tr>\n",
646
+ " <th>31853</th>\n",
647
+ " <td>847855</td>\n",
648
+ " <td>30490</td>\n",
649
+ " <td>34249</td>\n",
650
+ " <td>zip</td>\n",
651
+ " <td>FL</td>\n",
652
+ " <td>Sarasota</td>\n",
653
+ " <td>North Port-Sarasota-Bradenton, FL</td>\n",
654
+ " <td>Sarasota County</td>\n",
655
+ " <td>2023-12-31</td>\n",
656
+ " <td>NaN</td>\n",
657
+ " <td>NaN</td>\n",
658
+ " <td>NaN</td>\n",
659
+ " <td>-0.9</td>\n",
660
+ " <td>-0.1</td>\n",
661
+ " <td>5.4</td>\n",
662
+ " </tr>\n",
663
+ " </tbody>\n",
664
+ "</table>\n",
665
+ "<p>31854 rows × 15 columns</p>\n",
666
+ "</div>"
667
+ ],
668
+ "text/plain": [
669
+ " Region ID Size Rank Region Region Type State City \\\n",
670
+ "0 58001 30490 501 zip NY Holtsville \n",
671
+ "1 58002 30490 544 zip NY Holtsville \n",
672
+ "2 58196 7440 1001 zip MA Agawam \n",
673
+ "3 58197 3911 1002 zip MA Amherst \n",
674
+ "4 58198 8838 1003 zip MA Amherst \n",
675
+ "... ... ... ... ... ... ... \n",
676
+ "31849 827279 7779 72405 zip AR Jonesboro \n",
677
+ "31850 834213 30490 11437 zip NY New York \n",
678
+ "31851 845914 6361 85288 zip AZ Tempe \n",
679
+ "31852 847854 39992 20598 zip VA Arlington \n",
680
+ "31853 847855 30490 34249 zip FL Sarasota \n",
681
+ "\n",
682
+ " Metro County \\\n",
683
+ "0 New York-Newark-Jersey City, NY-NJ-PA Suffolk County \n",
684
+ "1 New York-Newark-Jersey City, NY-NJ-PA Suffolk County \n",
685
+ "2 Springfield, MA Hampden County \n",
686
+ "3 Springfield, MA Hampshire County \n",
687
+ "4 Springfield, MA Hampshire County \n",
688
+ "... ... ... \n",
689
+ "31849 Jonesboro, AR Craighead County \n",
690
+ "31850 New York-Newark-Jersey City, NY-NJ-PA Queens County \n",
691
+ "31851 Phoenix-Mesa-Chandler, AZ Maricopa County \n",
692
+ "31852 Washington-Arlington-Alexandria, DC-VA-MD-WV Arlington County \n",
693
+ "31853 North Port-Sarasota-Bradenton, FL Sarasota County \n",
694
+ "\n",
695
+ " Date Month Over Month % (Smoothed) (Seasonally Adjusted) \\\n",
696
+ "0 2023-12-31 NaN \n",
697
+ "1 2023-12-31 NaN \n",
698
+ "2 2023-12-31 0.4 \n",
699
+ "3 2023-12-31 0.2 \n",
700
+ "4 2023-12-31 NaN \n",
701
+ "... ... ... \n",
702
+ "31849 2023-12-31 NaN \n",
703
+ "31850 2023-12-31 NaN \n",
704
+ "31851 2023-12-31 NaN \n",
705
+ "31852 2023-12-31 NaN \n",
706
+ "31853 2023-12-31 NaN \n",
707
+ "\n",
708
+ " Quarter Over Quarter % (Smoothed) (Seasonally Adjusted) \\\n",
709
+ "0 NaN \n",
710
+ "1 NaN \n",
711
+ "2 0.9 \n",
712
+ "3 0.7 \n",
713
+ "4 NaN \n",
714
+ "... ... \n",
715
+ "31849 NaN \n",
716
+ "31850 NaN \n",
717
+ "31851 NaN \n",
718
+ "31852 NaN \n",
719
+ "31853 NaN \n",
720
+ "\n",
721
+ " Year Over Year % (Smoothed) (Seasonally Adjusted) Month Over Month % \\\n",
722
+ "0 NaN -0.7 \n",
723
+ "1 NaN -0.7 \n",
724
+ "2 3.2 -0.6 \n",
725
+ "3 2.7 -0.6 \n",
726
+ "4 NaN -0.7 \n",
727
+ "... ... ... \n",
728
+ "31849 NaN -0.7 \n",
729
+ "31850 NaN -0.7 \n",
730
+ "31851 NaN -1.0 \n",
731
+ "31852 NaN -0.4 \n",
732
+ "31853 NaN -0.9 \n",
733
+ "\n",
734
+ " Quarter Over Quarter % Year Over Year % \n",
735
+ "0 -0.9 0.6 \n",
736
+ "1 -0.9 0.6 \n",
737
+ "2 0.0 3.0 \n",
738
+ "3 0.0 2.9 \n",
739
+ "4 0.0 3.4 \n",
740
+ "... ... ... \n",
741
+ "31849 0.0 2.5 \n",
742
+ "31850 -0.9 0.6 \n",
743
+ "31851 0.0 4.5 \n",
744
+ "31852 0.9 1.2 \n",
745
+ "31853 -0.1 5.4 \n",
746
+ "\n",
747
+ "[31854 rows x 15 columns]"
748
+ ]
749
+ },
750
+ "execution_count": 4,
751
+ "metadata": {},
752
+ "output_type": "execute_result"
753
  }
754
  ],
755
  "source": [
processors/new_construction.ipynb CHANGED
@@ -514,18 +514,18 @@
514
  "49485 845162 535 Granbury, TX msa TX all homes \n",
515
  "49486 845162 535 Granbury, TX msa TX all homes \n",
516
  "\n",
517
- " Date Sales Count Median Sale Price per Sqft Median Sale Price \n",
518
- "0 2018-01-31 33940.0 137.412316 309000.0 \n",
519
- "1 2018-02-28 33304.0 137.199170 309072.5 \n",
520
- "2 2018-03-31 42641.0 139.520863 315488.0 \n",
521
- "3 2018-04-30 37588.0 139.778110 314990.0 \n",
522
- "4 2018-05-31 39933.0 143.317968 324500.0 \n",
523
- "... ... ... ... ... \n",
524
- "49482 2023-07-31 31.0 NaN NaN \n",
525
- "49483 2023-08-31 33.0 NaN NaN \n",
526
- "49484 2023-09-30 26.0 NaN NaN \n",
527
- "49485 2023-10-31 24.0 NaN NaN \n",
528
- "49486 2023-11-30 16.0 NaN NaN \n",
529
  "\n",
530
  "[49487 rows x 10 columns]"
531
  ]
 
514
  "49485 845162 535 Granbury, TX msa TX all homes \n",
515
  "49486 845162 535 Granbury, TX msa TX all homes \n",
516
  "\n",
517
+ " Date Sales Count Median Sale Price per Sqft Median Sale Price \n",
518
+ "0 2018-01-31 33940.0 137.412316 309000.0 \n",
519
+ "1 2018-02-28 33304.0 137.199170 309072.5 \n",
520
+ "2 2018-03-31 42641.0 139.520863 315488.0 \n",
521
+ "3 2018-04-30 37588.0 139.778110 314990.0 \n",
522
+ "4 2018-05-31 39933.0 143.317968 324500.0 \n",
523
+ "... ... ... ... ... \n",
524
+ "49482 2023-07-31 31.0 NaN NaN \n",
525
+ "49483 2023-08-31 33.0 NaN NaN \n",
526
+ "49484 2023-09-30 26.0 NaN NaN \n",
527
+ "49485 2023-10-31 24.0 NaN NaN \n",
528
+ "49486 2023-11-30 16.0 NaN NaN \n",
529
  "\n",
530
  "[49487 rows x 10 columns]"
531
  ]
processors/rentals.ipynb CHANGED
@@ -2,7 +2,7 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 2,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
@@ -19,7 +19,7 @@
19
  },
20
  {
21
  "cell_type": "code",
22
- "execution_count": 3,
23
  "metadata": {},
24
  "outputs": [],
25
  "source": [
@@ -32,7 +32,7 @@
32
  },
33
  {
34
  "cell_type": "code",
35
- "execution_count": 7,
36
  "metadata": {},
37
  "outputs": [
38
  {
@@ -346,7 +346,7 @@
346
  "[1258740 rows x 15 columns]"
347
  ]
348
  },
349
- "execution_count": 7,
350
  "metadata": {},
351
  "output_type": "execute_result"
352
  }
@@ -438,7 +438,7 @@
438
  },
439
  {
440
  "cell_type": "code",
441
- "execution_count": 8,
442
  "metadata": {},
443
  "outputs": [
444
  {
@@ -740,7 +740,7 @@
740
  "[1258740 rows x 14 columns]"
741
  ]
742
  },
743
- "execution_count": 8,
744
  "metadata": {},
745
  "output_type": "execute_result"
746
  }
@@ -764,7 +764,7 @@
764
  },
765
  {
766
  "cell_type": "code",
767
- "execution_count": 6,
768
  "metadata": {},
769
  "outputs": [
770
  {
@@ -915,7 +915,7 @@
915
  " <td>city</td>\n",
916
  " <td>all homes plus multifamily</td>\n",
917
  " <td>Camden County</td>\n",
918
- " <td>NaN</td>\n",
919
  " <td>NaN</td>\n",
920
  " <td>NaN</td>\n",
921
  " <td>2023-08-31</td>\n",
@@ -932,7 +932,7 @@
932
  " <td>city</td>\n",
933
  " <td>all homes plus multifamily</td>\n",
934
  " <td>Camden County</td>\n",
935
- " <td>NaN</td>\n",
936
  " <td>NaN</td>\n",
937
  " <td>NaN</td>\n",
938
  " <td>2023-09-30</td>\n",
@@ -949,7 +949,7 @@
949
  " <td>city</td>\n",
950
  " <td>all homes plus multifamily</td>\n",
951
  " <td>Camden County</td>\n",
952
- " <td>NaN</td>\n",
953
  " <td>NaN</td>\n",
954
  " <td>NaN</td>\n",
955
  " <td>2023-10-31</td>\n",
@@ -966,7 +966,7 @@
966
  " <td>city</td>\n",
967
  " <td>all homes plus multifamily</td>\n",
968
  " <td>Camden County</td>\n",
969
- " <td>NaN</td>\n",
970
  " <td>NaN</td>\n",
971
  " <td>NaN</td>\n",
972
  " <td>2023-11-30</td>\n",
@@ -983,7 +983,7 @@
983
  " <td>city</td>\n",
984
  " <td>all homes plus multifamily</td>\n",
985
  " <td>Camden County</td>\n",
986
- " <td>NaN</td>\n",
987
  " <td>NaN</td>\n",
988
  " <td>NaN</td>\n",
989
  " <td>2023-12-31</td>\n",
@@ -1011,31 +1011,44 @@
1011
  "1258738 857850 713 Cherry Hill city \n",
1012
  "1258739 857850 713 Cherry Hill city \n",
1013
  "\n",
1014
- " Home Type State Metro \\\n",
1015
- "0 all homes plus multifamily Ada County Boise City, ID \n",
1016
- "1 all homes plus multifamily Ada County Boise City, ID \n",
1017
- "2 all homes plus multifamily Ada County Boise City, ID \n",
1018
- "3 all homes plus multifamily Ada County Boise City, ID \n",
1019
- "4 all homes plus multifamily Ada County Boise City, ID \n",
1020
- "... ... ... ... \n",
1021
- "1258735 all homes plus multifamily Camden County NaN \n",
1022
- "1258736 all homes plus multifamily Camden County NaN \n",
1023
- "1258737 all homes plus multifamily Camden County NaN \n",
1024
- "1258738 all homes plus multifamily Camden County NaN \n",
1025
- "1258739 all homes plus multifamily Camden County NaN \n",
 
 
 
 
 
 
 
 
 
 
 
 
 
1026
  "\n",
1027
- " State Code FIPS Municipal Code FIPS Date Rent (Smoothed) \\\n",
1028
- "0 16.0 1.0 2015-01-31 927.493763 \n",
1029
- "1 16.0 1.0 2015-02-28 931.690623 \n",
1030
- "2 16.0 1.0 2015-03-31 932.568601 \n",
1031
- "3 16.0 1.0 2015-04-30 933.148134 \n",
1032
- "4 16.0 1.0 2015-05-31 941.045724 \n",
1033
- "... ... ... ... ... \n",
1034
- "1258735 NaN NaN 2023-08-31 2291.604800 \n",
1035
- "1258736 NaN NaN 2023-09-30 2296.188906 \n",
1036
- "1258737 NaN NaN 2023-10-31 2292.270938 \n",
1037
- "1258738 NaN NaN 2023-11-30 2253.417140 \n",
1038
- "1258739 NaN NaN 2023-12-31 2280.830303 \n",
1039
  "\n",
1040
  " Rent (Smoothed) (Seasonally Adjusted) City County \n",
1041
  "0 927.493763 NaN Ada County \n",
@@ -1053,7 +1066,7 @@
1053
  "[1258740 rows x 14 columns]"
1054
  ]
1055
  },
1056
- "execution_count": 6,
1057
  "metadata": {},
1058
  "output_type": "execute_result"
1059
  }
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 1,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
 
19
  },
20
  {
21
  "cell_type": "code",
22
+ "execution_count": 2,
23
  "metadata": {},
24
  "outputs": [],
25
  "source": [
 
32
  },
33
  {
34
  "cell_type": "code",
35
+ "execution_count": 3,
36
  "metadata": {},
37
  "outputs": [
38
  {
 
346
  "[1258740 rows x 15 columns]"
347
  ]
348
  },
349
+ "execution_count": 3,
350
  "metadata": {},
351
  "output_type": "execute_result"
352
  }
 
438
  },
439
  {
440
  "cell_type": "code",
441
+ "execution_count": 4,
442
  "metadata": {},
443
  "outputs": [
444
  {
 
740
  "[1258740 rows x 14 columns]"
741
  ]
742
  },
743
+ "execution_count": 4,
744
  "metadata": {},
745
  "output_type": "execute_result"
746
  }
 
764
  },
765
  {
766
  "cell_type": "code",
767
+ "execution_count": 5,
768
  "metadata": {},
769
  "outputs": [
770
  {
 
915
  " <td>city</td>\n",
916
  " <td>all homes plus multifamily</td>\n",
917
  " <td>Camden County</td>\n",
918
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
919
  " <td>NaN</td>\n",
920
  " <td>NaN</td>\n",
921
  " <td>2023-08-31</td>\n",
 
932
  " <td>city</td>\n",
933
  " <td>all homes plus multifamily</td>\n",
934
  " <td>Camden County</td>\n",
935
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
936
  " <td>NaN</td>\n",
937
  " <td>NaN</td>\n",
938
  " <td>2023-09-30</td>\n",
 
949
  " <td>city</td>\n",
950
  " <td>all homes plus multifamily</td>\n",
951
  " <td>Camden County</td>\n",
952
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
953
  " <td>NaN</td>\n",
954
  " <td>NaN</td>\n",
955
  " <td>2023-10-31</td>\n",
 
966
  " <td>city</td>\n",
967
  " <td>all homes plus multifamily</td>\n",
968
  " <td>Camden County</td>\n",
969
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
970
  " <td>NaN</td>\n",
971
  " <td>NaN</td>\n",
972
  " <td>2023-11-30</td>\n",
 
983
  " <td>city</td>\n",
984
  " <td>all homes plus multifamily</td>\n",
985
  " <td>Camden County</td>\n",
986
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
987
  " <td>NaN</td>\n",
988
  " <td>NaN</td>\n",
989
  " <td>2023-12-31</td>\n",
 
1011
  "1258738 857850 713 Cherry Hill city \n",
1012
  "1258739 857850 713 Cherry Hill city \n",
1013
  "\n",
1014
+ " Home Type State \\\n",
1015
+ "0 all homes plus multifamily Ada County \n",
1016
+ "1 all homes plus multifamily Ada County \n",
1017
+ "2 all homes plus multifamily Ada County \n",
1018
+ "3 all homes plus multifamily Ada County \n",
1019
+ "4 all homes plus multifamily Ada County \n",
1020
+ "... ... ... \n",
1021
+ "1258735 all homes plus multifamily Camden County \n",
1022
+ "1258736 all homes plus multifamily Camden County \n",
1023
+ "1258737 all homes plus multifamily Camden County \n",
1024
+ "1258738 all homes plus multifamily Camden County \n",
1025
+ "1258739 all homes plus multifamily Camden County \n",
1026
+ "\n",
1027
+ " Metro State Code FIPS \\\n",
1028
+ "0 Boise City, ID 16.0 \n",
1029
+ "1 Boise City, ID 16.0 \n",
1030
+ "2 Boise City, ID 16.0 \n",
1031
+ "3 Boise City, ID 16.0 \n",
1032
+ "4 Boise City, ID 16.0 \n",
1033
+ "... ... ... \n",
1034
+ "1258735 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
1035
+ "1258736 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
1036
+ "1258737 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
1037
+ "1258738 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
1038
+ "1258739 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
1039
  "\n",
1040
+ " Municipal Code FIPS Date Rent (Smoothed) \\\n",
1041
+ "0 1.0 2015-01-31 927.493763 \n",
1042
+ "1 1.0 2015-02-28 931.690623 \n",
1043
+ "2 1.0 2015-03-31 932.568601 \n",
1044
+ "3 1.0 2015-04-30 933.148134 \n",
1045
+ "4 1.0 2015-05-31 941.045724 \n",
1046
+ "... ... ... ... \n",
1047
+ "1258735 NaN 2023-08-31 2291.604800 \n",
1048
+ "1258736 NaN 2023-09-30 2296.188906 \n",
1049
+ "1258737 NaN 2023-10-31 2292.270938 \n",
1050
+ "1258738 NaN 2023-11-30 2253.417140 \n",
1051
+ "1258739 NaN 2023-12-31 2280.830303 \n",
1052
  "\n",
1053
  " Rent (Smoothed) (Seasonally Adjusted) City County \n",
1054
  "0 927.493763 NaN Ada County \n",
 
1066
  "[1258740 rows x 14 columns]"
1067
  ]
1068
  },
1069
+ "execution_count": 5,
1070
  "metadata": {},
1071
  "output_type": "execute_result"
1072
  }
processors/sales.ipynb CHANGED
@@ -2,7 +2,7 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 2,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
@@ -19,7 +19,7 @@
19
  },
20
  {
21
  "cell_type": "code",
22
- "execution_count": 3,
23
  "metadata": {},
24
  "outputs": [],
25
  "source": [
@@ -32,7 +32,7 @@
32
  },
33
  {
34
  "cell_type": "code",
35
- "execution_count": 4,
36
  "metadata": {},
37
  "outputs": [
38
  {
@@ -442,7 +442,7 @@
442
  "[255024 rows x 18 columns]"
443
  ]
444
  },
445
- "execution_count": 4,
446
  "metadata": {},
447
  "output_type": "execute_result"
448
  }
@@ -502,7 +502,7 @@
502
  },
503
  {
504
  "cell_type": "code",
505
- "execution_count": 52,
506
  "metadata": {},
507
  "outputs": [
508
  {
@@ -878,7 +878,7 @@
878
  "[255024 rows x 18 columns]"
879
  ]
880
  },
881
- "execution_count": 52,
882
  "metadata": {},
883
  "output_type": "execute_result"
884
  }
@@ -901,7 +901,7 @@
901
  },
902
  {
903
  "cell_type": "code",
904
- "execution_count": 53,
905
  "metadata": {},
906
  "outputs": [
907
  {
@@ -1277,7 +1277,7 @@
1277
  "[255024 rows x 18 columns]"
1278
  ]
1279
  },
1280
- "execution_count": 53,
1281
  "metadata": {},
1282
  "output_type": "execute_result"
1283
  }
@@ -1290,7 +1290,7 @@
1290
  },
1291
  {
1292
  "cell_type": "code",
1293
- "execution_count": 54,
1294
  "metadata": {},
1295
  "outputs": [],
1296
  "source": [
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 1,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
 
19
  },
20
  {
21
  "cell_type": "code",
22
+ "execution_count": 2,
23
  "metadata": {},
24
  "outputs": [],
25
  "source": [
 
32
  },
33
  {
34
  "cell_type": "code",
35
+ "execution_count": 3,
36
  "metadata": {},
37
  "outputs": [
38
  {
 
442
  "[255024 rows x 18 columns]"
443
  ]
444
  },
445
+ "execution_count": 3,
446
  "metadata": {},
447
  "output_type": "execute_result"
448
  }
 
502
  },
503
  {
504
  "cell_type": "code",
505
+ "execution_count": 4,
506
  "metadata": {},
507
  "outputs": [
508
  {
 
878
  "[255024 rows x 18 columns]"
879
  ]
880
  },
881
+ "execution_count": 4,
882
  "metadata": {},
883
  "output_type": "execute_result"
884
  }
 
901
  },
902
  {
903
  "cell_type": "code",
904
+ "execution_count": 5,
905
  "metadata": {},
906
  "outputs": [
907
  {
 
1277
  "[255024 rows x 18 columns]"
1278
  ]
1279
  },
1280
+ "execution_count": 5,
1281
  "metadata": {},
1282
  "output_type": "execute_result"
1283
  }
 
1290
  },
1291
  {
1292
  "cell_type": "code",
1293
+ "execution_count": 6,
1294
  "metadata": {},
1295
  "outputs": [],
1296
  "source": [
test-sales.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:089f3958ace720a88adc9f6ea28689c9d8dd27a972b78a21a0883874cae8719d
3
+ size 7524442
zillow.py CHANGED
@@ -35,6 +35,16 @@ _HOMEPAGE = "https://www.zillow.com/research/data/"
35
 
36
  _LICENSE = "other"
37
 
 
 
 
 
 
 
 
 
 
 
38
 
39
  class Zillow(datasets.GeneratorBasedBuilder):
40
  """Housing data in the United States provided by Zillow"""
@@ -89,13 +99,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
89
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
90
  "Region": datasets.Value(dtype="string", id="Region"),
91
  "Region Type": datasets.ClassLabel(
92
- num_classes=3, names=["zip", "country", "msa"]
93
  ),
94
  "State": datasets.Value(dtype="string", id="State"),
95
  "City": datasets.Value(dtype="string", id="City"),
96
  "Metro": datasets.Value(dtype="string", id="Metro"),
97
  "County": datasets.Value(dtype="string", id="County"),
98
- "Date": datasets.Value(dtype="string", id="Date"),
99
  "Month Over Month % (Smoothed) (Seasonally Adjusted)": datasets.Value(
100
  dtype="float32",
101
  id="Month Over Month % (Smoothed) (Seasonally Adjusted)",
@@ -126,13 +136,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
126
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
127
  "Region": datasets.Value(dtype="string", id="Region"),
128
  "Region Type": datasets.ClassLabel(
129
- num_classes=2, names=["country", "msa"]
130
  ),
131
  "State": datasets.Value(dtype="string", id="State"),
132
  "Home Type": datasets.ClassLabel(
133
- num_classes=3, names=["SFR", "all homes", "condo/co-op only"]
134
  ),
135
- "Date": datasets.Value(dtype="string", id="Date"),
136
  "Median Sale Price": datasets.Value(
137
  dtype="float32", id="Median Sale Price"
138
  ),
@@ -149,13 +159,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
149
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
150
  "Region": datasets.Value(dtype="string", id="Region"),
151
  "Region Type": datasets.ClassLabel(
152
- num_classes=2, names=["country", "msa"]
153
  ),
154
  "State": datasets.Value(dtype="string", id="State"),
155
  "Home Type": datasets.ClassLabel(
156
- num_classes=2, names=["SFR", "all homes"]
157
  ),
158
- "Date": datasets.Value(dtype="string", id="Date"),
159
  "Median Listing Price": datasets.Value(
160
  dtype="float32", id="Median Listing Price"
161
  ),
@@ -179,14 +189,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
179
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
180
  "Region": datasets.Value(dtype="string", id="Region"),
181
  "Region Type": datasets.ClassLabel(
182
- num_classes=5, names=["county", "city", "zip", "country", "msa"]
183
  ),
184
  "State": datasets.Value(dtype="string", id="State"),
185
  "Home Type": datasets.ClassLabel(
186
- num_classes=3,
187
- names=["all homes plus multifamily", "SFR", "multifamily"],
188
  ),
189
- "Date": datasets.Value(dtype="string", id="Date"),
190
  "Rent (Smoothed)": datasets.Value(
191
  dtype="float32", id="Rent (Smoothed)"
192
  ),
@@ -202,12 +211,11 @@ class Zillow(datasets.GeneratorBasedBuilder):
202
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
203
  "Region": datasets.Value(dtype="string", id="Region"),
204
  "Region Type": datasets.ClassLabel(
205
- num_classes=2, names=["country", "msa"]
206
  ),
207
  "State": datasets.Value(dtype="string", id="State"),
208
  "Home Type": datasets.ClassLabel(
209
- num_classes=2,
210
- names=["SFR", "all homes"],
211
  ),
212
  "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
213
  "Mean Sale to List Ratio (Smoothed)": datasets.Value(
@@ -252,10 +260,12 @@ class Zillow(datasets.GeneratorBasedBuilder):
252
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
253
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
254
  "Region": datasets.Value(dtype="string", id="Region"),
255
- "Region Type": datasets.ClassLabel(num_classes=1, names=["state"]),
 
 
256
  "State": datasets.Value(dtype="string", id="State"),
257
  "Home Type": datasets.ClassLabel(
258
- num_classes=3, names=["all homes (SFR/condo)", "SFR", "condo"]
259
  ),
260
  "Bedroom Count": datasets.ClassLabel(
261
  num_classes=6,
@@ -268,7 +278,7 @@ class Zillow(datasets.GeneratorBasedBuilder):
268
  "All Bedrooms",
269
  ],
270
  ),
271
- "Date": datasets.Value(dtype="string", id="Date"),
272
  "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
273
  dtype="float32",
274
  id="Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)",
@@ -289,16 +299,14 @@ class Zillow(datasets.GeneratorBasedBuilder):
289
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
290
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
291
  "Region": datasets.Value(dtype="string", id="Region"),
292
- # "Region Type": datasets.Value(dtype="string", id="Region Type"),
293
  "Region Type": datasets.ClassLabel(
294
- num_classes=2, names=["country", "msa"]
295
  ),
296
  "State": datasets.Value(dtype="string", id="State"),
297
- # "Home Type": datasets.Value(dtype="string", id="Home Type"),
298
  "Home Type": datasets.ClassLabel(
299
- num_classes=2, names=["SFR", "all homes (SFR + Condo)"]
300
  ),
301
- "Date": datasets.Value(dtype="string", id="Date"),
302
  "Mean Listings Price Cut Amount (Smoothed)": datasets.Value(
303
  dtype="float32", id="Mean Listings Price Cut Amount (Smoothed)"
304
  ),
@@ -336,7 +344,7 @@ class Zillow(datasets.GeneratorBasedBuilder):
336
  )
337
 
338
  def _split_generators(self, dl_manager):
339
- file_path = os.path.join("processed", self.config.name, "final5.jsonl")
340
  file_train = dl_manager.download(file_path)
341
  # file_test = dl_manager.download(os.path.join(self.config.name, "test.csv"))
342
  # file_eval = dl_manager.download(os.path.join(self.config.name, "valid.csv"))
 
35
 
36
  _LICENSE = "other"
37
 
38
+ HOME_TYPES = [
39
+ "all homes",
40
+ "all homes plus multifamily",
41
+ "SFR",
42
+ "condo/co-op",
43
+ "multifamily",
44
+ ]
45
+
46
+ REGION_TYPES = ["county", "city", "zip", "country", "msa"]
47
+
48
 
49
  class Zillow(datasets.GeneratorBasedBuilder):
50
  """Housing data in the United States provided by Zillow"""
 
99
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
100
  "Region": datasets.Value(dtype="string", id="Region"),
101
  "Region Type": datasets.ClassLabel(
102
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
103
  ),
104
  "State": datasets.Value(dtype="string", id="State"),
105
  "City": datasets.Value(dtype="string", id="City"),
106
  "Metro": datasets.Value(dtype="string", id="Metro"),
107
  "County": datasets.Value(dtype="string", id="County"),
108
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
109
  "Month Over Month % (Smoothed) (Seasonally Adjusted)": datasets.Value(
110
  dtype="float32",
111
  id="Month Over Month % (Smoothed) (Seasonally Adjusted)",
 
136
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
137
  "Region": datasets.Value(dtype="string", id="Region"),
138
  "Region Type": datasets.ClassLabel(
139
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
140
  ),
141
  "State": datasets.Value(dtype="string", id="State"),
142
  "Home Type": datasets.ClassLabel(
143
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
144
  ),
145
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
146
  "Median Sale Price": datasets.Value(
147
  dtype="float32", id="Median Sale Price"
148
  ),
 
159
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
160
  "Region": datasets.Value(dtype="string", id="Region"),
161
  "Region Type": datasets.ClassLabel(
162
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
163
  ),
164
  "State": datasets.Value(dtype="string", id="State"),
165
  "Home Type": datasets.ClassLabel(
166
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
167
  ),
168
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
169
  "Median Listing Price": datasets.Value(
170
  dtype="float32", id="Median Listing Price"
171
  ),
 
189
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
190
  "Region": datasets.Value(dtype="string", id="Region"),
191
  "Region Type": datasets.ClassLabel(
192
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
193
  ),
194
  "State": datasets.Value(dtype="string", id="State"),
195
  "Home Type": datasets.ClassLabel(
196
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
 
197
  ),
198
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
199
  "Rent (Smoothed)": datasets.Value(
200
  dtype="float32", id="Rent (Smoothed)"
201
  ),
 
211
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
212
  "Region": datasets.Value(dtype="string", id="Region"),
213
  "Region Type": datasets.ClassLabel(
214
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
215
  ),
216
  "State": datasets.Value(dtype="string", id="State"),
217
  "Home Type": datasets.ClassLabel(
218
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
 
219
  ),
220
  "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
221
  "Mean Sale to List Ratio (Smoothed)": datasets.Value(
 
260
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
261
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
262
  "Region": datasets.Value(dtype="string", id="Region"),
263
+ "Region Type": datasets.ClassLabel(
264
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
265
+ ),
266
  "State": datasets.Value(dtype="string", id="State"),
267
  "Home Type": datasets.ClassLabel(
268
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
269
  ),
270
  "Bedroom Count": datasets.ClassLabel(
271
  num_classes=6,
 
278
  "All Bedrooms",
279
  ],
280
  ),
281
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
282
  "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
283
  dtype="float32",
284
  id="Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)",
 
299
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
300
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
301
  "Region": datasets.Value(dtype="string", id="Region"),
 
302
  "Region Type": datasets.ClassLabel(
303
+ num_classes=len(REGION_TYPES), names=REGION_TYPES
304
  ),
305
  "State": datasets.Value(dtype="string", id="State"),
 
306
  "Home Type": datasets.ClassLabel(
307
+ num_classes=len(HOME_TYPES), names=HOME_TYPES
308
  ),
309
+ "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
310
  "Mean Listings Price Cut Amount (Smoothed)": datasets.Value(
311
  dtype="float32", id="Mean Listings Price Cut Amount (Smoothed)"
312
  ),
 
344
  )
345
 
346
  def _split_generators(self, dl_manager):
347
+ file_path = os.path.join("processed", self.config.name, "final.jsonl")
348
  file_train = dl_manager.download(file_path)
349
  # file_test = dl_manager.download(os.path.join(self.config.name, "test.csv"))
350
  # file_eval = dl_manager.download(os.path.join(self.config.name, "valid.csv"))