{"id":23117800,"url":"https://github.com/praveendecode/us_hpi_prediction","last_synced_at":"2026-05-01T09:31:31.140Z","repository":{"id":211732188,"uuid":"729831834","full_name":"praveendecode/US_HPI_Prediction","owner":"praveendecode","description":"Explore the dynamics of US home prices over two decades using a robust Random Forest Regressor model. Achieving a 99.87% R2 score, uncover key factors influencing real estate trends","archived":false,"fork":false,"pushed_at":"2024-01-08T07:05:01.000Z","size":1611,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-04T02:11:47.666Z","etag":null,"topics":["exploratory-data-analysis","feature-engineering","feature-importance","insights","model-building","model-selection","predictive-modeling","python","usahpi"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/praveendecode.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}},"created_at":"2023-12-10T13:52:41.000Z","updated_at":"2025-02-08T18:51:14.000Z","dependencies_parsed_at":"2023-12-21T14:01:29.529Z","dependency_job_id":null,"html_url":"https://github.com/praveendecode/US_HPI_Prediction","commit_stats":null,"previous_names":["praveendecode/us_hpi_prediction"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/praveendecode/US_HPI_Prediction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praveendecode%2FUS_HPI_Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praveendecode%2FUS_HPI_Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praveendecode%2FUS_HPI_Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praveendecode%2FUS_HPI_Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/praveendecode","download_url":"https://codeload.github.com/praveendecode/US_HPI_Prediction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/praveendecode%2FUS_HPI_Prediction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32492104,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-30T13:12:12.517Z","status":"online","status_checked_at":"2026-05-01T02:00:05.856Z","response_time":64,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["exploratory-data-analysis","feature-engineering","feature-importance","insights","model-building","model-selection","predictive-modeling","python","usahpi"],"created_at":"2024-12-17T04:30:00.581Z","updated_at":"2026-05-01T09:31:31.125Z","avatar_url":"https://github.com/praveendecode.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Overview\n\n  - This repository contains a data science project focused on understanding the key factors influencing US home prices over the last 20 years.\n    \n  - The project utilizes the S\u0026P Case-Schiller Home Price Index as a proxy for home prices and explores various economic indicators to build a predictive model.\n    \n  - The chosen model is a Random Forest Regressor, achieving a remarkable R2 score of 99.87%\n\n  ![image](https://github.com/praveendecode/US_HPI_Prediction/assets/95226524/a957cf46-fdf3-4bd0-8690-67a13f9eb529)\n\n\n# Problem Statement\n\n  - The objective is to identify and analyze factors that significantly impact home prices in the United States.\n     \n  - By leveraging publicly available data, we aim to build a robust predictive model that explains the variations in the S\u0026P Case-Schiller Home Price Index over the past two decades.\n\n# Main Features\n\n The following features were selected for the predictive model:\n\n  ### Zillow Home Value Index (USAUCSFRCONDOSMSAMID):\n  \n  - Median market value of all homes.\n  - Reflects overall trends in home values, providing insights into market conditions and price movements.\n\n  ### Average Sales Price for New Houses Sold (ASPNHSUS):\n  \n  - Reflects the average cost of newly constructed homes.\n  - Influences perceptions of the affordability of new housing.\n\n  ### Median Sales Price for New Houses Sold (MSPNHSUS):\n  \n  - Provides insights into the typical price of newly sold houses.\n  - Helps understand the distribution of new home prices.\n\n  ### Total Construction Spending: Residential (TLRESCONS):\n  \n  - Reflects the level of investment in residential construction.\n  - Influences housing supply, potentially affecting home prices.\n\n  ### Gross Domestic Income (GDI):\n  \n  - Influences economic conditions and affects consumer confidence in buying houses.\n\n  ### Consumer Price Index for All Urban Consumers: Housing (CPIHOSNS):\n  \n  - Indicates inflation in housing costs, potentially impacting home prices.\n\n  ### Total Population: All Ages (POP):\n  - Population growth influences housing demand, potentially affecting home prices.\n\n  ### National Totals of State and Local Tax Revenue: Property Taxes (QTAXT01QTAXCAT1USNO):\n  \n  - Property taxes influence the overall cost of living.\n\n  ### New Privately-Owned Housing Units Authorized (PERMIT):    \n  \n  - Authorized housing units can influence home prices and increase demand.\n\n  ### Monthly Supply of New Houses (MSACSR):\n  \n  - Reflects the balance between housing supply and demand.\n\n ### 30-Year Fixed Rate Mortgage Average (MORTGAGE30US):\n \n  - Provides insights about a fixed interest rate for 30 years that buyers can afford for a loan.\n\n### University of Michigan: Consumer Sentiment (UMCSENT):\n\n  - Provides insights into consumer sentiment about the economy and the housing market.\n\n### Unemployed Population: Aged 25-54 (LFUN25TTUSM647S) and Unemployment Rate (UNRATE):\n\n  - Influences job security; more job security improves purchasing ability.\n\n### Housing Inventory Estimate: Vacant Units (EVACANTUSQ176N):\n\n  - Provides insights into vacant unit availability, market conditions, and supply and demand balance.\n\n### Federal Funds Effective Rate (FEDFUNDS):\n\n  - Changing in the federal reserve; adjusts the federal funds rate may influence the mortgage rate.\n\n# Model Process\n\n  ### Data Cleaning and Imputation:\n  \n   - Utilized machine learning models to impute missing values in the dataset.\n\n  ### Exploratory Data Analysis (EDA):\n  \n   - Conducted thorough EDA to understand the relationships between features and the target variable.\n\n  ### Feature Engineering:\n  \n   - Engineered relevant features to improve model performance.\n\n  ### Model Selection:\n  \n   - Chose the Random Forest Regressor based on its outstanding R2 score of 99.87%.\n\n  ### Model Evaluation:\n  \n   - Utilized R2 score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for model evaluation.\n     \n  ### Feature Importance :\n\n   -  After training the Random Forest Regressor model, the feature importance analysis has done to reveal the predictor variables that contributes more when predicting target variable.\n\n  # Tools Covered\n\n  - Programming Langauge : Python\n\n  - Code Notebook : Google Colab-notebook\n  \n  - Data Collection: FRED (Federal Reserve Economic Data)\n    \n  - Data Cleaning: Machine Learning Imputation\n\n  - Exploratory Data Analysis: Pandas, Matplotlib, Seaborn\n    \n  - Model Building: Scikit-Learn (Random Forest Regressor)\n    \n  - Model Evaluation: R2 Score, MAE, RMSE\n\n  # Results\n\n   - Model Fit (R2 Score): 99.87%\n   - MAE (Mean Absolute Error): 1.39\n   - RMSE (Root Mean Squared Error): 2.37\n   - The Random Forest Regressor demonstrated superior performance in minimizing errors and capturing the variance in the target variable compared to other models\n   \n \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpraveendecode%2Fus_hpi_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpraveendecode%2Fus_hpi_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpraveendecode%2Fus_hpi_prediction/lists"}