California Historical Housing Data (1990–2020): Reconstructed and Interpolated Block Group-Level Estimates

To ensure proper use of the data and code, please read this README carefully. The repository is organized into the following folders: 1. housingdatasetdownloaded Contains the original housing datasets in .csv format for the years: 1990 2000 2010 2020 (downloaded via Census Data API) Data sources: 1990 and 2000: U.S. Decennial Census 2010 and 2020: 5-Year American Community Survey (ACS) Note: ACS housing datasets do not include total population counts. 2. nhgis2010_2020DecennialSurvey_csv Contains original population data in .csv format for: 2010 2020 Downloaded from IPUMS NHGIS (Decennial Census datasets). 3. Cleaned90001020housingCSVdata Contains cleaned housing datasets for: 1990 2000 2010 2020 Processing steps: Standardized variable naming Data cleaning and formatting Population data merged into 2010 and 2020 housing datasets Population was joined using the GISJOIN unique identifier. 4. InterpolatedHousingValues Contains interpolated median housing values derived from the cleaned datasets (Folder 3). Interpolated fields: MEDIAN1990 MEDIAN2000 MEDIAN2010 MEDIAN2020 Output formats: .csv .xls Example files: ca_1990_bg_housing_interpolated.csv ca_2020_bg_housing_interpolated.xls Special Dataset Included: CA_2020_bg_housing_4YrsInterpolated This dataset uses 2020 block group boundaries as the spatial reference framework and includes interpolated median housing values for: 1990 2000 2010 2020 Fields: median90 median00 median10 median20 5. shapeFile90001020BGlevelDownloaded Contains shapefiles for: Block group boundaries (1990, 2000, 2010, 2020) County boundaries State boundaries Sources: TIGER/Line Shapefiles (2016 release) 6. ArcGISProModel Contains the ArcGIS Pro model developed for this research. All relevant layers and tables are included. Compatible with ArcGIS Pro version 3.4. The model can be opened, executed, and reviewed directly within ArcGIS Pro. Note: If running the model independently, some data paths may need to be updated. 7. CodewithArcPyNotebook Contains selected ArcPy scripts used in this research, organized as Jupyter Notebooks. Designed to run in: ArcGIS Pro’s built-in Python environment, or A standalone Jupyter environment with ArcPy properly configured. For privacy and security reasons, certain elements (e.g., API keys, local file paths) have been removed. Author Forrest (Shuang) TianFebruary 18, 2026