The real estate market is a dynamic and multifaceted industry which impacts professionals, investors, and policymakers. In this industry, comprehending market conditions, customer behavior, and property characteristics is critical for making informed decisions. This project leverages Python to preprocess, analyze, and visualize real estate data, uncovering meaningful insights into property transactions and customer profiles for a leading real estate company. 🏢💼
The client, a prominent real estate company, has collected extensive data on properties and their customers. The data is currently unprocessed, with missing values and inconsistencies, hindering meaningful analysis. The company seeks actionable insights to answer the below given key problems:
- 👥 What age groups exhibit the highest buyer potential?- 🏠 Which property types perform best in terms of sales and satisfaction?
- 🌍 How does deal satisfaction vary by region?
To address these challenges, the data needs to be cleaned, merged, and analyzed to generate insights and visualizations for strategic decision-making.
- realstate_data.csv 🗂️- Cleaned dataset containing property and customer information.
- Realestate_Analysis.ipynb 💻- Jupyter Notebook with all code and analysis https://github.com/Gkkumar2/Real-Estate-Market-Analysis-with-Python/blob/main/Realstate_Analysis.ipynb.
- README.md 📑- Project documentation.
- Both properties and customer datasets are consolidated in to Realstate dataset using the customerid column.
- Changed the column data to correct datatype.
The primary objectives of this project that overall covers the key project problems are given below:
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Data Preprocessing
- 🧹 Handle missing values and inconsistencies.
- 🔄 Transform and standardize column data types.
- 🤝 Merge datasets to create a unified dataset for analysis.
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Descriptive Statistics
- 📊 Understand numerical and categorical data distributions.
- 🏠 Summarize property sales, satisfaction levels, and customer profiles.
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Data Analysis
- 🏢 Examine property sales by building type, country, and state.
- 👤 Explore customer demographics and behavior patterns.
- 💰 Investigate relationships between variables like age and property price.
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Data Visualization
- 📈 Create meaningful visualizations for insights such as:
- 🔥 Deal satisfaction by country and state.
- 📅 Monthly sales trends.
- 🏙️ Yearly sales per building type.
- 📈 Create meaningful visualizations for insights such as:
This project is built using the following tools and libraries:
- Python 3.12.4- For data preprocessing, analysis, and visualization.
- Jupyter Notebook: For an interactive coding environment.
- pandas 📊- For data manipulation and cleaning.
- NumPy 🔢- For numerical operations.
- Matplotlib 🎨- For static visualizations.
- Seaborn 🌸- For statistical data visualizations.
- Plotly 📉- For interactive visualizations.
- datetime ⏳- For date transformations and operations.
This specific project revolves around two datasets
- Properties Dataset 🏘️- Contains details about properties, including property ID, building type, sale date, area, and price.
- Customers Dataset 👥- Includes customer ID, name, surname, date of birth, and other demographic information.
The objective is to combine and preprocess these datasets to analyze customer behavior and property transactions.
- Handle Missing Values 🚫 - Identify and appropriately manage missing entries across columns.
- Standardize Column Names ✏️ - Ensure consistency in column naming conventions (e.g., lowercase and underscore-separated).
- Transform Data Types 🔄 - Convert columns to appropriate data types (e.g., dates to datetime, categorical values to numerical).
- Remove Inconsistencies 🧼 - Address issues such as leading/trailing spaces in key columns (e.g.,
customer_id). - Merge Datasets 🔗 - Combine properties and customers datasets on the
customer_idcolumn to form a unified dataset of 267 rows and 19 columns.
Analyze key variables using descriptive statistics to understand:
Ⅰ . 🏠 Sales performance by building type, country, and state.
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Property distribution can be identified in where; apartments dominate the listings, accounting for 97% of all properties, while offices represent only 3%.
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The overall combined value of listed properties is $73M for apartments and $1.99M for offices, including both sold and unsold properties.
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The average listing price for an apartment is $284.2K, whereas office properties are listed at an average of $248.8K.
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In terms of , Revenue by country, The United States generates the majority of the revenue, with 91% of the listed properties located there.
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As a top performing state, ?California leads in revenue generation, contributing over $33M to the total sales.
Ⅱ . 👤 Customer age distribution and buyer behavior.
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When considering the 'customer age distribution';The majority of customers fall within the 30-50 age range, indicating a strong market presence in this demographic.
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Revenue by Age Group ;
- The 36-42 age group generated the highest revenue, contributing $10M.
- The 31-36 and 42-48 age groups each generated around $6.5M in revenue.
- The 71-76 age group contributed the lowest revenue, with $775K.
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Where the spending pattern by age was analyzed, Customers aged 19 to 48 tend to spend more on average when purchasing properties. In contrast, those aged 49 and older exhibit lower average spending on property purchases.
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Property Purchases by Age Group ;
- The 36-42 age group purchased the highest number of properties (35).
- Both the 31-36 and 42-48 age groups followed closely, each purchasing 24 properties.
Ⅲ . 💵 Key numerical variable distributions, such as property price and area.
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Apartments are listed at an average price of $284.2K, while office properties average $248.8K.
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Apartments have an average area of 939 ft²; overall incosideration of the property area, and office properties average 835 ft².
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Impact of Mortgages on Pricing ;
- Properties with mortgages tend to have higher average prices compared to those without mortgages.
- Apartments:
- With mortgage: $247.5K
- Without mortgage: $268K
- Office Properties:
- With mortgage: $268K
- Without mortgage: $233K
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Correlation Between Area and Price:
- There is a strong positive correlation (0.95) between area and price, indicating that as property area increases, its price also rises which can provide a better exploratory on the analysis.
- 🧮 Examine age groups and their buying potential by categorizing ages into intervals.
- 💰 Analyze property price distributions and create price bins for further insights.
- 📉 Investigate relationships between customer demographics (e.g., age) and property-related metrics (e.g., price).
Below - given are the key visualizations addressing critical questions of this project :
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Customer Satisfaction by Country ;
- Canada and Germany have the highest customer satisfaction scores of 5 out of 5, but this is likely due to the small number of properties listed in these countries.
- In contrast, the USA has an average customer satisfaction score of 3.58, reflecting a larger dataset.
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Customer Satisfaction by Property Type ;
- In the USA, apartments have an average satisfaction score of 3.57, while office properties score slightly higher at 3.86.
- Canada and Germany maintain perfect satisfaction scores across apartment listings.
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🗺️ State Chart
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Customer Satisfaction by State ;
- Customers who purchased properties in Virginia are the most satisfied, with an average rating of 4.5.
- California follows with the second-highest satisfaction score of 3.75.
- Satisfaction scores for other states range between 3 and 3.75.
- Properties listed without a state mentioned (categorized as Other Countries) have an average satisfaction rating of 3.64.
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Customer Satisfaction by Property Type ;
- For apartments, Virginia stands out with the highest customer satisfaction score of 4.5.
- For office properties, Nevada leads with a perfect satisfaction score of 5.
📈 Revenue Chart
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Peak Performance ;
- November 2007 achieved the highest monthly revenue at $4.69M, reflecting exceptional sales performance during this period.
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Lowest Revenue ;
- The lowest monthly revenue occurred in May 2005, with a total of $215k, possibly due to a market slowdown or reduced transactions.
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Revenue Spikes ;
- Significant growth periods were observed in July 2005, September 2006, March 2007, and November 2007, indicating surges in market demand or strategic sales efforts.
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Revenue Decline ;
- A sharp decline in revenue was recorded between March 2007 and July 2007, dropping from $3.5M to $1.5M, which may reflect seasonality or market challenges.
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Stable Performance :
- Years such as 2004 and 2008 demonstrated consistent revenue trends, showcasing periods of steady market activity.
🤝 State Revenue Chart ;
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Top-Performing State;
- California recorded the highest number of property sales, with 120 properties sold, followed by Nevada, which sold just 14 properties.
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Dominant Share;
- California accounts for 61.5% of all sold properties, highlighting its dominance in the real estate market.
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Revenue Leader;
- California generated the highest revenue at $33.4M, significantly surpassing the second-highest state, Nevada, which achieved $4.7M in revenue.
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Sales-Driven Revenue;
- The substantial number of property sales in California directly contributed to its leadership in revenue generation.
👥 Age Distribution Chart;
📝 Insight;
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Customer Age Distribution;
The 30-50 age range dominates the customer base, highlighting this demographic as the primary market segment for property purchases. -
Revenue by Age Group;
- The 36-42 age group stands out, generating the highest revenue of $10M.
- Both the 31-36 and 42-48 age groups contributed approximately $6.5M each in revenue.
- The 71-76 age group recorded the lowest revenue, amounting to $775K.
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Spending Patterns by Age; Customers aged 19 to 48 exhibit higher average spending on property purchases, whereas those aged 49 and older tend to spend less on average.
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Property Purchases by Age Group;
- The 36-42 age group purchased the highest number of properties (35).
- Both the 31-36 and 42-48 age groups closely followed, with 24 properties purchased each.
🗓️ Sales Charts
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Building No.1;
- Sales increased from 15 in 2004 to 24 in 2005, but then declined sharply to 6 in 2006 and 1 in 2007.
- Revenue followed a similar trend, rising from $4.2M in 2004 to $6.3M in 2005, before declining to $1.6M in 2006 and $446K in 2007.
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Building No.2;
- Sales showed consistent growth, starting with 1 property in 2004, followed by 3 in 2005, 23 in 2006, and 27 in 2007.
- Correspondingly, revenue increased steadily from $169K in 2004 to $7.38M in 2007.
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Building No.3;
- In 2006, the building generated $1.9M in revenue from 8 sold properties.
- In 2007, sales surged to 43 properties, generating $11.3M, before declining significantly to $437K with just 3 sold properties in the following year.
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Building No.4;
- Revenue in 2006 was $457K from 2 sold properties, which rose to $5.4M with 20 properties sold in 2007.
- Sales declined in subsequent years, with revenue dropping to $388K from 1 property sold.
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Building No.5;
- In 2006, revenue was $249K from 1 sold property, increasing to $2.7M with 11 properties sold in 2007.
- Revenue decreased in subsequent years, dropping to $1.3M and $357K, respectively.
This document outlines strategic recommendations based on a comprehensive analysis of real estate market data. These insights focus on revenue optimization, customer satisfaction, and market expansion opportunities.
- California generates the highest revenue. Focus on retaining and expanding the market here by offering premium properties and targeted campaigns for high-value customers.
- Design exclusive marketing campaigns emphasizing California’s prime properties to maintain dominance.
- Despite lower sales than California, Nevada shows significant growth potential. Invest in targeted promotions and strategic property listings to attract customers and capture this market.
- Virginia exhibits high satisfaction scores, indicating a loyal customer base. Expand presence to build on this trend and further improve satisfaction-driven loyalty.
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Focus marketing efforts on the 36–42 age group, the highest revenue-generating demographic, followed by:
- 31–36 years
- 42–46 years
- 25–31 years
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Tailor promotional content to resonate with these audiences based on lifestyle and purchasing behavior.
- Customers aged 19–48 demonstrate a higher average purchase price. Prioritize marketing premium properties to this segment for maximum revenue.
- Customers aged 48+ prefer affordable property options. Align pricing strategies and campaigns to cater to this segment effectively.
- Satisfaction scores are highest among senior age groups. Focus on these demographics to leverage satisfaction and build loyalty.
- Canada and Germany customers report higher satisfaction levels, indicating a lucrative market. List more properties and strengthen marketing in these regions.
- Denmark has lower satisfaction scores. Address concerns by improving quality assurance and transparency in property dealings. Develop a proactive customer support system for future engagements.
- Increase apartment listings to meet rising market demand. Apartments are the most sought-after properties.
- Promote office properties to niche customer segments that align with their investment potential and utility.
- Building #4 records the highest satisfaction rates. Prioritize listing additional properties in this building to attract more customers.
- Historical data indicates October to November as a high-revenue period. Plan promotional campaigns during these months to capitalize on the trend.
- Invest heavily in California as the primary market and explore Nevada for growth opportunities.
- Tailor marketing strategies based on the age-specific purchase patterns and regional satisfaction levels.
- Use customer feedback from Denmark to improve service quality and build trust in underperforming regions.
- Leverage data insights on high-revenue months and satisfaction hotspots for targeted campaigns.
The Real Estate Market Analysis revealed critical insights to guide business decisions and improve market strategies. As a part of the analysis, California emerged as the highest revenue-generating state, making it a prime focus for investments, while Nevada demonstrated significant growth potential. While the age group analysis highlighted the importance of targeting customers aged 36-42, with tailored approaches for different property types based on spending patterns.
Furthermore, customer satisfaction trends underscored the value of focusing on high-performing countries like Canada and Germany, while addressing transparency and quality concerns in Denmark. The provided strategic recommendations, including expanding apartment listings and leveraging historical data for promotional campaigns, provide actionable steps to enhance revenue and customer experience.
This analysis lays a strong foundation for informed decision-making, ensuring sustainable growth and improved market positioning in the competitive real estate industry.
Ensure you have the following installed:
- Python 3.12.4 🐍
- Jupyter Notebook 📓