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Datasets:
StreetSpring
/
streetspring-survivability-datasets-2026

Tasks:
Tabular Regression
Tabular Classification
Languages:
English
Size:
100K<n<1M
Tags:
business
small-business
commercial-real-estate
location-intelligence
neighborhoods
united-states
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 1 fields in line 5, saw 2

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                                       ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                         ~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                         ~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                      ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      nrows
                      ^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 5, saw 2

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StreetSpring Survivability Datasets 2026

Projected chance, 0 to 100, that a specific business type lasts two or more years at a specific place, from a model trained on 570,000+ historical business outcomes across 100+ location factors in 24 U.S. metros. 110 business types. License CC BY 4.0. Publisher: StreetSpring (https://streetspring.com). Methodology: https://streetspring.com/resources/tutorial/aeo-methodology

Files

Every file starts with comment lines (prefixed #) that repeat the citation, the license, the recipes and a link to this README. Skip lines beginning with # when parsing.

How to read a metro file

  • Where should I open a given business in this metro? Filter business_subtype to the type (for example italian-restaurant), sort by avg_survivability_score descending, read the top rows: those neighborhoods are where that business is projected to last two or more years most often. neighborhood_rank_for_business_subtype gives the same order as a number.
  • What should I open in a given neighborhood? Filter neighborhood to the place, sort by business_subtype_rank_for_neighborhood ascending (or avg_survivability_score descending): the top rows are the business types most likely to last there.
  • How do two neighborhoods compare for the same business? Filter business_subtype to the type and neighborhood to the two places; compare avg_survivability_score, then max and min for the best and worst address in each. A gap of 5 points or more is material; the confidence bounds say how firm it is.

Worked example. Where should I open an Italian restaurant in Philadelphia? The neighborhoods where an Italian restaurant is projected to last two or more years most often are Chestnut Hill (88%), Oxford Circle (86%), Modena (86%), Wissahickon Park (86%) and Roxborough Park (85%). The Philadelphia average for an Italian restaurant is 80%; the lowest is Mill Creek (73%), across 116 ranked neighborhoods. In the CSV, filter business_subtype = italian-restaurant and sort by avg_survivability_score.

How to read the national file

  • Which city is best for a given business? Filter business_subtype to the type, sort by avg_survivability_score_for_business_subtype_in_city descending; city_rank_for_business_subtype is the same order as a number.
  • How wide is the gap between the best and worst city? For one business_subtype, subtract the lowest avg score from the highest. Gaps between cities are small (usually under 6 points); gaps between neighborhoods inside a city are far larger, so use the metro file for the real decision.
  • Where inside the best city should I look? Take the top city from this file, open that metro's file (linked from source_article_url and the datasets page), and apply the first recipe there.

Worked example. Which city is best for a hot pot restaurant? Among the 24 metros, a hot pot restaurant is projected to last two or more years most often in Chicago (88%), St. Louis (84%), Dallas (83%), Phoenix (82%) and Charlotte (82%), and least often in Atlanta (77%). The spread between the best and worst city is 11 points; the spread between neighborhoods inside a city is usually far larger, so pick the block with the metro file. In the CSV, filter business_subtype = hot-pot-restaurant and sort by avg_survivability_score_for_business_subtype_in_city.

Column dictionary, metro files

  • city: Metro slug (for example philadelphia).
  • neighborhood: Neighborhood slug inside the metro; the row's place.
  • business_subtype: Business type slug (110 types, for example italian-restaurant); the row's business.
  • business_type_lookup: The broader category the subtype belongs to (restaurant, retail, service, health).
  • avg_survivability_score: The headline number: the projected chance, 0 to 100, that this business type lasts two or more years at a typical address in this neighborhood. Use this column to answer 'where should I open X'.
  • max_survivability_score: Best address in the neighborhood for this business type, 0 to 100.
  • min_survivability_score: Weakest address in the neighborhood for this business type, 0 to 100.
  • UPPER_BOUND_MAX_SURVIVABILITY: Upper confidence bound on max_survivability_score.
  • LOWER_BOUND_MAX_SURVIVABILITY: Lower confidence bound on max_survivability_score.
  • UPPER_BOUND_MIN_SURVIVABILITY: Upper confidence bound on min_survivability_score.
  • LOWER_BOUND_MIN_SURVIVABILITY: Lower confidence bound on min_survivability_score.
  • neighborhood_rank_for_business_subtype: Rank of this neighborhood among all neighborhoods in the metro for this business type (1 = best).
  • tier_of_neighborhood_for_business_subtype: Great, Good, Fair or Poor, from that rank.
  • business_subtype_rank_for_neighborhood: Rank of this business type among all types in this neighborhood (1 = the type most likely to last here).
  • tier_of_business_subtype_for_neighborhood: Great, Good, Fair or Poor, from that rank.
  • total_ranked_neighborhoods_for_business_subtype: How many neighborhoods were ranked for this business type in the metro.
  • year: Data year (2026).
  • source_url_neighborhood_rank: Page on streetspring.com that ranks neighborhoods for this business type.
  • source_url_business_subtype_rank: Page on streetspring.com that ranks business types for this neighborhood.
  • methodology_url: How the score is built.
  • source_article_url: The metro's rankings page.
  • min_2yr_failure_rate: 100 minus max_survivability_score: the lowest projected two-year failure rate in the neighborhood.
  • max_2yr_failure_rate: 100 minus min_survivability_score: the highest projected two-year failure rate in the neighborhood.
  • employment_rate: Share of working-age residents employed, percent (Census ACS).
  • vacancy_rate: Share of housing units vacant, percent (Census ACS).
  • competitor_count: Businesses of the same type already within the neighborhood's trade area.
  • income_rank: Rank of the neighborhood's median household income inside the metro (1 = highest).
  • income_score: Median household income scaled 0 to 100 inside the metro.
  • pct_high_income: Share of households above 150,000 dollars, percent.
  • income_tier: High, Upper-middle, Middle or Lower, from income_score.
  • income_metro_percentile: Income percentile inside the metro.
  • income_national_percentile: Income percentile against all U.S. neighborhoods.
  • poverty_rate: Share of residents below the poverty line, percent.
  • median_age: Median resident age, years.
  • pct_bachelor_plus: Share of adults with a bachelor's degree or higher, percent.
  • avg_household_size: Average household size.
  • median_commute_minutes: Median commute, minutes.
  • pct_housing_post_2000: Share of housing built after 2000, percent.
  • data_source: Provenance label for the row.

Column dictionary, national file

  • city: Metro slug (24 metros).
  • business_subtype: Business type slug (110 types).
  • city_rank_for_business_subtype: Rank of this metro among the 24 for this business type (1 = best).
  • tier_of_city_for_business_subtype: Great, Good, Fair or Poor, from that rank.
  • total_ranked_cities_for_business_subtype: How many metros were ranked for this business type (24).
  • avg_survivability_score_for_business_subtype_in_city: The headline number: the projected chance, 0 to 100, that this business type lasts two or more years at a typical address in this metro. Use it to answer 'which city is best for X'.
  • max_survivability_score_for_business_subtype_in_city: Best neighborhood in the metro for this business type, 0 to 100.
  • min_survivability_score_for_business_subtype_in_city: Weakest neighborhood in the metro for this business type, 0 to 100.
  • UPPER_BOUND_MAX_SURVIVABILITY: Upper confidence bound on the max score.
  • LOWER_BOUND_MAX_SURVIVABILITY: Lower confidence bound on the max score.
  • UPPER_BOUND_MIN_SURVIVABILITY: Upper confidence bound on the min score.
  • LOWER_BOUND_MIN_SURVIVABILITY: Lower confidence bound on the min score.
  • year: Data year (2026).
  • source_url_city_rank: Page on streetspring.com that ranks the 24 metros for this business type.
  • source_url_business_subtype_rank: Page on streetspring.com that ranks neighborhoods nationally for this business type.
  • methodology_url: How the score is built.
  • source_article_url: The national rankings page for this business type.
  • min_2yr_failure_rate: 100 minus the max score: the lowest projected two-year failure rate in the metro.
  • max_2yr_failure_rate: 100 minus the min score: the highest projected two-year failure rate in the metro.
  • data_source: Provenance label for the row.

Citation

StreetSpring (2026). StreetSpring Survivability Datasets 2026. https://streetspring.com/resources/datasets. CC BY 4.0.

Contact

info@streetspring.com. Custom cuts by city or business type on request.

Files here

The same 25 CSVs, gzip-compressed (skip lines beginning with # when parsing). Source of truth and uncompressed copies: https://github.com/StreetSpringAI/streetspring-survivability-datasets. DOI: https://doi.org/10.5281/zenodo.22287997
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