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Datasets:
misikoff
/
zillow

Tasks:
Tabular Regression
Time Series Forecasting
Languages:
English
License:
Dataset card Files
xet
Community
misikoff commited on
Commit
55b505d
·
1 Parent(s): 8737554

fix: regiontype to region type and use categories instead of strings where possible

Browse files
Files changed (5) hide show
  1. checker.ipynb +412 -0
  2. processed/home_values_forecasts/final5.jsonl +2 -2
  3. processors/{home_value_forecasts.ipynb → home_values_forecasts.ipynb} +12 -328
  4. processors/{home_value_forecasts.py → home_values_forecasts.py} +2 -1
  5. zillow.py +50 -13
checker.ipynb ADDED
@@ -0,0 +1,412 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# import json as pandas\n",
10
+ "import pandas as pd"
11
+ ]
12
+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 27,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "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",
37
+ " <tr style=\"text-align: right;\">\n",
38
+ " <th></th>\n",
39
+ " <th>Region ID</th>\n",
40
+ " <th>Size Rank</th>\n",
41
+ " <th>Region</th>\n",
42
+ " <th>Region Type</th>\n",
43
+ " <th>Home Type</th>\n",
44
+ " <th>State</th>\n",
45
+ " <th>Metro</th>\n",
46
+ " <th>State Code FIPS</th>\n",
47
+ " <th>Municipal Code FIPS</th>\n",
48
+ " <th>Date</th>\n",
49
+ " <th>Rent (Smoothed)</th>\n",
50
+ " <th>Rent (Smoothed) (Seasonally Adjusted)</th>\n",
51
+ " <th>City</th>\n",
52
+ " <th>County</th>\n",
53
+ " </tr>\n",
54
+ " </thead>\n",
55
+ " <tbody>\n",
56
+ " <tr>\n",
57
+ " <th>0</th>\n",
58
+ " <td>66</td>\n",
59
+ " <td>146</td>\n",
60
+ " <td>Ada County</td>\n",
61
+ " <td>county</td>\n",
62
+ " <td>all homes plus multifamily</td>\n",
63
+ " <td>Ada County</td>\n",
64
+ " <td>Boise City, ID</td>\n",
65
+ " <td>16.0</td>\n",
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+ " <td>1.0</td>\n",
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+ " <td>2015-01-31</td>\n",
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+ " <td>927.493763</td>\n",
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+ " <td>927.493763</td>\n",
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+ " <td>None</td>\n",
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+ " <td>Ada County</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>66</td>\n",
76
+ " <td>146</td>\n",
77
+ " <td>Ada County</td>\n",
78
+ " <td>county</td>\n",
79
+ " <td>all homes plus multifamily</td>\n",
80
+ " <td>Ada County</td>\n",
81
+ " <td>Boise City, ID</td>\n",
82
+ " <td>16.0</td>\n",
83
+ " <td>1.0</td>\n",
84
+ " <td>2015-02-28</td>\n",
85
+ " <td>931.690623</td>\n",
86
+ " <td>931.690623</td>\n",
87
+ " <td>None</td>\n",
88
+ " <td>Ada County</td>\n",
89
+ " </tr>\n",
90
+ " <tr>\n",
91
+ " <th>2</th>\n",
92
+ " <td>66</td>\n",
93
+ " <td>146</td>\n",
94
+ " <td>Ada County</td>\n",
95
+ " <td>county</td>\n",
96
+ " <td>all homes plus multifamily</td>\n",
97
+ " <td>Ada County</td>\n",
98
+ " <td>Boise City, ID</td>\n",
99
+ " <td>16.0</td>\n",
100
+ " <td>1.0</td>\n",
101
+ " <td>2015-03-31</td>\n",
102
+ " <td>932.568601</td>\n",
103
+ " <td>932.568601</td>\n",
104
+ " <td>None</td>\n",
105
+ " <td>Ada County</td>\n",
106
+ " </tr>\n",
107
+ " <tr>\n",
108
+ " <th>3</th>\n",
109
+ " <td>66</td>\n",
110
+ " <td>146</td>\n",
111
+ " <td>Ada County</td>\n",
112
+ " <td>county</td>\n",
113
+ " <td>all homes plus multifamily</td>\n",
114
+ " <td>Ada County</td>\n",
115
+ " <td>Boise City, ID</td>\n",
116
+ " <td>16.0</td>\n",
117
+ " <td>1.0</td>\n",
118
+ " <td>2015-04-30</td>\n",
119
+ " <td>933.148134</td>\n",
120
+ " <td>933.148134</td>\n",
121
+ " <td>None</td>\n",
122
+ " <td>Ada County</td>\n",
123
+ " </tr>\n",
124
+ " <tr>\n",
125
+ " <th>4</th>\n",
126
+ " <td>66</td>\n",
127
+ " <td>146</td>\n",
128
+ " <td>Ada County</td>\n",
129
+ " <td>county</td>\n",
130
+ " <td>all homes plus multifamily</td>\n",
131
+ " <td>Ada County</td>\n",
132
+ " <td>Boise City, ID</td>\n",
133
+ " <td>16.0</td>\n",
134
+ " <td>1.0</td>\n",
135
+ " <td>2015-05-31</td>\n",
136
+ " <td>941.045724</td>\n",
137
+ " <td>941.045724</td>\n",
138
+ " <td>None</td>\n",
139
+ " <td>Ada County</td>\n",
140
+ " </tr>\n",
141
+ " <tr>\n",
142
+ " <th>...</th>\n",
143
+ " <td>...</td>\n",
144
+ " <td>...</td>\n",
145
+ " <td>...</td>\n",
146
+ " <td>...</td>\n",
147
+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
157
+ " </tr>\n",
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+ " <tr>\n",
159
+ " <th>1258735</th>\n",
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+ " <td>857850</td>\n",
161
+ " <td>713</td>\n",
162
+ " <td>Cherry Hill</td>\n",
163
+ " <td>city</td>\n",
164
+ " <td>all homes plus multifamily</td>\n",
165
+ " <td>Camden County</td>\n",
166
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
167
+ " <td>NaN</td>\n",
168
+ " <td>NaN</td>\n",
169
+ " <td>2023-08-31</td>\n",
170
+ " <td>2291.604800</td>\n",
171
+ " <td>2244.961006</td>\n",
172
+ " <td>Cherry Hill</td>\n",
173
+ " <td>None</td>\n",
174
+ " </tr>\n",
175
+ " <tr>\n",
176
+ " <th>1258736</th>\n",
177
+ " <td>857850</td>\n",
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+ " <td>713</td>\n",
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+ " <td>Cherry Hill</td>\n",
180
+ " <td>city</td>\n",
181
+ " <td>all homes plus multifamily</td>\n",
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+ " <td>Camden County</td>\n",
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+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
184
+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
186
+ " <td>2023-09-30</td>\n",
187
+ " <td>2296.188906</td>\n",
188
+ " <td>2254.213172</td>\n",
189
+ " <td>Cherry Hill</td>\n",
190
+ " <td>None</td>\n",
191
+ " </tr>\n",
192
+ " <tr>\n",
193
+ " <th>1258737</th>\n",
194
+ " <td>857850</td>\n",
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+ " <td>713</td>\n",
196
+ " <td>Cherry Hill</td>\n",
197
+ " <td>city</td>\n",
198
+ " <td>all homes plus multifamily</td>\n",
199
+ " <td>Camden County</td>\n",
200
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
201
+ " <td>NaN</td>\n",
202
+ " <td>NaN</td>\n",
203
+ " <td>2023-10-31</td>\n",
204
+ " <td>2292.270938</td>\n",
205
+ " <td>2261.540446</td>\n",
206
+ " <td>Cherry Hill</td>\n",
207
+ " <td>None</td>\n",
208
+ " </tr>\n",
209
+ " <tr>\n",
210
+ " <th>1258738</th>\n",
211
+ " <td>857850</td>\n",
212
+ " <td>713</td>\n",
213
+ " <td>Cherry Hill</td>\n",
214
+ " <td>city</td>\n",
215
+ " <td>all homes plus multifamily</td>\n",
216
+ " <td>Camden County</td>\n",
217
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
218
+ " <td>NaN</td>\n",
219
+ " <td>NaN</td>\n",
220
+ " <td>2023-11-30</td>\n",
221
+ " <td>2253.417140</td>\n",
222
+ " <td>2257.956024</td>\n",
223
+ " <td>Cherry Hill</td>\n",
224
+ " <td>None</td>\n",
225
+ " </tr>\n",
226
+ " <tr>\n",
227
+ " <th>1258739</th>\n",
228
+ " <td>857850</td>\n",
229
+ " <td>713</td>\n",
230
+ " <td>Cherry Hill</td>\n",
231
+ " <td>city</td>\n",
232
+ " <td>all homes plus multifamily</td>\n",
233
+ " <td>Camden County</td>\n",
234
+ " <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
235
+ " <td>NaN</td>\n",
236
+ " <td>NaN</td>\n",
237
+ " <td>2023-12-31</td>\n",
238
+ " <td>2280.830303</td>\n",
239
+ " <td>2280.830303</td>\n",
240
+ " <td>Cherry Hill</td>\n",
241
+ " <td>None</td>\n",
242
+ " </tr>\n",
243
+ " </tbody>\n",
244
+ "</table>\n",
245
+ "<p>1258740 rows × 14 columns</p>\n",
246
+ "</div>"
247
+ ],
248
+ "text/plain": [
249
+ " Region ID Size Rank Region Region Type \\\n",
250
+ "0 66 146 Ada County county \n",
251
+ "1 66 146 Ada County county \n",
252
+ "2 66 146 Ada County county \n",
253
+ "3 66 146 Ada County county \n",
254
+ "4 66 146 Ada County county \n",
255
+ "... ... ... ... ... \n",
256
+ "1258735 857850 713 Cherry Hill city \n",
257
+ "1258736 857850 713 Cherry Hill city \n",
258
+ "1258737 857850 713 Cherry Hill city \n",
259
+ "1258738 857850 713 Cherry Hill city \n",
260
+ "1258739 857850 713 Cherry Hill city \n",
261
+ "\n",
262
+ " Home Type State \\\n",
263
+ "0 all homes plus multifamily Ada County \n",
264
+ "1 all homes plus multifamily Ada County \n",
265
+ "2 all homes plus multifamily Ada County \n",
266
+ "3 all homes plus multifamily Ada County \n",
267
+ "4 all homes plus multifamily Ada County \n",
268
+ "... ... ... \n",
269
+ "1258735 all homes plus multifamily Camden County \n",
270
+ "1258736 all homes plus multifamily Camden County \n",
271
+ "1258737 all homes plus multifamily Camden County \n",
272
+ "1258738 all homes plus multifamily Camden County \n",
273
+ "1258739 all homes plus multifamily Camden County \n",
274
+ "\n",
275
+ " Metro State Code FIPS \\\n",
276
+ "0 Boise City, ID 16.0 \n",
277
+ "1 Boise City, ID 16.0 \n",
278
+ "2 Boise City, ID 16.0 \n",
279
+ "3 Boise City, ID 16.0 \n",
280
+ "4 Boise City, ID 16.0 \n",
281
+ "... ... ... \n",
282
+ "1258735 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
283
+ "1258736 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
284
+ "1258737 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
285
+ "1258738 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
286
+ "1258739 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD NaN \n",
287
+ "\n",
288
+ " Municipal Code FIPS Date Rent (Smoothed) \\\n",
289
+ "0 1.0 2015-01-31 927.493763 \n",
290
+ "1 1.0 2015-02-28 931.690623 \n",
291
+ "2 1.0 2015-03-31 932.568601 \n",
292
+ "3 1.0 2015-04-30 933.148134 \n",
293
+ "4 1.0 2015-05-31 941.045724 \n",
294
+ "... ... ... ... \n",
295
+ "1258735 NaN 2023-08-31 2291.604800 \n",
296
+ "1258736 NaN 2023-09-30 2296.188906 \n",
297
+ "1258737 NaN 2023-10-31 2292.270938 \n",
298
+ "1258738 NaN 2023-11-30 2253.417140 \n",
299
+ "1258739 NaN 2023-12-31 2280.830303 \n",
300
+ "\n",
301
+ " Rent (Smoothed) (Seasonally Adjusted) City County \n",
302
+ "0 927.493763 None Ada County \n",
303
+ "1 931.690623 None Ada County \n",
304
+ "2 932.568601 None Ada County \n",
305
+ "3 933.148134 None Ada County \n",
306
+ "4 941.045724 None Ada County \n",
307
+ "... ... ... ... \n",
308
+ "1258735 2244.961006 Cherry Hill None \n",
309
+ "1258736 2254.213172 Cherry Hill None \n",
310
+ "1258737 2261.540446 Cherry Hill None \n",
311
+ "1258738 2257.956024 Cherry Hill None \n",
312
+ "1258739 2280.830303 Cherry Hill None \n",
313
+ "\n",
314
+ "[1258740 rows x 14 columns]"
315
+ ]
316
+ },
317
+ "execution_count": 27,
318
+ "metadata": {},
319
+ "output_type": "execute_result"
320
+ }
321
+ ],
322
+ "source": [
323
+ "# read the data\n",
324
+ "x = pd.read_json(\"processed/rentals/final5.jsonl\", lines=True)\n",
325
+ "x"
326
+ ]
327
+ },
328
+ {
329
+ "cell_type": "code",
330
+ "execution_count": 28,
331
+ "metadata": {},
332
+ "outputs": [
333
+ {
334
+ "data": {
335
+ "text/plain": [
336
+ "array(['county', 'city', 'zip', 'country', 'msa'], dtype=object)"
337
+ ]
338
+ },
339
+ "execution_count": 28,
340
+ "metadata": {},
341
+ "output_type": "execute_result"
342
+ }
343
+ ],
344
+ "source": [
345
+ "# get unique values for column\n",
346
+ "x[\"Region Type\"].unique()"
347
+ ]
348
+ },
349
+ {
350
+ "cell_type": "code",
351
+ "execution_count": 29,
352
+ "metadata": {},
353
+ "outputs": [
354
+ {
355
+ "data": {
356
+ "text/plain": [
357
+ "array(['all homes plus multifamily', 'SFR', 'multifamily'], dtype=object)"
358
+ ]
359
+ },
360
+ "execution_count": 29,
361
+ "metadata": {},
362
+ "output_type": "execute_result"
363
+ }
364
+ ],
365
+ "source": [
366
+ "x[\"Home Type\"].unique()"
367
+ ]
368
+ },
369
+ {
370
+ "cell_type": "code",
371
+ "execution_count": 15,
372
+ "metadata": {},
373
+ "outputs": [
374
+ {
375
+ "data": {
376
+ "text/plain": [
377
+ "array(['1-Bedroom', '2-Bedrooms', '3-Bedrooms', '4-Bedrooms',\n",
378
+ " '5+-Bedrooms', 'All Bedrooms'], dtype=object)"
379
+ ]
380
+ },
381
+ "execution_count": 15,
382
+ "metadata": {},
383
+ "output_type": "execute_result"
384
+ }
385
+ ],
386
+ "source": [
387
+ "x[\"Bedroom Count\"].unique()"
388
+ ]
389
+ }
390
+ ],
391
+ "metadata": {
392
+ "kernelspec": {
393
+ "display_name": "sta663",
394
+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
407
+ "version": "3.12.2"
408
+ }
409
+ },
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+ "nbformat": 4,
411
+ "nbformat_minor": 2
412
+ }
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processors/{home_value_forecasts.ipynb → home_values_forecasts.ipynb} RENAMED
@@ -419,336 +419,19 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 4,
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  "metadata": {},
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  "outputs": [
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- "data": {
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- " <thead>\n",
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- " <tr style=\"text-align: right;\">\n",
445
- " <th></th>\n",
446
- " <th>Region ID</th>\n",
447
- " <th>Size Rank</th>\n",
448
- " <th>Region</th>\n",
449
- " <th>RegionType</th>\n",
450
- " <th>State</th>\n",
451
- " <th>City</th>\n",
452
- " <th>Metro</th>\n",
453
- " <th>County</th>\n",
454
- " <th>Date</th>\n",
455
- " <th>Month Over Month % (Smoothed) (Seasonally Adjusted)</th>\n",
456
- " <th>Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)</th>\n",
457
- " <th>Year Over Year % (Smoothed) (Seasonally Adjusted)</th>\n",
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- " <th>Month Over Month %</th>\n",
459
- " <th>Quarter Over Quarter %</th>\n",
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- " <th>Year Over Year %</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>58001</td>\n",
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- " <td>30490</td>\n",
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- " <td>501</td>\n",
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- " <td>zip</td>\n",
470
- " <td>NY</td>\n",
471
- " <td>Holtsville</td>\n",
472
- " <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
473
- " <td>Suffolk County</td>\n",
474
- " <td>2023-12-31</td>\n",
475
- " <td>NaN</td>\n",
476
- " <td>NaN</td>\n",
477
- " <td>NaN</td>\n",
478
- " <td>-0.7</td>\n",
479
- " <td>-0.9</td>\n",
480
- " <td>0.6</td>\n",
481
- " </tr>\n",
482
- " <tr>\n",
483
- " <th>1</th>\n",
484
- " <td>58002</td>\n",
485
- " <td>30490</td>\n",
486
- " <td>544</td>\n",
487
- " <td>zip</td>\n",
488
- " <td>NY</td>\n",
489
- " <td>Holtsville</td>\n",
490
- " <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
491
- " <td>Suffolk County</td>\n",
492
- " <td>2023-12-31</td>\n",
493
- " <td>NaN</td>\n",
494
- " <td>NaN</td>\n",
495
- " <td>NaN</td>\n",
496
- " <td>-0.7</td>\n",
497
- " <td>-0.9</td>\n",
498
- " <td>0.6</td>\n",
499
- " </tr>\n",
500
- " <tr>\n",
501
- " <th>2</th>\n",
502
- " <td>58196</td>\n",
503
- " <td>7440</td>\n",
504
- " <td>1001</td>\n",
505
- " <td>zip</td>\n",
506
- " <td>MA</td>\n",
507
- " <td>Agawam</td>\n",
508
- " <td>Springfield, MA</td>\n",
509
- " <td>Hampden County</td>\n",
510
- " <td>2023-12-31</td>\n",
511
- " <td>0.4</td>\n",
512
- " <td>0.9</td>\n",
513
- " <td>3.2</td>\n",
514
- " <td>-0.6</td>\n",
515
- " <td>0.0</td>\n",
516
- " <td>3.0</td>\n",
517
- " </tr>\n",
518
- " <tr>\n",
519
- " <th>3</th>\n",
520
- " <td>58197</td>\n",
521
- " <td>3911</td>\n",
522
- " <td>1002</td>\n",
523
- " <td>zip</td>\n",
524
- " <td>MA</td>\n",
525
- " <td>Amherst</td>\n",
526
- " <td>Springfield, MA</td>\n",
527
- " <td>Hampshire County</td>\n",
528
- " <td>2023-12-31</td>\n",
529
- " <td>0.2</td>\n",
530
- " <td>0.7</td>\n",
531
- " <td>2.7</td>\n",
532
- " <td>-0.6</td>\n",
533
- " <td>0.0</td>\n",
534
- " <td>2.9</td>\n",
535
- " </tr>\n",
536
- " <tr>\n",
537
- " <th>4</th>\n",
538
- " <td>58198</td>\n",
539
- " <td>8838</td>\n",
540
- " <td>1003</td>\n",
541
- " <td>zip</td>\n",
542
- " <td>MA</td>\n",
543
- " <td>Amherst</td>\n",
544
- " <td>Springfield, MA</td>\n",
545
- " <td>Hampshire County</td>\n",
546
- " <td>2023-12-31</td>\n",
547
- " <td>NaN</td>\n",
548
- " <td>NaN</td>\n",
549
- " <td>NaN</td>\n",
550
- " <td>-0.7</td>\n",
551
- " <td>0.0</td>\n",
552
- " <td>3.4</td>\n",
553
- " </tr>\n",
554
- " <tr>\n",
555
- " <th>...</th>\n",
556
- " <td>...</td>\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
- " </tr>\n",
572
- " <tr>\n",
573
- " <th>31849</th>\n",
574
- " <td>827279</td>\n",
575
- " <td>7779</td>\n",
576
- " <td>72405</td>\n",
577
- " <td>zip</td>\n",
578
- " <td>AR</td>\n",
579
- " <td>Jonesboro</td>\n",
580
- " <td>Jonesboro, AR</td>\n",
581
- " <td>Craighead County</td>\n",
582
- " <td>2023-12-31</td>\n",
583
- " <td>NaN</td>\n",
584
- " <td>NaN</td>\n",
585
- " <td>NaN</td>\n",
586
- " <td>-0.7</td>\n",
587
- " <td>0.0</td>\n",
588
- " <td>2.5</td>\n",
589
- " </tr>\n",
590
- " <tr>\n",
591
- " <th>31850</th>\n",
592
- " <td>834213</td>\n",
593
- " <td>30490</td>\n",
594
- " <td>11437</td>\n",
595
- " <td>zip</td>\n",
596
- " <td>NY</td>\n",
597
- " <td>New York</td>\n",
598
- " <td>New York-Newark-Jersey City, NY-NJ-PA</td>\n",
599
- " <td>Queens County</td>\n",
600
- " <td>2023-12-31</td>\n",
601
- " <td>NaN</td>\n",
602
- " <td>NaN</td>\n",
603
- " <td>NaN</td>\n",
604
- " <td>-0.7</td>\n",
605
- " <td>-0.9</td>\n",
606
- " <td>0.6</td>\n",
607
- " </tr>\n",
608
- " <tr>\n",
609
- " <th>31851</th>\n",
610
- " <td>845914</td>\n",
611
- " <td>6361</td>\n",
612
- " <td>85288</td>\n",
613
- " <td>zip</td>\n",
614
- " <td>AZ</td>\n",
615
- " <td>Tempe</td>\n",
616
- " <td>Phoenix-Mesa-Chandler, AZ</td>\n",
617
- " <td>Maricopa County</td>\n",
618
- " <td>2023-12-31</td>\n",
619
- " <td>NaN</td>\n",
620
- " <td>NaN</td>\n",
621
- " <td>NaN</td>\n",
622
- " <td>-1.0</td>\n",
623
- " <td>0.0</td>\n",
624
- " <td>4.5</td>\n",
625
- " </tr>\n",
626
- " <tr>\n",
627
- " <th>31852</th>\n",
628
- " <td>847854</td>\n",
629
- " <td>39992</td>\n",
630
- " <td>20598</td>\n",
631
- " <td>zip</td>\n",
632
- " <td>VA</td>\n",
633
- " <td>Arlington</td>\n",
634
- " <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
635
- " <td>Arlington County</td>\n",
636
- " <td>2023-12-31</td>\n",
637
- " <td>NaN</td>\n",
638
- " <td>NaN</td>\n",
639
- " <td>NaN</td>\n",
640
- " <td>-0.4</td>\n",
641
- " <td>0.9</td>\n",
642
- " <td>1.2</td>\n",
643
- " </tr>\n",
644
- " <tr>\n",
645
- " <th>31853</th>\n",
646
- " <td>847855</td>\n",
647
- " <td>30490</td>\n",
648
- " <td>34249</td>\n",
649
- " <td>zip</td>\n",
650
- " <td>FL</td>\n",
651
- " <td>Sarasota</td>\n",
652
- " <td>North Port-Sarasota-Bradenton, FL</td>\n",
653
- " <td>Sarasota County</td>\n",
654
- " <td>2023-12-31</td>\n",
655
- " <td>NaN</td>\n",
656
- " <td>NaN</td>\n",
657
- " <td>NaN</td>\n",
658
- " <td>-0.9</td>\n",
659
- " <td>-0.1</td>\n",
660
- " <td>5.4</td>\n",
661
- " </tr>\n",
662
- " </tbody>\n",
663
- "</table>\n",
664
- "<p>31854 rows × 15 columns</p>\n",
665
- "</div>"
666
- ],
667
- "text/plain": [
668
- " Region ID Size Rank Region RegionType State City \\\n",
669
- "0 58001 30490 501 zip NY Holtsville \n",
670
- "1 58002 30490 544 zip NY Holtsville \n",
671
- "2 58196 7440 1001 zip MA Agawam \n",
672
- "3 58197 3911 1002 zip MA Amherst \n",
673
- "4 58198 8838 1003 zip MA Amherst \n",
674
- "... ... ... ... ... ... ... \n",
675
- "31849 827279 7779 72405 zip AR Jonesboro \n",
676
- "31850 834213 30490 11437 zip NY New York \n",
677
- "31851 845914 6361 85288 zip AZ Tempe \n",
678
- "31852 847854 39992 20598 zip VA Arlington \n",
679
- "31853 847855 30490 34249 zip FL Sarasota \n",
680
- "\n",
681
- " Metro County \\\n",
682
- "0 New York-Newark-Jersey City, NY-NJ-PA Suffolk County \n",
683
- "1 New York-Newark-Jersey City, NY-NJ-PA Suffolk County \n",
684
- "2 Springfield, MA Hampden County \n",
685
- "3 Springfield, MA Hampshire County \n",
686
- "4 Springfield, MA Hampshire County \n",
687
- "... ... ... \n",
688
- "31849 Jonesboro, AR Craighead County \n",
689
- "31850 New York-Newark-Jersey City, NY-NJ-PA Queens County \n",
690
- "31851 Phoenix-Mesa-Chandler, AZ Maricopa County \n",
691
- "31852 Washington-Arlington-Alexandria, DC-VA-MD-WV Arlington County \n",
692
- "31853 North Port-Sarasota-Bradenton, FL Sarasota County \n",
693
- "\n",
694
- " Date Month Over Month % (Smoothed) (Seasonally Adjusted) \\\n",
695
- "0 2023-12-31 NaN \n",
696
- "1 2023-12-31 NaN \n",
697
- "2 2023-12-31 0.4 \n",
698
- "3 2023-12-31 0.2 \n",
699
- "4 2023-12-31 NaN \n",
700
- "... ... ... \n",
701
- "31849 2023-12-31 NaN \n",
702
- "31850 2023-12-31 NaN \n",
703
- "31851 2023-12-31 NaN \n",
704
- "31852 2023-12-31 NaN \n",
705
- "31853 2023-12-31 NaN \n",
706
- "\n",
707
- " Quarter Over Quarter % (Smoothed) (Seasonally Adjusted) \\\n",
708
- "0 NaN \n",
709
- "1 NaN \n",
710
- "2 0.9 \n",
711
- "3 0.7 \n",
712
- "4 NaN \n",
713
- "... ... \n",
714
- "31849 NaN \n",
715
- "31850 NaN \n",
716
- "31851 NaN \n",
717
- "31852 NaN \n",
718
- "31853 NaN \n",
719
- "\n",
720
- " Year Over Year % (Smoothed) (Seasonally Adjusted) Month Over Month % \\\n",
721
- "0 NaN -0.7 \n",
722
- "1 NaN -0.7 \n",
723
- "2 3.2 -0.6 \n",
724
- "3 2.7 -0.6 \n",
725
- "4 NaN -0.7 \n",
726
- "... ... ... \n",
727
- "31849 NaN -0.7 \n",
728
- "31850 NaN -0.7 \n",
729
- "31851 NaN -1.0 \n",
730
- "31852 NaN -0.4 \n",
731
- "31853 NaN -0.9 \n",
732
- "\n",
733
- " Quarter Over Quarter % Year Over Year % \n",
734
- "0 -0.9 0.6 \n",
735
- "1 -0.9 0.6 \n",
736
- "2 0.0 3.0 \n",
737
- "3 0.0 2.9 \n",
738
- "4 0.0 3.4 \n",
739
- "... ... ... \n",
740
- "31849 0.0 2.5 \n",
741
- "31850 -0.9 0.6 \n",
742
- "31851 0.0 4.5 \n",
743
- "31852 0.9 1.2 \n",
744
- "31853 -0.1 5.4 \n",
745
- "\n",
746
- "[31854 rows x 15 columns]"
747
- ]
748
- },
749
- "execution_count": 4,
750
- "metadata": {},
751
- "output_type": "execute_result"
752
  }
753
  ],
754
  "source": [
@@ -760,6 +443,7 @@
760
  " \"CountyName\": \"County\",\n",
761
  " \"BaseDate\": \"Date\",\n",
762
  " \"RegionName\": \"Region\",\n",
 
763
  " \"RegionID\": \"Region ID\",\n",
764
  " \"SizeRank\": \"Size Rank\",\n",
765
  " }\n",
@@ -767,7 +451,7 @@
767
  "\n",
768
  "# iterate over rows of final_df and populate State and City columns if the regionType is msa\n",
769
  "for index, row in final_df.iterrows():\n",
770
- " if row[\"RegionType\"] == \"msa\":\n",
771
  " regionName = row[\"Region\"]\n",
772
  " # final_df.at[index, 'Metro'] = regionName\n",
773
  "\n",
 
419
  },
420
  {
421
  "cell_type": "code",
422
+ "execution_count": 1,
423
  "metadata": {},
424
  "outputs": [
425
  {
426
+ "ename": "NameError",
427
+ "evalue": "name 'combined_df' is not defined",
428
+ "output_type": "error",
429
+ "traceback": [
430
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
431
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
432
+ "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",
433
+ "\u001b[0;31mNameError\u001b[0m: name 'combined_df' is not defined"
434
+ ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
435
  }
436
  ],
437
  "source": [
 
443
  " \"CountyName\": \"County\",\n",
444
  " \"BaseDate\": \"Date\",\n",
445
  " \"RegionName\": \"Region\",\n",
446
+ " \"RegionType\": \"Region Type\",\n",
447
  " \"RegionID\": \"Region ID\",\n",
448
  " \"SizeRank\": \"Size Rank\",\n",
449
  " }\n",
 
451
  "\n",
452
  "# iterate over rows of final_df and populate State and City columns if the regionType is msa\n",
453
  "for index, row in final_df.iterrows():\n",
454
+ " if row[\"Region Type\"] == \"msa\":\n",
455
  " regionName = row[\"Region\"]\n",
456
  " # final_df.at[index, 'Metro'] = regionName\n",
457
  "\n",
processors/{home_value_forecasts.py → home_values_forecasts.py} RENAMED
@@ -69,6 +69,7 @@ final_df = final_df.rename(
69
  "CountyName": "County",
70
  "BaseDate": "Date",
71
  "RegionName": "Region",
 
72
  "RegionID": "Region ID",
73
  "SizeRank": "Size Rank",
74
  }
@@ -76,7 +77,7 @@ final_df = final_df.rename(
76
 
77
  # iterate over rows of final_df and populate State and City columns if the regionType is msa
78
  for index, row in final_df.iterrows():
79
- if row["RegionType"] == "msa":
80
  regionName = row["Region"]
81
  # final_df.at[index, 'Metro'] = regionName
82
 
 
69
  "CountyName": "County",
70
  "BaseDate": "Date",
71
  "RegionName": "Region",
72
+ "RegionType": "Region Type",
73
  "RegionID": "Region ID",
74
  "SizeRank": "Size Rank",
75
  }
 
77
 
78
  # iterate over rows of final_df and populate State and City columns if the regionType is msa
79
  for index, row in final_df.iterrows():
80
+ if row["Region Type"] == "msa":
81
  regionName = row["Region"]
82
  # final_df.at[index, 'Metro'] = regionName
83
 
zillow.py CHANGED
@@ -88,7 +88,9 @@ class Zillow(datasets.GeneratorBasedBuilder):
88
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
89
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
90
  "Region": datasets.Value(dtype="string", id="Region"),
91
- "RegionType": datasets.Value(dtype="string", id="RegionType"),
 
 
92
  "State": datasets.Value(dtype="string", id="State"),
93
  "City": datasets.Value(dtype="string", id="City"),
94
  "Metro": datasets.Value(dtype="string", id="Metro"),
@@ -123,9 +125,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
123
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
124
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
125
  "Region": datasets.Value(dtype="string", id="Region"),
126
- "Region Type": datasets.Value(dtype="string", id="Region Type"),
 
 
127
  "State": datasets.Value(dtype="string", id="State"),
128
- "Home Type": datasets.Value(dtype="string", id="Home Type"),
 
 
129
  "Date": datasets.Value(dtype="string", id="Date"),
130
  "Median Sale Price": datasets.Value(
131
  dtype="float32", id="Median Sale Price"
@@ -142,9 +148,13 @@ class Zillow(datasets.GeneratorBasedBuilder):
142
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
143
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
144
  "Region": datasets.Value(dtype="string", id="Region"),
145
- "Region Type": datasets.Value(dtype="string", id="Region Type"),
 
 
146
  "State": datasets.Value(dtype="string", id="State"),
147
- "Home Type": datasets.Value(dtype="string", id="Home Type"),
 
 
148
  "Date": datasets.Value(dtype="string", id="Date"),
149
  "Median Listing Price": datasets.Value(
150
  dtype="float32", id="Median Listing Price"
@@ -168,9 +178,14 @@ class Zillow(datasets.GeneratorBasedBuilder):
168
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
169
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
170
  "Region": datasets.Value(dtype="string", id="Region"),
171
- "Region Type": datasets.Value(dtype="string", id="Region Type"),
 
 
172
  "State": datasets.Value(dtype="string", id="State"),
173
- "Home Type": datasets.Value(dtype="string", id="Home Type"),
 
 
 
174
  "Date": datasets.Value(dtype="string", id="Date"),
175
  "Rent (Smoothed)": datasets.Value(
176
  dtype="float32", id="Rent (Smoothed)"
@@ -186,9 +201,14 @@ class Zillow(datasets.GeneratorBasedBuilder):
186
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
187
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
188
  "Region": datasets.Value(dtype="string", id="Region"),
189
- "Region Type": datasets.Value(dtype="string", id="Region Type"),
 
 
190
  "State": datasets.Value(dtype="string", id="State"),
191
- "Home Type": datasets.Value(dtype="string", id="Home Type"),
 
 
 
192
  "Date": datasets.Value(dtype="string", id="Date"),
193
  "Mean Sale to List Ratio (Smoothed)": datasets.Value(
194
  dtype="float32", id="Mean Sale to List Ratio (Smoothed)"
@@ -232,9 +252,22 @@ class Zillow(datasets.GeneratorBasedBuilder):
232
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
233
  "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
234
  "Region": datasets.Value(dtype="string", id="Region"),
235
- "Region Type": datasets.Value(dtype="string", id="Region Type"),
236
  "State": datasets.Value(dtype="string", id="State"),
237
- "Home Type": datasets.Value(dtype="string", id="Home Type"),
 
 
 
 
 
 
 
 
 
 
 
 
 
238
  "Date": datasets.Value(dtype="string", id="Date"),
239
  "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
240
  dtype="float32",
@@ -261,7 +294,10 @@ class Zillow(datasets.GeneratorBasedBuilder):
261
  num_classes=2, names=["country", "msa"]
262
  ),
263
  "State": datasets.Value(dtype="string", id="State"),
264
- "Home Type": datasets.Value(dtype="string", id="Home Type"),
 
 
 
265
  "Date": datasets.Value(dtype="string", id="Date"),
266
  "Mean Listings Price Cut Amount (Smoothed)": datasets.Value(
267
  dtype="float32", id="Mean Listings Price Cut Amount (Smoothed)"
@@ -342,7 +378,7 @@ class Zillow(datasets.GeneratorBasedBuilder):
342
  "Region ID": data["Region ID"],
343
  "Size Rank": data["Size Rank"],
344
  "Region": data["Region"],
345
- "RegionType": data["RegionType"],
346
  "State": data["State"],
347
  "City": data["City"],
348
  "Metro": data["Metro"],
@@ -449,6 +485,7 @@ class Zillow(datasets.GeneratorBasedBuilder):
449
  "Region Type": data["Region Type"],
450
  "State": data["State"],
451
  "Home Type": data["Home Type"],
 
452
  "Date": data["Date"],
453
  "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
454
  "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)"
 
88
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
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"),
 
125
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
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"
 
148
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
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"
 
178
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
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)"
 
201
  "Region ID": datasets.Value(dtype="string", id="Region ID"),
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="string", id="Date"),
213
  "Mean Sale to List Ratio (Smoothed)": datasets.Value(
214
  dtype="float32", id="Mean Sale to List Ratio (Smoothed)"
 
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,
262
+ names=[
263
+ "1-Bedroom",
264
+ "2-Bedrooms",
265
+ "3-Bedrooms",
266
+ "4-Bedrooms",
267
+ "5+-Bedrooms",
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",
 
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)"
 
378
  "Region ID": data["Region ID"],
379
  "Size Rank": data["Size Rank"],
380
  "Region": data["Region"],
381
+ "Region Type": data["Region Type"],
382
  "State": data["State"],
383
  "City": data["City"],
384
  "Metro": data["Metro"],
 
485
  "Region Type": data["Region Type"],
486
  "State": data["State"],
487
  "Home Type": data["Home Type"],
488
+ "Bedroom Count": data["Bedroom Count"],
489
  "Date": data["Date"],
490
  "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
491
  "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)"