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Philadelphia Housing Data · Nissim Lebovits · Source Cooperative | Source Cooperative

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AGENTS.md — Affordable Housing Production

Name: Philadelphia Housing Data
Creator: Nissim Lebovits

Guidance for AI agents and LLMs working with this collection.

Overview

Affordable housing projects funded by the Division of Housing and Community Development and completed since 1994. 501 projects accounting for 19,249 units, spanning fiscal years 1995 through 2026.

This is the only point collection in the catalog.

Accessing the data

Coordinates are EPSG:3857 (Web Mercator) metres. DuckDB reads geometry natively, so do not wrap it in ST_GeomFromWKB.

Schema & field notes

project_name, developer_name, address — who built what, and where.
fiscal_year_complete — fiscal year of completion, 1995 to 2026.
total_units — units delivered. Sums to 19,249.
accessible_units, sensory_units, visitable_units — accessibility counts within the total.
project_type — Rental, Special Needs, Homeownership, Mixed Use, or a semicolon-joined combination such as Rental;Special Needs;Mixed Use.
development_type — New Construction, Rehab (unoccupied or vacant), or Preservation (occupied). Also semicolon-joined in places.
status — Complete or Under Construction.

Data quality & usage notes

project_type and development_type hold multiple values separated by semicolons. Grouping on the raw column treats each combination as its own category, which undercounts: naive grouping reports 242 rental projects, while splitting on ; gives the true 262.

Both columns mix three kinds of missing value: a real SQL null, the literal string 'null', and the literal string 'NULL'. Filter all three.

25 of 501 projects have null geometry. They carry attributes but cannot be mapped, and they sort to the end of the file.

The dataset covers DHCD-funded projects only. Affordable housing built without DHCD funding does not appear, so this is not a complete picture of affordable housing in the city.

Example queries

Correct project-type counts, splitting the semicolon-joined values:

Units delivered per fiscal year:

Accessibility share of the portfolio:

Projects that returned vacant buildings to use:

vacant_indicators_bldg — the vacant-building stock that rehabilitation projects draw from.
council_districts_2024 — join spatially to see where production has landed.
zoning_basedistricts — what the code permits where projects were built.

AGENTS.md — Affordable Housing Production

Guidance for AI agents and LLMs working with this collection.

Overview

Affordable housing projects funded by the Division of Housing and Community Development and completed since 1994. 501 projects accounting for 19,249 units, spanning fiscal years 1995 through 2026.

This is the only point collection in the catalog.

Accessing the data

Coordinates are EPSG:3857 (Web Mercator) metres. DuckDB reads geometry natively, so do not wrap it in ST_GeomFromWKB.

Schema & field notes

project_name, developer_name, address — who built what, and where.
fiscal_year_complete — fiscal year of completion, 1995 to 2026.
total_units — units delivered. Sums to 19,249.
accessible_units, sensory_units, visitable_units — accessibility counts within the total.
project_type — Rental, Special Needs, Homeownership, Mixed Use, or a semicolon-joined combination such as Rental;Special Needs;Mixed Use.
development_type — New Construction, Rehab (unoccupied or vacant), or Preservation (occupied). Also semicolon-joined in places.
status — Complete or Under Construction.

Data quality & usage notes

Both columns mix three kinds of missing value: a real SQL null, the literal string 'null', and the literal string 'NULL'. Filter all three.

25 of 501 projects have null geometry. They carry attributes but cannot be mapped, and they sort to the end of the file.

The dataset covers DHCD-funded projects only. Affordable housing built without DHCD funding does not appear, so this is not a complete picture of affordable housing in the city.

Example queries

Correct project-type counts, splitting the semicolon-joined values:

Units delivered per fiscal year:

Accessibility share of the portfolio:

Projects that returned vacant buildings to use:

vacant_indicators_bldg — the vacant-building stock that rehabilitation projects draw from.
council_districts_2024 — join spatially to see where production has landed.
zoning_basedistricts — what the code permits where projects were built.

1INSTALL spatial; LOAD spatial;
2INSTALL httpfs; LOAD httpfs;
3
4SELECT * FROM read_parquet(
5  'https://data.source.coop/nlebovits/phl-housing-demo/affordable_housing/affordable_housing.parquet'
6) LIMIT 5;

1INSTALL spatial; LOAD spatial;
2INSTALL httpfs; LOAD httpfs;
3
4SELECT * FROM read_parquet(
5  'https://data.source.coop/nlebovits/phl-housing-demo/affordable_housing/affordable_housing.parquet'
6) LIMIT 5;

1SET VARIABLE base = 'https://data.source.coop/nlebovits/phl-housing-demo';
2
3SELECT trim(t) AS project_type, count(*) AS projects
4FROM read_parquet(getvariable('base') || '/affordable_housing/affordable_housing.parquet'),
5     UNNEST(string_split(project_type, ';')) AS s(t)
6WHERE project_type IS NOT NULL
7  AND project_type NOT IN ('null', 'NULL')
8GROUP BY 1 ORDER BY 2 DESC;

1SET VARIABLE base = 'https://data.source.coop/nlebovits/phl-housing-demo';
2
3SELECT trim(t) AS project_type, count(*) AS projects
4FROM read_parquet(getvariable('base') || '/affordable_housing/affordable_housing.parquet'),
5     UNNEST(string_split(project_type, ';')) AS s(t)
6WHERE project_type IS NOT NULL
7  AND project_type NOT IN ('null', 'NULL')
8GROUP BY 1 ORDER BY 2 DESC;

1SET VARIABLE base = 'https://data.source.coop/nlebovits/phl-housing-demo';
2
3SELECT fiscal_year_complete, sum(total_units) AS units, count(*) AS projects
4FROM read_parquet(getvariable('base') || '/affordable_housing/affordable_housing.parquet')
5GROUP BY 1 ORDER BY 1;

1SET VARIABLE base = 'https://data.source.coop/nlebovits/phl-housing-demo';
2
3SELECT fiscal_year_complete, sum(total_units) AS units, count(*) AS projects
4FROM read_parquet(getvariable('base') || '/affordable_housing/affordable_housing.parquet')
5GROUP BY 1 ORDER BY 1;

1SET VARIABLE base = 'https://data.source.coop/nlebovits/phl-housing-demo';
2
3SELECT sum(total_units) AS units,
4       sum(accessible_units) AS accessible,
5       round(100.0 * sum(accessible_units) / sum(total_units), 1) AS pct
6FROM read_parquet(getvariable('base') || '/affordable_housing/affordable_housing.parquet');

1SET VARIABLE base = 'https://data.source.coop/nlebovits/phl-housing-demo';
2
3SELECT sum(total_units) AS units,
4       sum(accessible_units) AS accessible,
5       round(100.0 * sum(accessible_units) / sum(total_units), 1) AS pct
6FROM read_parquet(getvariable('base') || '/affordable_housing/affordable_housing.parquet');

1SET VARIABLE base = 'https://data.source.coop/nlebovits/phl-housing-demo';
2
3SELECT project_name, address, total_units, fiscal_year_complete
4FROM read_parquet(getvariable('base') || '/affordable_housing/affordable_housing.parquet')
5WHERE development_type LIKE '%Rehab%'
6ORDER BY total_units DESC;

1SET VARIABLE base = 'https://data.source.coop/nlebovits/phl-housing-demo';
2
3SELECT project_name, address, total_units, fiscal_year_complete
4FROM read_parquet(getvariable('base') || '/affordable_housing/affordable_housing.parquet')
5WHERE development_type LIKE '%Rehab%'
6ORDER BY total_units DESC;

Philadelphia Housing Data

Philadelphia Housing Data

AGENTS.md — Affordable Housing Production

Overview

Accessing the data

Schema & field notes

Data quality & usage notes

Example queries

Related collections

AGENTS.md — Affordable Housing Production

Overview

Accessing the data

Schema & field notes

Data quality & usage notes

Example queries

Related collections