{"id":26609279,"url":"https://github.com/mohitsai/boston-housing-data-analysis","last_synced_at":"2026-05-05T12:34:01.665Z","repository":{"id":282145619,"uuid":"947633238","full_name":"Mohitsai/boston-housing-data-analysis","owner":"Mohitsai","description":"Data Analysis Project for the City of Boston Government for insights into effect of property rennovations and remodelling on housing availability in the city","archived":false,"fork":false,"pushed_at":"2025-03-13T02:24:22.000Z","size":3,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-24T00:59:25.147Z","etag":null,"topics":["data-analysis","data-science","matplotlib","numpy","pandas","python"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Mohitsai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-13T02:09:35.000Z","updated_at":"2025-03-13T02:28:28.000Z","dependencies_parsed_at":"2025-03-13T03:24:53.321Z","dependency_job_id":"ad6eec9e-bc0b-4bfc-a53c-b0fc700632f1","html_url":"https://github.com/Mohitsai/boston-housing-data-analysis","commit_stats":null,"previous_names":["mohitsai/boston-housing-data-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Mohitsai/boston-housing-data-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohitsai%2Fboston-housing-data-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohitsai%2Fboston-housing-data-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohitsai%2Fboston-housing-data-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohitsai%2Fboston-housing-data-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mohitsai","download_url":"https://codeload.github.com/Mohitsai/boston-housing-data-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mohitsai%2Fboston-housing-data-analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32649598,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-05T11:29:49.557Z","status":"ssl_error","status_checked_at":"2026-05-05T11:29:48.587Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","matplotlib","numpy","pandas","python"],"created_at":"2025-03-24T00:59:27.636Z","updated_at":"2026-05-05T12:34:01.639Z","avatar_url":"https://github.com/Mohitsai.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Remodeling and Unit Loss in Boston\n\n## Project Overview\nThis project was conducted for the **City of Boston Government** to analyze **remodeling trends and unit loss** across Boston’s neighborhoods. The study examined changes in **housing availability, renovation trends, and multi-unit to single-family conversions**, using historical **property assessment and building permit data**.\n\nKey analyses included:\n- **Communities gaining vs. losing housing units**\n- **Impact of renovations on housing availability**\n- **Factors influencing unit loss in areas like Hyde Park and the South End**\n- **The role of multi-unit to single-family conversions in housing shortages**\n- **Effects of income-restricted housing policies**\n\n## Key Findings\n\n### **1️⃣ Housing Growth and Decline Trends**\n- **South Boston, Central Boston, and Dorchester** had the highest increase in housing units.\n- **Mattapan experienced significant unit loss**, though not primarily due to renovations.\n- **Roxbury, Dorchester, South Boston, and East Boston** showed increased safety-focused renovations.\n- **R4 (multi-family) unit numbers declined**, while **R1 (single-family) units increased**, indicating a shift towards single-family housing.\n\n### **2️⃣ Renovations and Housing Availability**\n- On average, **9,173 renovation-related permits were issued per year**.\n- The neighborhoods with the most renovation-related permits:\n  - **Central Boston**\n  - **Dorchester**\n  - **Back Bay/Beacon Hill**\n- **Most significant unit losses occurred in:**\n  - **South End**\n  - **Back Bay**\n  - **Charlestown**\n- **Two-unit to single-family conversions were the most common residential transformation.**\n\n### **3️⃣ Relationship Between Renovations and Housing Availability**\n- **Citywide correlation between renovations and unit growth: 0.086 (very weak)**\n- Some areas showed stronger correlations (~0.3):\n  - **02109** (North End)\n  - **02119** (Roxbury)\n  - **02130** (Jamaica Plain)\n  - **02136** (Hyde Park)\n  - **02124** (Dorchester)\n- **02115 (Fenway/Kenmore)** showed the strongest correlation: **0.50**, suggesting that in this area, renovations were linked to increases in available housing units.\n\n### **4️⃣ Factors Influencing Housing Changes in Hyde Park \u0026 South End**\n- **Hyde Park (02136)** saw significant increases in **bedrooms and bathrooms per unit**.\n- **Factors driving increase in unit size:**\n  - Changing **family size and income levels**.\n  - Preference for larger homes.\n- **Factors driving smaller units:**\n  - Urbanization and affordability concerns.\n- **Interior renovations were the most common type of renovation permit.**\n\n### **5️⃣ Multi-Unit to Single-Family Conversions**\n- **Highest conversion rates observed in:**\n  - **02115 (Fenway, Kenmore, Back Bay)** – a highly desirable area for affluent buyers.\n  - **02136 (Hyde Park)** and **02124 (Dorchester)** also showed high conversion rates.\n- **Average annual loss of 410.68 units** due to housing conversions.\n- **Factors contributing to conversions:**\n  - **Economic shifts:** Higher-income buyers drive demand for single-family homes.\n  - **Urban development policies:** Zoning laws favor single-family dwellings.\n  - **Market demand:** Preference for private, spacious homes.\n  - **Gentrification:** Rising property values displace lower-income residents.\n\n### **6️⃣ Income-Restricted Housing**\n- **Income-restricted housing ensures affordability for lower-income families.**\n- Most neighborhoods saw **an increase** in income-restricted housing, but **six neighborhoods experienced a decline**.\n- Understanding and implementing income-restricted policies is crucial for **ensuring socio-economic equality in housing.**\n\n## Data Sources\n- **Property Assessment Data (2004-2024)**\n- **Building Permits Data**\n- **Zoning and Housing Policy Reports**\n\n## Data Processing \u0026 Analysis\n- **Neighborhood ZIP Code Mapping**: Grouped Boston's ZIP codes into neighborhoods.\n- **Permit and Property Data Cleaning**:\n  - Standardized ZIP codes.\n  - Mapped **land use (LU) types**.\n  - Filtered for relevant renovation-related permits.\n- **Weighted Unit Process for Accurate Analysis**:\n  - Assigned **LU weights** to property types to ensure **accurate unit counts**:\n    - Single-family (R1) = 1\n    - Two-family (R2) = 2\n    - Three-family (R3) = 3\n    - Multi-family (R4) = 4\n    - Apartments (A) = 7\n- **Correlation Analysis**:\n  - Measured relationships between **permit approvals and housing availability**.\n- **Permit Trends Visualization**:\n  - **Line graphs, bar charts, and heatmaps** analyzed permit distributions over time.\n  \n## Key Visualizations\n- **Unit Growth \u0026 Loss by Neighborhood (2004-2024)**\n- **Top Renovation Permit Neighborhoods**\n- **Housing Unit Losses Due to Conversions**\n- **Interior Renovation Trends Across Boston**\n- **Correlation Between Permits and Unit Availability**\n- **Impact of Income-Restricted Housing Policies**\n\n## Policy \u0026 Planning Recommendations\n- **Strengthen zoning protections** for multi-unit housing in high-conversion areas.\n- **Expand income-restricted housing policies** to stabilize housing availability.\n- **Encourage targeted renovation incentives** in communities with strong permit-to-unit growth correlations (e.g., **02115, 02136, 02124**).\n- **Monitor trends in housing conversion** to assess gentrification risks.\n\n\n## 📞 Contact\nFor more details, please reach out:\n- **[LinkedIn](https://www.linkedin.com/in/mohitsaigutha/)**\n- **[Email](mailto:mohit.sai6@gmail.com)**\n\n---\n\n**© 2025 Mohit Sai Gutha** | Project for **City of Boston Government**\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohitsai%2Fboston-housing-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmohitsai%2Fboston-housing-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohitsai%2Fboston-housing-data-analysis/lists"}