Introduction:

The United Arab Emirates (UAE) has emerged as a global hub for real estate investment, attracting buyers and developers from around the world. This project aims to analyze a dataset of property listings in the UAE to gain insights into the current state of the market, identify key trends, and uncover factors that influence property prices.

Using a dataset of approximately 500 property listings, this analysis will explore the distribution of property prices, the relationship between property features (e.g., location, number of bedrooms, property type) and prices, and the prevalence of studio apartments versus larger units.

This project is relevant to investors, developers, and anyone interested in understanding the dynamics of the UAE real estate market. By analyzing real-world data, we can gain valuable insights into the factors that drive property values and identify potential investment opportunities.

Data Description

This analysis utilizes a dataset containing 492 rows and 10 columns, representing property listings in the UAE. The dataset includes information such as property ID, title, display address, number of bathrooms and bedrooms, date added, property type, price, and RERA registration number.

The columns have the following data types:

• int64: id, bathrooms, bedrooms, price

• object: title, displayAddress, type, propertyType

• float64: rera

The dataset was obtained from Kaggle.

Here are some basic statistics for the numerical columns:

It is important to note several potential limitations of this dataset:

• Limited scope: The dataset contains only 492 property listings, which may not be fully representative of the entire UAE real estate market.

• Missing information: The rera column has a significant number of missing values, which could limit our ability to analyze the relationship between RERA registration and property prices.

• Data accuracy: The accuracy of the data cannot be guaranteed, as it may be subject to errors or inconsistencies in the original source.

• Bathroom anomaly: The bathroom column has same value for all the properties (No of Bathrooms - 2). So this column was removed later.
  
  
  

Here are some other useful info about the dataset :

First 5 rows

Information about the dataset

Number of rows and columns & Data types of each column

Data Cleaning and Preprocessing section.

Exploratory Data Analysis (EDA) 

1. Distribution of Property Prices:

• Visualization: Histogram of the price column

• Purpose: To understand the range of prices, the typical price, and whether there are any outliers or unusual patterns.

• Insights: The distribution is heavily skewed to the right, with most properties priced below 5 million and a few outliers with much higher prices.

2. Average Price by Studio vs. Non-Studio:

• Analysis: Calculate the average price for properties grouped by the is_studio column

• Purpose: To compare the average price of studio apartments to that of other property types.

• Insights: Studio apartments have a lower average price than non-studio apartments, likely because they are smaller and have fewer bedrooms.

3. Property Prices by Location:

Analysis: Identify the locations with the most expensive and least expensive properties.

Purpose: To understand which locations are driving high prices and which are more affordable.

Insights: Palm Jumeirah, Business Bay, and other prime locations have the highest average property prices.

4. Property Prices Over Time:

• Analysis: Calculate the average price of properties added in each year.

• Visualization: Line chart of the average price over time.

• Purpose: To identify any trends in property prices over time.

• Insights: The average property price has generally increased over the observed period. However, the limited number of data points in 2022 suggests this initial increase may not be statistically significant. Comparing 2023 and 2024 reveals a substantial boom in the sector, evidenced by the significant rise in the number of entries. Interestingly, despite this surge in activity, the average property price remained relatively stable, showing only a modest increase. 

5. Distribution of Bedrooms:

Visualization: Bar chart of the number of properties with different numbers of bedrooms.

Purpose: To understand the composition of the property market in terms of property size.

Insights: The dataset mainly consists of 0-bedroom (studio) and 3-bedroom apartments.

6. Price Distribution by Number of Bedrooms

Visualization: Box plots of price, separated by the number of bedrooms.

Purpose: To visualize the different price distributions based on how many bedrooms a property has.

Insights: Based on the boxplot, we can observe the following:

Price Difference: There's a clear difference in price distribution between properties with 0 bedrooms and those with 3 bedrooms.

0 Bedrooms: Properties with 0 bedrooms (likely studios or smaller apartments) generally have a lower price range. Most of the data points are concentrated in the lower price range.

3 Bedrooms: Properties with 3 bedrooms tend to have a higher median price and a wider range of prices. There are also several high-priced outliers, indicating some very expensive 3-bedroom properties.

Outliers: Both categories have outliers, but the 3-bedroom category has a more significant number of high-value outliers. This suggests that while most 3-bedroom properties are more expensive than 0-bedroom ones, there's also a greater possibility of very high-priced luxury 3-bedroom properties.

CONCLUSION

This analysis of UAE property listings has provided valuable insights into the dynamics of the real estate market. We found that property prices are heavily influenced by location, with prime areas like Palm Jumeirah and Business Bay commanding the highest prices. Studio apartments, which constitute a significant portion of the market, have a lower average price compared to larger, multi-bedroom units.

Our analysis also revealed that the dataset mainly consists of 0-bedroom (studio) and 3-bedroom apartments. The average property price has generally been increasing.

These findings have several implications for stakeholders in the UAE real estate market:

• Investors: Location is a key factor to consider when making investment decisions. While prime locations offer the potential for high returns, more affordable areas may provide opportunities for value investing.

• Developers: Understanding the demand for different types of properties (e.g., studio apartments vs. multi-bedroom units) can help developers make informed decisions about what types of projects to build.

It is important to acknowledge the limitations of this analysis. The dataset is relatively small and may not be fully representative of the entire UAE real estate market. Additionally, the dataset lacks information on property quality, amenities, and other factors that could influence property prices.

Future research could explore the relationship between property prices and other variables, such as property age, size, and proximity to amenities. It would also be beneficial to analyze a larger and more comprehensive dataset to gain a more complete understanding of the UAE real estate market.