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zip int64 1.23k 94.2k ⌀ | latitude float64 37.7 42.4 ⌀ | longitude float64 -122.51 -70.93 ⌀ | price float64 125k 40M ⌀ | emb_0 float32 -0.11 0.15 ⌀ | emb_1 float32 -0.15 0.12 ⌀ | emb_2 float32 -0.08 0.14 ⌀ | emb_3 float32 -0.1 0.11 ⌀ | emb_4 float32 -0.16 0.11 ⌀ | emb_5 float32 -0.09 0.13 ⌀ | emb_6 float32 -0.16 0.11 ⌀ | emb_7 float32 -0.12 0.11 ⌀ | emb_8 float32 -0.17 0.08 ⌀ | emb_9 float32 -0.14 0.09 ⌀ | emb_10 float32 -0.14 0.11 ⌀ | emb_11 float32 -0.17 0.13 ⌀ | emb_12 float32 -0.1 0.12 ⌀ | emb_13 float32 -0.14 0.17 ⌀ | emb_14 float32 -0.08 0.18 ⌀ | emb_15 float32 -0.11 0.17 ⌀ | emb_16 float32 -0.05 0.22 ⌀ | emb_17 float32 -0.14 0.09 ⌀ | emb_18 float32 -0.15 0.14 ⌀ | emb_19 float32 -0.09 0.14 ⌀ | emb_20 float32 -0.11 0.13 ⌀ | emb_21 float32 -0.13 0.08 ⌀ | emb_22 float32 -0.09 0.12 ⌀ | emb_23 float32 -0.11 0.12 ⌀ | emb_24 float32 -0.11 0.11 ⌀ | emb_25 float32 -0.07 0.17 ⌀ | emb_26 float32 -0.13 0.11 ⌀ | emb_27 float32 -0.07 0.19 ⌀ | emb_28 float32 -0.1 0.1 ⌀ | emb_29 float32 -0.15 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float32 -0.11 0.14 ⌀ | emb_195 float32 -0.1 0.16 ⌀ | emb_196 float32 -0.11 0.13 ⌀ | emb_197 float32 -0.13 0.13 ⌀ | emb_198 float32 -0.15 0.12 ⌀ | emb_199 float32 -0.15 0.08 ⌀ | emb_200 float32 -0.07 0.16 ⌀ | emb_201 float32 -0.12 0.1 ⌀ | emb_202 float32 -0.11 0.13 ⌀ | emb_203 float32 -0.1 0.16 ⌀ | emb_204 float32 -0.17 0.06 ⌀ | emb_205 float32 -0.11 0.18 ⌀ | emb_206 float32 -0.09 0.16 ⌀ | emb_207 float32 -0.12 0.13 ⌀ | emb_208 float32 -0.14 0.14 ⌀ | emb_209 float32 -0.09 0.14 ⌀ | emb_210 float32 -0.12 0.14 ⌀ | emb_211 float32 -0.06 0.18 ⌀ | emb_212 float32 -0.07 0.17 ⌀ | emb_213 float32 -0.15 0.1 ⌀ | emb_214 float32 -0.1 0.15 ⌀ | emb_215 float32 -0.13 0.1 ⌀ | emb_216 float32 -0.18 0.11 ⌀ | emb_217 float32 -0.14 0.11 ⌀ | emb_218 float32 -0.12 0.12 ⌀ | emb_219 float32 -0.13 0.1 ⌀ | emb_220 float32 -0.12 0.2 ⌀ | emb_221 float32 -0.15 0.1 ⌀ | emb_222 float32 -0.17 0.09 ⌀ | emb_223 float32 -0.04 0.06 ⌀ | emb_224 float32 -0.09 0.15 ⌀ | emb_225 float32 -0.15 0.16 ⌀ | emb_226 float32 -0.14 0.09 ⌀ | emb_227 float32 -0.14 0.11 ⌀ | emb_228 float32 -0.12 0.11 ⌀ | emb_229 float32 -0.11 0.12 ⌀ | emb_230 float32 -0.14 0.11 ⌀ | emb_231 float32 -0.15 0.06 ⌀ | emb_232 float32 -0.11 0.12 ⌀ | emb_233 float32 -0.1 0.14 ⌀ | emb_234 float32 -0.2 0.09 ⌀ | emb_235 float32 -0.09 0.14 ⌀ | emb_236 float32 -0.11 0.16 ⌀ | emb_237 float32 -0.13 0.13 ⌀ | emb_238 float32 -0.12 0.14 ⌀ | emb_239 float32 -0.09 0.13 ⌀ | emb_240 float32 -0.09 0.16 ⌀ | emb_241 float32 -0.15 0.09 ⌀ | emb_242 float32 -0.15 0.12 ⌀ | emb_243 float32 -0.11 0.12 ⌀ | emb_244 float32 -0.12 0.15 ⌀ | emb_245 float32 -0.1 0.21 ⌀ | emb_246 float32 -0.19 0.13 ⌀ | emb_247 float32 -0.13 0.13 ⌀ | emb_248 float32 -0.12 0.12 ⌀ | emb_249 float32 -0.06 0.16 ⌀ | emb_250 float32 -0.16 0.1 ⌀ | emb_251 float32 -0.07 0.18 ⌀ | emb_252 float32 -0.14 0.13 ⌀ | emb_253 float32 -0.09 0.13 ⌀ | emb_254 float32 -0.2 0.08 ⌀ | emb_255 float32 -0.15 0.14 ⌀ | emb_256 float32 -0.13 0.12 ⌀ | emb_257 float32 -0.13 0.11 ⌀ | emb_258 float32 -0.11 0.11 ⌀ | emb_259 float32 -0.16 0.12 ⌀ | emb_260 float32 -0.14 0.13 ⌀ | emb_261 float32 -0.12 0.1 ⌀ | emb_262 float32 -0.2 0.08 ⌀ | emb_263 float32 -0.11 0.17 ⌀ | emb_264 float32 -0.06 0.14 ⌀ | emb_265 float32 -0.18 0.06 ⌀ | emb_266 float32 -0.12 0.14 ⌀ | emb_267 float32 -0.18 0.19 ⌀ | emb_268 float32 -0.1 0.13 ⌀ | emb_269 float32 -0.11 0.11 ⌀ | emb_270 float32 -0.13 0.11 ⌀ | emb_271 float32 -0.19 0.09 ⌀ | emb_272 float32 -0.14 0.13 ⌀ | emb_273 float32 -0.1 0.17 ⌀ | emb_274 float32 -0.11 0.13 ⌀ | emb_275 float32 -0.11 0.15 ⌀ | emb_276 float32 -0.18 0.11 ⌀ | emb_277 float32 -0.12 0.16 ⌀ | emb_278 float32 -0.1 0.1 ⌀ | emb_279 float32 -0.11 0.11 ⌀ | emb_280 float32 -0.11 0.13 ⌀ | emb_281 float32 -0.12 0.14 ⌀ | emb_282 float32 -0.19 0.12 ⌀ | emb_283 float32 -0.12 0.16 ⌀ | emb_284 float32 -0.08 0.16 ⌀ | emb_285 float32 -0.09 0.15 ⌀ | emb_286 float32 -0.13 0.11 ⌀ | emb_287 float32 -0.12 0.2 ⌀ | emb_288 float32 -0.1 0.11 ⌀ | emb_289 float32 -0.2 0.16 ⌀ | emb_290 float32 -0.14 0.14 ⌀ | emb_291 float32 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emb_324 float32 -0.12 0.16 ⌀ | emb_325 float32 -0.19 0.07 ⌀ | emb_326 float32 -0.06 0.19 ⌀ | emb_327 float32 -0.09 0.17 ⌀ | emb_328 float32 -0.12 0.11 ⌀ | emb_329 float32 -0.11 0.14 ⌀ | emb_330 float32 -0.11 0.15 ⌀ | emb_331 float32 -0.15 0.11 ⌀ | emb_332 float32 -0.13 0.12 ⌀ | emb_333 float32 -0.1 0.12 ⌀ | emb_334 float32 -0.15 0.09 ⌀ | emb_335 float32 -0.15 0.12 ⌀ | emb_336 float32 -0.12 0.14 ⌀ | emb_337 float32 -0.15 0.16 ⌀ | emb_338 float32 -0.14 0.04 ⌀ | emb_339 float32 -0.13 0.13 ⌀ | emb_340 float32 -0.09 0.12 ⌀ | emb_341 float32 -0.13 0.09 ⌀ | emb_342 float32 -0.1 0.16 ⌀ | emb_343 float32 -0.09 0.17 ⌀ | emb_344 float32 -0.14 0.08 ⌀ | emb_345 float32 -0.15 0.09 ⌀ | emb_346 float32 -0.14 0.11 ⌀ | emb_347 float32 -0.1 0.14 ⌀ | emb_348 float32 -0.1 0.17 ⌀ | emb_349 float32 -0.1 0.14 ⌀ | emb_350 float32 -0.12 0.13 ⌀ | emb_351 float32 -0.11 0.13 ⌀ | emb_352 float32 -0.12 0.17 ⌀ | emb_353 float32 -0.09 0.14 ⌀ | emb_354 float32 -0.15 0.1 ⌀ | emb_355 float32 -0.12 0.12 ⌀ | emb_356 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2,131 | null | null | null | 0.05227 | -0.066211 | 0.020232 | 0.013271 | -0.004055 | -0.067605 | 0.041555 | -0.02534 | -0.068783 | -0.046322 | -0.000544 | 0.041482 | -0.053169 | -0.016476 | 0.018793 | -0.040226 | 0.063035 | -0.041345 | 0.063703 | 0.086452 | -0.053492 | -0.104322 | 0.01086 | 0.001586 | 0.021307 | 0.059447 | -0.003581 | 0.065619 | 0.002155 | -0.072054 | 0.107938 | 0.078091 | 0.084641 | -0.091556 | -0.043481 | 0.028905 | 0.07989 | -0.026313 | 0.039729 | 0.030624 | -0.022508 | 0.02181 | -0.014235 | 0.080318 | -0.023126 | -0.107408 | -0.01765 | -0.093912 | 0.027591 | -0.02664 | 0.002556 | 0.070857 | -0.042347 | 0.025221 | -0.043597 | 0.001278 | -0.064484 | -0.042702 | 0.03908 | 0.03284 | 0.102829 | 0.000091 | -0.073544 | 0.007316 | -0.012352 | -0.062862 | -0.044544 | 0.00127 | 0.005459 | -0.085404 | 0.008869 | 0.029228 | -0.047896 | 0.000248 | 0.005168 | -0.062031 | 0.009549 | -0.077866 | -0.04248 | 0.105123 | -0.017724 | -0.006439 | -0.083919 | 0.029288 | -0.025291 | -0.105266 | -0.004287 | 0.03616 | 0.058521 | -0.008459 | -0.031362 | 0.057346 | -0.080454 | 0.014754 | -0.015986 | -0.022047 | 0.004964 | -0.064728 | -0.041618 | 0.094756 | -0.025986 | -0.007327 | 0.092798 | 0.027936 | 0.009393 | -0.042892 | 0.009013 | 0.114114 | -0.027638 | 0.012968 | -0.015438 | -0.02634 | 0.067198 | -0.044598 | 0.027675 | -0.044885 | 0.018499 | 0.040052 | 0.049603 | 0.0359 | 0.05402 | 0.079413 | -0.029666 | 0.024255 | 0.056066 | -0.045231 | -0.015362 | 0 | 0.014984 | 0.082724 | -0.015021 | 0.106742 | 0.06485 | 0.064885 | -0.07111 | 0.03521 | -0.019686 | 0.072098 | 0.085389 | -0.023647 | -0.02658 | -0.009208 | 0.009613 | 0.066288 | 0.012918 | -0.053718 | -0.062281 | -0.02323 | 0.005777 | 0.002994 | -0.064467 | 0.058481 | -0.018223 | -0.066089 | 0.074533 | -0.007857 | -0.002197 | 0.031162 | -0.007314 | 0.00915 | 0.06485 | 0.019181 | 0.028462 | 0.085688 | -0.048009 | -0.131089 | -0.007861 | -0.029339 | -0.014536 | -0.006653 | -0.016273 | 0.12924 | -0.004421 | 0.13057 | 0.071896 | 0.046018 | 0.00351 | -0.005752 | -0.016345 | 0.008926 | -0.135081 | 0.151947 | -0.089363 | 0.010535 | -0.067745 | 0.027771 | 0.129371 | 0.009111 | 0.046512 | 0.026656 | 0.01247 | 0.001593 | 0.000594 | -0.038622 | 0.040509 | -0.016389 | 0.071603 | 0.035781 | -0.001149 | 0.014325 | -0.019261 | 0.005945 | 0.024365 | -0.004498 | -0.011349 | -0.00936 | 0.031468 | 0.026514 | 0.00826 | 0.005801 | -0.053688 | 0.031768 | 0.062952 | -0.055205 | 0.065353 | -0.044312 | -0.073064 | 0.021054 | 0.051915 | -0.024588 | 0.036626 | -0.046856 | -0.056562 | -0 | 0.042494 | -0.121965 | -0.02177 | -0.048367 | -0.028407 | 0.045193 | -0.040296 | 0.031513 | -0.012046 | 0.00666 | -0.091349 | 0.019673 | 0.095605 | -0.017969 | 0.006158 | 0.012333 | 0.100293 | -0.059876 | -0.066766 | 0.017339 | -0.096164 | 0.085432 | -0.043653 | 0.020425 | -0.005127 | 0.080746 | -0.04849 | 0.008413 | -0.087384 | 0.006377 | -0.049283 | -0.02567 | -0.046246 | -0.026226 | -0.009824 | 0.108308 | 0.028365 | 0.000438 | -0.093837 | 0.059584 | -0.016366 | -0.085094 | 0.013109 | 0.020024 | 0.006273 | -0.038163 | -0.017565 | -0.019477 | -0.017279 | -0.017452 | 0.026744 | 0.078597 | -0.076533 | 0.052845 | 0.060796 | -0.057446 | 0.003722 | -0.00471 | -0.008489 | -0.001489 | 0.042426 | 0.013594 | -0.006555 | 0.090046 | 0.017533 | -0.032672 | -0.016028 | -0.060389 | -0.052779 | -0.001581 | -0.132888 | 0.03382 | 0.007099 | -0.032287 | -0.021325 | 0.048292 | 0.073908 | -0.037215 | -0.025873 | -0.034248 | 0.015627 | -0.040472 | 0.017287 | 0.035865 | -0.032989 | -0.023047 | -0.035106 | 0.075 | -0.000552 | 0.056947 | -0.032811 | 0.063844 | -0.121899 | -0.036695 | 0.086609 | -0 | -0.067148 | 0.032175 | -0.059343 | 0.012029 | -0.039529 | 0.073042 | 0.053963 | 0.037574 | -0.008263 | 0.067916 | 0.027162 | 0.031678 | -0.000255 | 0.019872 | -0.005798 | -0.006529 | -0.013447 | -0.022028 | -0.039538 | 0.050109 | 0.074593 | 0.014075 | -0.002416 | 0.036859 | -0.061456 | -0.024724 | -0.031658 | 0.001539 | 0.021272 | -0.060503 | 0.040157 | 0.026868 | 0.017469 | 0.034485 | -0.074546 | -0.027703 | 0.040623 | 0.011371 | -0.043111 | 0.010958 | 0.032329 | -0.146322 | -0.082675 | 0.010559 | -0.096023 | -0.00257 | 0.020114 | 0.010273 | 0.061467 | 0.012671 | 0.013893 | 0.047427 | 0.015152 | 0.021899 | 0.030643 | -0.055022 | -0.111267 | 0.037534 | -0.035703 | 0.034608 | 0.004282 | -0.046607 | -0.082256 | 0.014758 |
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Causal Real Estate
Multi-city parcel-level dataset and text embeddings for studying spatial confounding in real estate valuation, released alongside Causal Disentanglement of Location and Semantic Signals in Real Estate Valuation (Yee & Crainic, 2026).
The dataset combines property assessments, census demographics, geocoded crime incidents, and points-of-interest amenities for 556,636 parcels across Boston, New York (Manhattan + Brooklyn), and San Francisco. A held-out subset of ~3,000 parcels also ships with sentence-transformer embeddings of listing descriptions.
Contents
data/
boston/ parcels.parquet embeddings_mpnet.parquet embeddings_minilm.parquet
nyc/ parcels.parquet embeddings_mpnet.parquet embeddings_minilm.parquet
sf/ parcels.parquet embeddings_mpnet.parquet embeddings_minilm.parquet
splits/temporal_splits.csv parcel_id → train / val / test (NYC, SF only)
scripts/make_panel.py rebuilds these parquets from raw sources
scripts/fetch_descriptions.py re-derives raw text from your own Redfin access
scripts/regenerate_embeddings.py
| City | Parcels | With description / embeddings | Median price |
|---|---|---|---|
| Boston | 87,340 | 997 | $812,800 (assessed) |
| NYC (Manhattan + Brooklyn) | 242,062 | 990 | $995,000 (sale) |
| San Francisco | 227,677 | 995 | $1,283,160 (inferred sale) |
Schema
parcels.parquet per city. Common 34-covariate confounder set used in the paper:
- Property:
bedrooms,lot_area_sqft,bldg_area_sqft,year_built,assessed_* - Location:
latitude,longitude,GEOID(block group) - Census (ACS 5yr 2022):
median_household_income,median_home_value,median_gross_rent,pct_white,pct_black,pct_asian,pct_hispanic,pct_bachelors,labor_force_participation,pct_under_25,pct_over_60 - Crime (500m radius):
crime_violent,crime_property,crime_quality_of_life,crime_other,crime_total - Amenities (counts and densities):
amenity_food_dining,amenity_retail,amenity_services,amenity_recreation,amenity_transportation,amenity_education,amenity_total,amenity_diversity, plus*_densityversions - Micro-geography:
dist_park_m,dist_transit_m,dist_school_m,dist_restaurant_m,dist_retail_m,dist_medical_m
City-specific extras (NYC sale dates, SF assessor metadata) are documented in DATASHEET.md.
embeddings_*.parquet per city: latitude, longitude, zip, price, plus emb_0..emb_N
where N=767 (mpnet, 768-dim) or N=383 (MiniLM, 384-dim).
Loading
from datasets import load_dataset
ds = load_dataset("jcrainic2/causal-real-estate", "nyc-parcels")
# or directly:
import pandas as pd
parcels = pd.read_parquet("hf://datasets/jcrainic2/causal-real-estate/data/nyc/parcels.parquet")
What is not included, and why
Raw listing descriptions are not redistributed. The descriptions used to
produce the embeddings were retrieved from Redfin, whose Terms of Service prohibit
redistribution; the remarks themselves carry MLS copyright. To reproduce the
text-side analysis in full, run scripts/fetch_descriptions.py against your own
Redfin access. The pre-computed embeddings shipped here are derivative
representations and are released under CC BY 4.0.
Street addresses are not included even where they appear in source assessor rolls (notably SF). The paper's analysis uses lat/lon at parcel-centroid precision; addresses are unnecessary for replication.
License
- Structured features and embeddings: CC BY 4.0
- Code (
scripts/): MIT - Source attributions: see
DATASHEET.md§ Source Datasets
If you use this dataset, please cite:
@article{yee2026causal,
title={Causal Disentanglement of Location and Semantic Signals in Real Estate Valuation},
author={Yee, Brandon and Crainic, Jacob},
journal={Journal of Business and Economic Statistics},
year={2026},
note={Under review}
}
Reproducing the paper
The companion code repository contains the full pipeline (run_pipeline.py) including DML, doubly-robust estimation,
adversarial deconfounding, and the confounder escalation test. This dataset is
the input.
Contact
{b.yee, j.crainic}@ycrg-labs.org
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