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Nakrevive/Rwanda-housing-market-analysis

Domain:

socioeconomic

Record type:

dataset
Creator:
Nak
Host:
This project analyzes housing market conditions across Rwanda using over 15,000 housing records. Project Overview This project analyzes Rwanda's housing market using data from 15,054 households across the country. The objective was to explore: Housing affordability Housing demand Property values Rental opportunities Geographic housing patterns Real-estate investment potential Business Questions Demand Which districts have the highest housing demand? Are households concentrated in urban or rural areas? Affordability What is the average property value? Which locations are most affordable? Market Analysis Which districts have the highest housing values? What relationship exists between house size and property value? Investment Which locations may present attractive investment opportunities? Methodology 1. Data Collection Collected housing survey data containing demographic, housing, infrastructure, and property information. 2. Data Cleaning Handled missing values Standardized variables Created analytical features 3. Feature Engineering Created: Rent_Final Value_Per_Room Rent_Value_Ratio 4. Exploratory Data Analysis Performed: Descriptive statistics Correlation analysis Geographic analysis Distribution analysis 5. Dashboard Development Built an interactive Power BI dashboard to communicate findings. Key Findings Kigali Dominates Property Values Kicukiro, Gasabo, and Nyarugenge recorded the highest average housing values. Housing Affordability Varies Significantly Large regional differences exist between Kigali and other districts. House Size Influences Value A moderate positive relationship (r = 0.48) exists between floor area and property value. Investment Potential Exists Beyond Kigali Districts such as Musanze, Rubavu, Huye, and Bugesera demonstrated strong housing values outside the capital. The analysis was conducted using Python for data preparation and Power BI for interactive dashboard development Tools Used Tool Purpose Python Data Cleaning Pandas Data Manipulation NumPy Numerical Analysis Matplotlib Visualization Power BI Dashboard Deve …

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