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Tehran Residential Rental Listings
Data Science and Analytics
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About
This dataset offers insights into the residential rental market in Tehran, Iran, providing a beginner-friendly collection of rental advertisements. Housing in Tehran is diverse, ranging from opulent properties in the north to more modest dwellings in the south. The city's rental market is notably challenging, akin to London or New York City, exacerbated by significant inflation in Iran, making suitable housing a distant dream for many families. Typically, renters pay both a monthly rent and a security deposit, though some landlords prefer a larger upfront deposit. The dataset captures the dynamics of this unique market, reflecting the financial structures, such as the general requirement of a 1 million toman (or 10 million rials) security deposit for every 30,000 tomans of monthly rent.
Columns
The dataset comprises 14 columns, offering detailed information about rental properties:
- deposit (in million tomans): The upfront money renters pay before moving into a house, reclaimable upon evacuation. It is important to note that due to high inflation in IRR (Iranian Rial currency), this deposit can lose a notable portion of its value (between 50-75%) within a year.
- rent (in million tomans): The monthly payment due from the renter. A formula often applies: each 100 in deposit is equivalent to 3 in monthly rent.
- floor: The floor level of the building where the house is located.
- area: The size of the house, measured in square metres.
- age: The age of the house in years.
- rooms: The total number of rooms in the property.
- elevator: A binary indicator (1 for yes, 0 for no) if the building includes an elevator.
- parking: A binary indicator (1 for yes, 0 for no) if a parking space is included with the property.
- Warehouse: A binary indicator (1 for yes, 0 for no) if a warehouse or storage space is included.
- time: The Unix timestamp indicating when the advertisement was posted.
- region: Refers to the specific neighbourhood where the house is situated; this is a subdivision of a district, often pointing to a street or a local area.
- district: Refers to one of Tehran's 22 districts where the house is located.
- all_to_deposit: A calculated value based on the deposit and rent, indicating if the landlord prefers to receive most, if not all, of the money upfront as a deposit. This column might be considered redundant as it is derived from the
depositandrentfeatures.
Distribution
The data is provided in a CSV file format and has a file size of 13.05 MB. It is structured as a tabular dataset with 14 columns. Initially, 230,000 unique advertisements were collected. After filtering for null values in the
rent or deposit columns, the dataset contains 165,000 valid records.Usage
This dataset is suitable for a variety of applications and use cases, particularly for those new to data analysis. It can be used for:
- Exploratory Data Analysis (EDA) to understand patterns and trends in Tehran's rental market.
- Regression analysis to build predictive models for rent or deposit values based on property attributes.
- Studying the impact of inflation on housing costs and rental agreements in a major urban centre.
- Analysing geographical variations in rental prices and property features across different districts and regions of Tehran.
Coverage
The dataset's geographic scope is Tehran, Iran, encompassing the city's diverse housing landscape, from its northern, typically affluent areas to its southern zones. It includes data across all 22 districts of Tehran and their respective regions or neighbourhoods. The time coverage for the advertisements spans a period from approximately mid-July 2022 to late August 2022, as indicated by the Unix timestamps in the
time column. No specific demographic scope is provided beyond the general residential rental market.License
Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO).
Who Can Use It
This dataset is ideal for:
- Beginner data scientists and analysts looking for an accessible, real-world dataset to practise their skills.
- Researchers interested in urban studies, housing markets, or the socio-economic impacts of inflation in metropolitan areas.
- Students undertaking projects involving data cleaning, visualisation, or machine learning model development (e.g., predicting rental prices).
- Economists examining rental dynamics and security deposit practices in an inflationary environment.
Dataset Name Suggestions
- Tehran Residential Rental Listings
- Tehran Housing Market Insights 2022
- Iran Property Rental Dataset
- Tehran Rental Advertisements
Attributes
Original Data Source: Tehran Residential Rental Listings
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