This project analyzes historical sales data of a real estate company, providing insights into property prices, types, amenities, and locations. The dataset encompasses various aspects of the housing market, facilitating informed decision-making in real estate investment, property management, and market strategies.
- Introduction
- Dataset Overview
- Analysis
- Distribution of Property Prices
- Most Popular Axis
- Relationship between Property Size and Price
- Most Common House Types
- Distribution of Furnishing
- Distribution of Bedrooms
- Number of Properties with More Than 3 Bathrooms
- Percentage of Properties Located Within an Estate
- Number of Properties with a Boys' Quarters (BQ)
- Conclusion
This project aims to analyze historical sales data of a real estate company, focusing on house prices. The dataset includes sales records from various locations over a period of time. Predictive data analytics methods can be applied to this dataset for various insights into house pricing trends, factors influencing prices, and market dynamics.
The dataset used for analysis is stored in a CSV file named house_listings.csv. It contains the following columns:
price: Price of the propertyaxis: Location axisEstate Name: Name of the estate (if applicable)Property Size: Size of the property in square metersCondition: Condition of the propertyFurnishing: Furnishing status of the propertyHouse_Type: Type of houseBedroom: Number of bedroomsBathrooms: Number of bathroomsis_in_estate: Binary indicator (1 if the property is within an estate, 0 otherwise)has_BQ: Binary indicator (1 if the property has a boys' quarters, 0 otherwise)
The dataset was scraped from [https://jiji.ng/lagos/houses-apartments-for-rent].
The majority of property prices fall between 30,000 and 18,000,000, with a peak around the 4,000,000 to 8,000,000 range. The distribution appears to be slightly right-skewed, indicating that there are relatively more properties with lower prices compared to higher prices.
The axis with the highest number of properties listed is identified, which in this case is Lekki.
This scatter plot visualizes the relationship between property size (in square meters) and price. It shows the distribution of property prices based on their sizes. There doesn't seem to be a strong relationship between property size and price, as the data points are scattered without a clear trend.
This bar chart illustrates the count of each type of house listed in the dataset. The most common house type is identified, which is the Duplex.
This bar chart displays the distribution of different furnishing types among the listed properties. It shows the count of properties categorized by their furnishing status, such as Furnished, Semi Furnished, and Unfurnished.
This histogram depicts the distribution of the number of bedrooms among the listed properties. The average number of bedrooms is marked on the plot to provide a central measure.
This bar chart shows the count of properties that have more than 3 bathrooms.
This pie chart visualizes the percentage of properties that are located within an estate versus those outside an estate.
This bar chart displays the count of properties that have a boys' quarters (BQ).
In conclusion, this analysis provides valuable insights into the house listings data, including property prices, types, amenities, and locations. Understanding these insights can help in making informed decisions related to real estate investments, property management, and market strategies.
You can also refer to my blog post to read more on Medium.