File size: 26,783 Bytes
51f8985
 
 
 
 
 
 
 
 
 
 
 
 
ad34fa4
51f8985
 
 
 
 
 
 
 
 
ad34fa4
 
 
51f8985
 
 
 
ad34fa4
51f8985
 
c96e321
51f8985
ad34fa4
51f8985
 
e10c179
ad34fa4
51f8985
 
 
 
 
a1911c7
51f8985
45444a4
51f8985
416395d
a1911c7
416395d
45444a4
416395d
6c39add
 
 
45444a4
6c39add
3355ad5
 
 
45444a4
3355ad5
ebc591b
 
 
45444a4
ebc591b
 
 
 
45444a4
ebc591b
 
 
 
45444a4
ebc591b
51f8985
 
e10c179
51f8985
 
a1911c7
51f8985
 
e10c179
 
 
55b505d
 
 
416395d
 
 
 
e10c179
ef3130e
 
 
 
9cb9eef
bbb69c8
8d9c061
416395d
9cb9eef
bbb69c8
8d9c061
416395d
9cb9eef
bbb69c8
416395d
9cb9eef
a4b8e47
416395d
9cb9eef
a4b8e47
416395d
 
 
a1911c7
416395d
 
 
 
 
55b505d
 
 
416395d
55b505d
 
 
416395d
2be48a5
 
 
 
e5d0972
dea1e23
2be48a5
51f8985
 
6c39add
 
 
 
 
 
55b505d
 
 
6c39add
55b505d
 
 
6c39add
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3355ad5
 
 
 
 
 
55b505d
 
 
3355ad5
55b505d
 
 
 
3355ad5
 
 
 
 
 
 
 
 
ebc591b
 
 
 
 
 
55b505d
 
 
ebc591b
55b505d
 
 
 
011d48e
ebc591b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9cb4ac9
 
 
 
 
 
ebc591b
 
 
 
 
 
9cb4ac9
 
 
ebc591b
 
 
 
 
 
 
 
55b505d
ebc591b
55b505d
 
 
 
 
 
 
 
 
 
 
 
 
 
ebc591b
 
 
 
 
d1bd12c
 
 
 
ebc591b
 
 
 
 
 
f372b60
 
 
 
 
 
8737554
 
 
 
f372b60
55b505d
 
 
 
f372b60
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
51f8985
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
159f4ec
01b2f0e
e42b122
 
51f8985
 
 
 
 
ebc591b
51f8985
 
 
ebc591b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
51f8985
 
 
 
 
 
 
 
a1911c7
51f8985
e10c179
 
 
55b505d
416395d
 
 
 
e10c179
159f4ec
 
 
 
 
 
 
 
 
a6f83b9
 
 
416395d
a1911c7
416395d
 
 
 
 
 
 
 
2be48a5
 
 
 
 
51f8985
6c39add
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3355ad5
 
 
 
 
 
 
 
 
 
 
 
 
 
ebc591b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9cb4ac9
 
 
 
ebc591b
 
 
 
9cb4ac9
ebc591b
 
 
 
 
 
 
 
 
55b505d
ebc591b
 
 
 
d1bd12c
 
 
ebc591b
 
 
 
f372b60
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This dataset is comprised of seven different configurations of data covering different aspects of the housing market in the United States. All data is provided by Zillow. The seven configurations are: home_values_forecasts, new_construction, for_sale_listings, rentals, sales, home_values, and days_on_market. Each configuration has a different set of features and target variables. The data is provided in JSONL format."""


import json
import os

import datasets

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Housing Data},
author={Zillow},
year={2024}
}
"""

_DESCRIPTION = """\
This dataset is comprised of seven different configurations of data covering different aspects of the housing market in the United States. All data is provided by Zillow. The seven configurations are: home_values_forecasts, new_construction, for_sale_listings, rentals, sales, home_values, and days_on_market. Each configuration has a different set of features and target variables. The data is provided in JSONL format.
"""

_HOMEPAGE = "https://www.zillow.com/research/data/"

_LICENSE = "other"


class Zillow(datasets.GeneratorBasedBuilder):
    """Housing data in the United States provided by Zillow"""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="home_values_forecasts",
            version=VERSION,
            description="This data covers home value forecasts for the United States",
        ),
        datasets.BuilderConfig(
            name="new_construction",
            version=VERSION,
            description="This dataset covers new construction data for the United States",
        ),
        datasets.BuilderConfig(
            name="for_sale_listings",
            version=VERSION,
            description="This dataset covers for sale listings for the United States",
        ),
        datasets.BuilderConfig(
            name="rentals",
            version=VERSION,
            description="This dataset covers rental data for the United States",
        ),
        datasets.BuilderConfig(
            name="sales",
            version=VERSION,
            description="This dataset covers sales data for the United States",
        ),
        datasets.BuilderConfig(
            name="home_values",
            version=VERSION,
            description="This dataset covers home values for the United States",
        ),
        datasets.BuilderConfig(
            name="days_on_market",
            version=VERSION,
            description="This dataset covers days-on-market data for the United States",
        ),
    ]

    DEFAULT_CONFIG_NAME = ""

    def _info(self):
        if self.config.name == "home_values_forecasts":
            features = datasets.Features(
                {
                    "Region ID": datasets.Value(dtype="string", id="Region ID"),
                    "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
                    "Region": datasets.Value(dtype="string", id="Region"),
                    "Region Type": datasets.ClassLabel(
                        num_classes=3, names=["zip", "country", "msa"]
                    ),
                    "State": datasets.Value(dtype="string", id="State"),
                    "City": datasets.Value(dtype="string", id="City"),
                    "Metro": datasets.Value(dtype="string", id="Metro"),
                    "County": datasets.Value(dtype="string", id="County"),
                    "Date": datasets.Value(dtype="string", id="Date"),
                    "Month Over Month % (Smoothed) (Seasonally Adjusted)": datasets.Value(
                        dtype="float32",
                        id="Month Over Month % (Smoothed) (Seasonally Adjusted)",
                    ),
                    "Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)": datasets.Value(
                        dtype="float32",
                        id="Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)",
                    ),
                    "Year Over Year % (Smoothed) (Seasonally Adjusted)": datasets.Value(
                        dtype="float32",
                        id="Year Over Year % (Smoothed) (Seasonally Adjusted)",
                    ),
                    "Month Over Month %": datasets.Value(
                        dtype="float32", id="Month Over Month %"
                    ),
                    "Quarter Over Quarter %": datasets.Value(
                        dtype="float32", id="Quarter Over Quarter %"
                    ),
                    "Year Over Year %": datasets.Value(
                        dtype="float32", id="Year Over Year %"
                    ),
                }
            )
        elif self.config.name == "new_construction":
            features = datasets.Features(
                {
                    "Region ID": datasets.Value(dtype="string", id="Region ID"),
                    "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
                    "Region": datasets.Value(dtype="string", id="Region"),
                    "Region Type": datasets.ClassLabel(
                        num_classes=2, names=["country", "msa"]
                    ),
                    "State": datasets.Value(dtype="string", id="State"),
                    "Home Type": datasets.ClassLabel(
                        num_classes=3, names=["SFR", "all homes", "condo/co-op only"]
                    ),
                    "Date": datasets.Value(dtype="string", id="Date"),
                    "Median Sale Price": datasets.Value(
                        dtype="float32", id="Median Sale Price"
                    ),
                    "Median Sale Price per Sqft": datasets.Value(
                        dtype="float32", id="Sale Price per Sqft"
                    ),
                    "Sales Count": datasets.Value(dtype="int32", id="Sales Count"),
                }
            )
        elif self.config.name == "for_sale_listings":
            features = datasets.Features(
                {
                    "Region ID": datasets.Value(dtype="string", id="Region ID"),
                    "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
                    "Region": datasets.Value(dtype="string", id="Region"),
                    "Region Type": datasets.ClassLabel(
                        num_classes=2, names=["country", "msa"]
                    ),
                    "State": datasets.Value(dtype="string", id="State"),
                    "Home Type": datasets.ClassLabel(
                        num_classes=2, names=["SFR", "all homes"]
                    ),
                    "Date": datasets.Value(dtype="string", id="Date"),
                    "Median Listing Price": datasets.Value(
                        dtype="float32", id="Median Listing Price"
                    ),
                    "Median Listing Price (Smoothed)": datasets.Value(
                        dtype="float32", id="Median Listing Price (Smoothed)"
                    ),
                    "New Listings": datasets.Value(dtype="int32", id="New Listings"),
                    "New Listings (Smoothed)": datasets.Value(
                        dtype="int32", id="New Listings (Smoothed)"
                    ),
                    "New Pending (Smoothed)": datasets.Value(
                        dtype="int32", id="New Pending (Smoothed)"
                    ),
                    "New Pending": datasets.Value(dtype="int32", id="New Pending"),
                }
            )
        elif self.config.name == "rentals":
            features = datasets.Features(
                {
                    "Region ID": datasets.Value(dtype="string", id="Region ID"),
                    "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
                    "Region": datasets.Value(dtype="string", id="Region"),
                    "Region Type": datasets.ClassLabel(
                        num_classes=5, names=["county", "city", "zip", "country", "msa"]
                    ),
                    "State": datasets.Value(dtype="string", id="State"),
                    "Home Type": datasets.ClassLabel(
                        num_classes=3,
                        names=["all homes plus multifamily", "SFR", "multifamily"],
                    ),
                    "Date": datasets.Value(dtype="string", id="Date"),
                    "Rent (Smoothed)": datasets.Value(
                        dtype="float32", id="Rent (Smoothed)"
                    ),
                    "Rent (Smoothed) (Seasonally Adjusted)": datasets.Value(
                        dtype="float32", id="Rent (Smoothed) (Seasonally Adjusted)"
                    ),
                }
            )
        elif self.config.name == "sales":
            features = datasets.Features(
                {
                    "Region ID": datasets.Value(dtype="string", id="Region ID"),
                    "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
                    "Region": datasets.Value(dtype="string", id="Region"),
                    "Region Type": datasets.ClassLabel(
                        num_classes=2, names=["country", "msa"]
                    ),
                    "State": datasets.Value(dtype="string", id="State"),
                    "Home Type": datasets.ClassLabel(
                        num_classes=2,
                        names=["SFR", "all homes"],
                    ),
                    "Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
                    "Mean Sale to List Ratio (Smoothed)": datasets.Value(
                        dtype="float32", id="Mean Sale to List Ratio (Smoothed)"
                    ),
                    "Median Sale to List Ratio": datasets.Value(
                        dtype="float32", id="Median Sale to List Ratio"
                    ),
                    "Median Sale Price": datasets.Value(
                        dtype="float32", id="Median Sale Price"
                    ),
                    "Median Sale Price (Smoothed) (Seasonally Adjusted)": datasets.Value(
                        dtype="float32",
                        id="Median Sale Price (Smoothed) (Seasonally Adjusted)",
                    ),
                    "Median Sale Price (Smoothed)": datasets.Value(
                        dtype="float32", id="Median Sale Price (Smoothed)"
                    ),
                    "Median Sale to List Ratio (Smoothed)": datasets.Value(
                        dtype="float32", id="Median Sale to List Ratio (Smoothed)"
                    ),
                    "% Sold Below List": datasets.Value(
                        dtype="float32", id="% Sold Below List"
                    ),
                    "% Sold Below List (Smoothed)": datasets.Value(
                        dtype="float32", id="% Sold Below List (Smoothed)"
                    ),
                    "% Sold Above List": datasets.Value(
                        dtype="float32", id="% Sold Above List"
                    ),
                    "% Sold Above List (Smoothed)": datasets.Value(
                        dtype="float32", id="% Sold Above List (Smoothed)"
                    ),
                    "Mean Sale to List Ratio": datasets.Value(
                        dtype="float32", id="Mean Sale to List Ratio"
                    ),
                }
            )
        elif self.config.name == "home_values":
            features = datasets.Features(
                {
                    "Region ID": datasets.Value(dtype="string", id="Region ID"),
                    "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
                    "Region": datasets.Value(dtype="string", id="Region"),
                    "Region Type": datasets.ClassLabel(num_classes=1, names=["state"]),
                    "State": datasets.Value(dtype="string", id="State"),
                    "Home Type": datasets.ClassLabel(
                        num_classes=3, names=["all homes (SFR/condo)", "SFR", "condo"]
                    ),
                    "Bedroom Count": datasets.ClassLabel(
                        num_classes=6,
                        names=[
                            "1-Bedroom",
                            "2-Bedrooms",
                            "3-Bedrooms",
                            "4-Bedrooms",
                            "5+-Bedrooms",
                            "All Bedrooms",
                        ],
                    ),
                    "Date": datasets.Value(dtype="string", id="Date"),
                    "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
                        dtype="float32",
                        id="Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)",
                    ),
                    "Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
                        dtype="float32",
                        id="Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)",
                    ),
                    "Top Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
                        dtype="float32",
                        id="Top Tier ZHVI (Smoothed) (Seasonally Adjusted)",
                    ),
                }
            )
        elif self.config.name == "days_on_market":
            features = datasets.Features(
                {
                    "Region ID": datasets.Value(dtype="string", id="Region ID"),
                    "Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
                    "Region": datasets.Value(dtype="string", id="Region"),
                    # "Region Type": datasets.Value(dtype="string", id="Region Type"),
                    "Region Type": datasets.ClassLabel(
                        num_classes=2, names=["country", "msa"]
                    ),
                    "State": datasets.Value(dtype="string", id="State"),
                    # "Home Type": datasets.Value(dtype="string", id="Home Type"),
                    "Home Type": datasets.ClassLabel(
                        num_classes=2, names=["SFR", "all homes (SFR + Condo)"]
                    ),
                    "Date": datasets.Value(dtype="string", id="Date"),
                    "Mean Listings Price Cut Amount (Smoothed)": datasets.Value(
                        dtype="float32", id="Mean Listings Price Cut Amount (Smoothed)"
                    ),
                    "Percent Listings Price Cut": datasets.Value(
                        dtype="float32", id="Percent Listings Price Cut"
                    ),
                    "Mean Listings Price Cut Amount": datasets.Value(
                        dtype="float32", id="Mean Listings Price Cut Amount"
                    ),
                    "Percent Listings Price Cut (Smoothed)": datasets.Value(
                        dtype="float32", id="Percent Listings Price Cut (Smoothed)"
                    ),
                    "Median Days on Pending (Smoothed)": datasets.Value(
                        dtype="float32", id="Median Days on Pending (Smoothed)"
                    ),
                    "Median Days on Pending": datasets.Value(
                        dtype="float32", id="Median Days on Pending"
                    ),
                }
            )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        file_path = os.path.join("processed", self.config.name, "final5.jsonl")
        file_train = dl_manager.download(file_path)
        # file_test = dl_manager.download(os.path.join(self.config.name, "test.csv"))
        # file_eval = dl_manager.download(os.path.join(self.config.name, "valid.csv"))
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": file_train,
                    "split": "train",
                },
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.VALIDATION,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": file_train,  # os.path.join(data_dir, "dev.jsonl"),
            #         "split": "dev",
            #     },
            # ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.TEST,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": file_train,  # os.path.join(data_dir, "test.jsonl"),
            #         "split": "test",
            #     },
            # ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                if self.config.name == "home_values_forecasts":
                    yield key, {
                        "Region ID": data["Region ID"],
                        "Size Rank": data["Size Rank"],
                        "Region": data["Region"],
                        "Region Type": data["Region Type"],
                        "State": data["State"],
                        "City": data["City"],
                        "Metro": data["Metro"],
                        "County": data["County"],
                        "Date": data["Date"],
                        "Month Over Month % (Smoothed) (Seasonally Adjusted)": data[
                            "Month Over Month % (Smoothed) (Seasonally Adjusted)"
                        ],
                        "Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)": data[
                            "Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)"
                        ],
                        "Year Over Year % (Smoothed) (Seasonally Adjusted)": data[
                            "Year Over Year % (Smoothed) (Seasonally Adjusted)"
                        ],
                        "Month Over Month %": data["Month Over Month %"],
                        "Quarter Over Quarter %": data["Quarter Over Quarter %"],
                        "Year Over Year %": data["Year Over Year %"],
                    }
                elif self.config.name == "new_construction":
                    yield key, {
                        "Region ID": data["Region ID"],
                        "Size Rank": data["Size Rank"],
                        "Region": data["Region"],
                        "Region Type": data["Region Type"],
                        "State": data["State"],
                        "Home Type": data["Home Type"],
                        "Date": data["Date"],
                        "Median Sale Price": data["Median Sale Price"],
                        "Median Sale Price per Sqft": data[
                            "Median Sale Price per Sqft"
                        ],
                        "Sales Count": data["Sales Count"],
                    }
                elif self.config.name == "for_sale_listings":
                    yield key, {
                        "Region ID": data["Region ID"],
                        "Size Rank": data["Size Rank"],
                        "Region": data["Region"],
                        "Region Type": data["Region Type"],
                        "State": data["State"],
                        "Home Type": data["Home Type"],
                        "Date": data["Date"],
                        "Median Listing Price": data["Median Listing Price"],
                        "Median Listing Price (Smoothed)": data[
                            "Median Listing Price (Smoothed)"
                        ],
                        "New Listings": data["New Listings"],
                        "New Listings (Smoothed)": data["New Listings (Smoothed)"],
                        "New Pending (Smoothed)": data["New Pending (Smoothed)"],
                        "New Pending": data["New Pending"],
                    }
                elif self.config.name == "rentals":
                    yield key, {
                        "Region ID": data["Region ID"],
                        "Size Rank": data["Size Rank"],
                        "Region": data["Region"],
                        "Region Type": data["Region Type"],
                        "State": data["State"],
                        "Home Type": data["Home Type"],
                        "Date": data["Date"],
                        "Rent (Smoothed)": data["Rent (Smoothed)"],
                        "Rent (Smoothed) (Seasonally Adjusted)": data[
                            "Rent (Smoothed) (Seasonally Adjusted)"
                        ],
                    }
                elif self.config.name == "sales":
                    yield key, {
                        "Region ID": data["Region ID"],
                        "Size Rank": data["Size Rank"],
                        "Region": data["Region"],
                        "Region Type": data["Region Type"],
                        "State": data["State"],
                        "Home Type": data["Home Type"],
                        "Date": data["Date"],
                        "Mean Sale to List Ratio (Smoothed)": data[
                            "Mean Sale to List Ratio (Smoothed)"
                        ],
                        "Median Sale to List Ratio": data["Median Sale to List Ratio"],
                        "Median Sale Price": data["Median Sale Price"],
                        "Median Sale Price (Smoothed) (Seasonally Adjusted)": data[
                            "Median Sale Price (Smoothed) (Seasonally Adjusted)"
                        ],
                        "Median Sale Price (Smoothed)": data[
                            "Median Sale Price (Smoothed)"
                        ],
                        "Median Sale to List Ratio (Smoothed)": data[
                            "Median Sale to List Ratio (Smoothed)"
                        ],
                        "% Sold Below List": data["% Sold Below List"],
                        "% Sold Below List (Smoothed)": data[
                            "% Sold Below List (Smoothed)"
                        ],
                        "% Sold Above List": data["% Sold Above List"],
                        "% Sold Above List (Smoothed)": data[
                            "% Sold Above List (Smoothed)"
                        ],
                        "Mean Sale to List Ratio": data["Mean Sale to List Ratio"],
                    }
                elif self.config.name == "home_values":
                    yield key, {
                        "Region ID": data["Region ID"],
                        "Size Rank": data["Size Rank"],
                        "Region": data["Region"],
                        "Region Type": data["Region Type"],
                        "State": data["State"],
                        "Home Type": data["Home Type"],
                        "Bedroom Count": data["Bedroom Count"],
                        "Date": data["Date"],
                        "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
                            "Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)"
                        ],
                        "Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
                            "Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)"
                        ],
                        "Top Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
                            "Top Tier ZHVI (Smoothed) (Seasonally Adjusted)"
                        ],
                    }
                elif self.config.name == "days_on_market":
                    yield key, {
                        "Region ID": data["Region ID"],
                        "Size Rank": data["Size Rank"],
                        "Region": data["Region"],
                        "Region Type": data["Region Type"],
                        "State": data["State"],
                        "Home Type": data["Home Type"],
                        "Date": data["Date"],
                        "Mean Listings Price Cut Amount (Smoothed)": data[
                            "Mean Listings Price Cut Amount (Smoothed)"
                        ],
                        "Percent Listings Price Cut": data[
                            "Percent Listings Price Cut"
                        ],
                        "Mean Listings Price Cut Amount": data[
                            "Mean Listings Price Cut Amount"
                        ],
                        "Percent Listings Price Cut (Smoothed)": data[
                            "Percent Listings Price Cut (Smoothed)"
                        ],
                        "Median Days on Pending (Smoothed)": data[
                            "Median Days on Pending (Smoothed)"
                        ],
                        "Median Days on Pending": data["Median Days on Pending"],
                    }