Zillow reigns supreme in the U.S. real estate website landscape, attracting a staggering ***** million monthly visits in 2024. This figure dwarfs its closest competitor, Realtor.com, which garnered less than half of Zillow's traffic. Online platforms are extremely popular, with the majority of homebuyers using a mobile device during the buying process. The rise of Zillow Founded in 2006, the Seattle-headquartered proptech Zillow has steadily grown over the years, establishing itself as the most popular U.S. real estate website. In 2023, the listing platform recorded about *** million unique monthly users across its mobile applications and website. Despite holding an undisputed position as a market leader, Zillow's revenue has decreased since 2021. A probable cause for the decline is the plummeting of housing transactions and the negative housing sentiment. Performance and trends in the proptech market The proptech market has shown remarkable performance, with companies like Opendoor and Redfin experiencing significant stock price increase in 2023. This growth is particularly notable in the residential brokerage segment. Meanwhile, major players in proptech fundraising, such as Fifth Wall and Hidden Hill Capital, have raised billions in direct investment, further fueling the sector's development. As technology continues to reshape the real estate industry, online platforms like Zillow are likely to play an increasingly crucial role in how people search for and purchase homes. (1477916, 1251604)

Zillow.com was the most-visited real estate website worldwide in 2024, with an average of ************* visits per month during the measured period. Leboncoin.fr ranked second, with ***** million monthly visits, while Carigslist.org ranked third, with ***** million average accesses.

In 2024, U.S. homebuyers considered photos and the detailed information about a home listing as the most valuable features of real estate websites. Additionally, ** percent of respondents cited virtual listings as very useful, while ** percent listed flor plans.

1. Customers File (customers.csv)

• Description: This file contains information about clients involved in real estate transactions. It includes personal details such as name, surname, birth date, gender, and country, along with transaction-specific information like the purpose of the deal and the satisfaction level.
• Key Columns:
  • customerid: Unique identifier for the customer.
  • entity: Type of client, whether an individual or a company.
  • name and surname: First and last name of the customer.
  • birth_date: Customer's date of birth.
  • sex: Gender of the customer (Male/Female).
  • country and state: The country and state the customer is associated with.
  • purpose: Purpose of the transaction (e.g., Home purchase or Investment).
  • deal_satisfaction: Customer's satisfaction level with the transaction, ranging from 1 to 5.
  • mortgage: Indicates whether the transaction involved a mortgage (Yes/No).
  • source: How the customer was acquired (e.g., Website or Agency).

2. Properties File (properties.csv)

• Description: This file contains information about the properties sold, including building details, property type, area, price, and sale status.
• Key Columns:
  • id: Unique identifier for the property.
  • building: Number of the building where the property is located.
  • date_sale: The date when the property was sold.
  • type: Type of property (e.g., Apartment).
  • property#: The property number within the building.
  • area: Area of the property in square feet.
  • price: Sale price of the property.
  • status: Status of the sale (e.g., Sold).
  • customerid: The unique identifier of the customer associated with the property.

Suggested Analysis and Tasks

1 Customer Insights: - Customer Segmentation: Group customers based on demographics, purpose, or deal satisfaction to understand different customer profiles. - Satisfaction Analysis: Investigate what factors (e.g., property price, area, or mortgage involvement) influence customer satisfaction levels. - Source Effectiveness: Analyze which acquisition sources (e.g., website or agency) yield the highest deal satisfaction.

2 Property Market Analysis: - Price Trends: Analyze how property prices vary over time or by location to identify market trends. - Demand Analysis: Determine which types of properties (e.g., apartments vs. houses) are most popular based on sales data. - Area vs. Price: Explore the relationship between property area and price to develop pricing models or evaluate property value.

3 Predictive Modeling: - Price Prediction: Build models to predict property prices based on features like area, type, and location. - Satisfaction Prediction: Create models to predict customer satisfaction using transaction details and demographics. - Likelihood of Sale: Develop a model to predict the likelihood of a property being sold based on its attributes and market conditions.

4 Geographical Analysis: - Heatmaps: Create heatmaps to visualize property sales and identify high-demand areas. - Country and State Trends: Examine how real estate trends differ between countries and states.

5 Mortgage Impact Study: - Mortgage vs. Non-Mortgage Analysis: Compare transactions that involved a mortgage to those that didn’t to study the impact on price, satisfaction, and deal closure speed.

6 Time Series Analysis: - Sales Over Time: Analyze property sales over different periods to identify seasonal trends or patterns. - Customer Birth Date Analysis: Study any correlations between customers’ birth years and their purchasing behavior.

Graph and download economic data for Housing Inventory: Active Listing Count in the United States (ACTLISCOUUS) from Jul 2016 to Oct 2025 about active listing, listing, and USA.

Real estate datasets from various websites cover all major real estate data points including: property type, size, location, price, bedrooms, baths, address, history, images, and much more. Popular use cases include: forecast housing demand, analyze price fluctuations, improve customer satisfaction, see past prices to monitor market trends, and more.

Our extensive database contains approximately 800,000 active rental property listings from across the United States. Updated daily, this comprehensive collection provides real estate professionals, investors, and property managers with valuable market intelligence and business opportunities. Database Contents

Property Addresses: Complete location data including street address, city, state, ZIP code Listing Dates: Original listing date and most recent update date Availability Status: Currently available, pending, or recently rented properties Geographic Coverage: Properties spanning all 50 states and major metropolitan areas

Applications & Uses

Market Analysis: Track rental pricing trends across different regions and property types Investment Research: Identify high-opportunity markets with favorable rental conditions Lead Generation: Connect with property owners potentially needing management services Competitive Intelligence: Monitor listing volumes, vacancy rates, and market saturation Business Development: Target specific neighborhoods or property categories for expansion

File Format & Delivery

Organized in easy-to-use CSV format for seamless integration with data analysis tools Accessible through secure download portal or API connection Daily updates ensure you're working with the most current market information Custom filtering options available to narrow results by location, date range, or other criteria

Data Quality

Rigorous validation processes to ensure address accuracy Duplicate listing detection and removal Regular verification of active status Standardized format for consistent analysis

Subscription Benefits

Access to historical listing archives for trend analysis Advanced search capabilities to target specific property characteristics Regular market reports summarizing key trends and opportunities Custom data exports tailored to your specific business needs

AK ~ 1,342 listings AL ~ 6,636 listings AR ~ 4,024 listings AZ ~ 25,782 listings CA ~ 102,833 listings CO ~ 14,333 listings CT ~ 10,515 listings DC ~ 1,988 listings DE ~ 1,528 listings FL ~ 152,258 listings GA ~ 28,248 listings HI ~ 3,447 listings IA ~ 4,557 listings ID ~ 3,426 listings IL ~ 42,642 listings IN ~ 8,634 listings KS ~ 3,263 listings KY ~ 5,166 listings LA ~ 11,522 listings MA ~ 53,624 listings MD ~ 12,124 listings ME ~ 1,754 listings MI ~ 12,040 listings MN ~ 7,242 listings MO ~ 10,766 listings MS ~ 2,633 listings MT ~ 1,953 listings NC ~ 22,708 listings ND ~ 1,268 listings NE ~ 1,847 listings NH ~ 2,672 listings NJ ~ 31,286 listings NM ~ 2,084 listings NV ~ 13,111 listings NY ~ 94,790 listings OH ~ 15,843 listings OK ~ 5,676 listings OR ~ 8,086 listings PA ~ 37,701 listings RI ~ 4,345 listings SC ~ 8,018 listings SD ~ 1,018 listings TN ~ 15,983 listings TX ~ 132,620 listings UT ~ 3,798 listings VA ~ 14,087 listings VT ~ 946 listings WA ~ 15,039 listings WI ~ 7,393 listings WV ~ 1,681 listings WY ~ 730 listings

Grand Total ~ 977,010 listings

Dataset Overview

This dataset provides a snapshot of properties listed for sale in London, sourced from the Rightmove website. It includes various property details such as the number of bedrooms, bathrooms, type of property, and price. The dataset is designed for educational purposes, offering insights into real estate trends and allowing data science enthusiasts to apply their skills in the context of property analysis.

Data Science Applications

This dataset is a valuable resource for students and researchers to practice various data science and analytics techniques. Potential applications include: - Exploratory Data Analysis (EDA): Understanding property distribution across London, price trends, and property types. - Price Prediction Models: Building machine learning models to estimate property prices based on available features. - Real Estate Trend Analysis: Analyzing trends in London’s real estate market, such as price fluctuations or differences in property features by neighborhood. - Text Analysis: Using the property descriptions for natural language processing (NLP) to extract keywords or sentiment related to property value or appeal.

Column Descriptions

• addedOn: Date when the property listing was added or updated on the website.
• title: Brief listing title describing the property, typically including the number of bedrooms and the location.
• descriptionHtml: Detailed description of the property, including features and potentially some promotional language.
• propertyType: Type of property, such as House, Terraced, or Detached.
• sizeSqFeetMax: Maximum size of the property in square feet, if provided.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• listingUpdateReason: Reason for updating the listing (e.g., new listing, price reduction).
• price: Price at which the property is listed for sale.

Ethically Mined Data

This dataset was ethically mined from a publicly accessible website using the APIFY API. All data in this dataset reflects publicly available information about properties listed for sale, with no Personally Identifiable Information (PII) included. The dataset does not include any data that could infringe on individual privacy.

Acknowledgements

• Data Source: Rightmove for providing publicly accessible real estate listings.
• Image Credit: Photo by Douglas Sheppard on Unsplash.

Description

Welcome to the "Real Estate Market Insights: Magic Bricks Web Scraped Dataset" available on Kaggle! This comprehensive dataset provides a wealth of information on real estate properties extracted from the popular real estate portal, Magic Bricks. With this dataset, you can explore and analyze the dynamic and ever-changing landscape of the real estate market.

Dataset Overview:

This dataset comprises meticulously scraped data from Magic Bricks, a prominent platform for buying, selling, and renting real estate properties in various regions. The dataset is regularly updated to ensure it reflects the most current market conditions and trends.

Key Features:

• Property Details: Gain access to a wide range of property details, including property type (apartment, house, commercial, etc.), location, size, and more.
• Price Information: Explore property prices, including listing price, area-based pricing, and price trends.
• Property Amenities: Discover the amenities and features associated with each property, from the number of bedrooms and bathrooms to parking availability and more.
• Property Status: Determine whether a property is available for sale, rent, or lease.

Use Cases:

• Market Analysis: Use this dataset to perform in-depth market analysis to understand price trends, property demand, and supply dynamics.
• Investment Opportunities: Identify potential investment opportunities in different regions based on price trends and property types.
• Location-Based Insights: Explore how property prices and amenities vary across different localities and cities.
• Real Estate Research: Use this dataset for academic research, business strategies, or data-driven decision-making.

Data Collection Method:

The dataset was collected using web scraping techniques, ensuring that it captures a wide array of properties listed on the Magic Bricks platform. Data integrity and accuracy are maintained through regular updates and quality checks.

Data Format:

The dataset is provided in a CSV format, making it easy to import and analyze using various data analysis tools and programming languages.

Disclaimer:

Please note that this dataset is for research and analytical purposes only. It is advisable to verify the data with Magic Bricks or other reliable sources before making any real estate transactions or investment decisions.

Real estate websites emerged as the most popular resource among homebuyers reviewing mortgage financing options in 2024. Approximately 58 percent of respondents shared that they used websites such as Zillow, RE/MAX or Realtor.com when looking at finance options. Referrals and search engines also played a crucial role, according to over half of respondents.

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Oct 2025 about median and USA.

When Zillow was founded back in 2004, it was intended to revolutionise the real estate industry. Frustrated with his home buying experience, Microsoft executive Rich Barton hoped to improve the...

Approximately ** percent of homebuyers in the United States in 2024 found their lender through a referral from a real estate agent, realtor, or broker. Real estate websites emerged as the second most important lead channel, according to ** percent of the respondents.

Snapshot img

US Real Estate Brokerage Software Market Size 2024-2028

The US real estate brokerage software market size is forecast to increase by USD 989.1 million at a CAGR of 9.33% between 2023 and 2028.

The real estate brokerage software market In the US is witnessing significant growth due to several key trends. Residential real estate is continually seeking ways to enhance operational efficiency and client services. companies are responding by introducing innovative real estate software solutions, such as cloud-based deployment, omnichannel communications, and predictive analytics. Furthermore, the availability of open-source real estate brokerage software solutions is providing more options for brokers, enabling them to choose solutions that best fit their business requirements. These trends are driving the growth of the market and are expected to continue shaping its future trajectory.
Cloud-based brokerage software is a popular choice due to its flexibility, scalability, and cost-effectiveness. ROI is a key consideration for brokerages, making software technologies that offer blockchain technology, smart contracts, and contract management software attractive. Internet and smartphone usage continues to rise, driving the demand for user-friendly, mobile-responsive software. The market is expected to grow, offering significant opportunities for companies providing innovative, efficient, and secure solutions.

What will be the size of the US Real Estate Brokerage Software Market during the forecast period?

Request Free Sample

The real estate brokerage industry In the US is experiencing significant digital transformation, with an increasing adoption of software solutions to streamline operations and enhance customer experiences. Digital technologies, including CRM, transaction management, marketing automation, property listing management, and lead generation tools, are becoming essential for real estate brokerages to remain competitive. The complexity of real estate transactions necessitates smart solutions that offer centralized data management, security, and automation.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Customer relationship management
  Transaction management
  Lead generation
  Property management
  Others


Deployment

  Cloud based
  On-premises


Application

  Residential
  Commercial
  Industrial


Geography

  US

By Type Insights

The customer relationship management segment is estimated to witness significant growth during the forecast period.

Real Estate Customer Relationship Management (CRM) software In the US market is a vital tool for brokers and agents to manage client interactions and streamline business processes. CRM systems facilitate lead tracking, client data management, and automated communication workflows, allowing real estate professionals to analyze customer data, schedule follow-ups, and personalize engagement. The increasing importance of customer experience and personalized service In the competitive real estate sector is driving the growth of CRM software.

Additionally, remote work and cloud-based solutions, data analytics, integration with other tools, and emerging technologies like Augmented Reality (AR), Virtual Reality (VR), Machine Learning (ML), and Artificial Intelligence (AI) are enhancing the functionality and efficiency of CRM software In the real estate industry. Enhanced data security features are also crucial for protecting sensitive client information.

Get a glance at the market share of various segments Request Free Sample

The customer relationship management segment was valued at USD 401.70 million in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in the adoption of US Real Estate Brokerage Software Market?

The increasing focus of real estate brokers on enhancing operational efficiency and client services is the key driver of the market.

The Real Estate Brokerage Software Market In the US is witnessing significant growth due to the implementation of digital solutions that streamline operations and enhance customer service. These software solutions cater to the unique requirements of real estate brokerages by offering features such as Customer Relationship Management (CRM), Transaction Management, Marketing Automation, Property Listing Management, and Lead Generation. BoomTown offers an all-in-one plat

This statistic presents the real estate websites that proved most popular among people who hunt for properties to purchase in the United Kingdom in 2015. One ****** of respondents said they would use all three websites: Rightmove, Zoopla and OnTheMarket. However, OnTheMarket only had *** percent of respondents reporting they would use the site alone.

Main Dataset

I scrapped data from 99acres using their (kind of) hidden API. I scrapped almost 10,000+ data using my scrapper app see here.

DESCRIPTION OF THE DATA

• Contains details of properties of Gurgaon, Hyderabad, Mumbai, Kolkata cities of India.
• All datasets of different cities contains almost 10K properties.
• In some datasets, some columns are not available. Sorry!!
• Target column: PRICE

Data Usage

This dataset can be used for various real estate-related tasks, including:

• Property price prediction.
• Market analysis to identify trends and patterns.
• Identifying popular property types and locations.
• Evaluating the impact of property attributes on price.

EXPLANATION OF EACH COLUMNS

NOTE: Not all the columns are important for you so first try to understand your problem statement and then filter this dataset accordingly.

• AGE: The age of the property in years.
• ALT_TAG: An alternative tag or description.
• AMENITIES: Describes the amenities available with the property.
• AREA: The area of the property.
• BALCONY_NUM: The number of balconies in the property.
• BATHROOM_NUM: The number of bathrooms in the property.
• BEDROOM_NUM: The number of bedrooms in the property.
• BROKERAGE: Information about the brokerage or agency associated with the property listing.
• BUILDING_ID: An integer identifier for the building.
• BUILDING_NAME: The name of the building.
• BUILTUP_SQFT: The total built-up area of the property in square feet.
• CARPET_SQFT: The total carpet area of the property in square feet.
• CITY_ID: An identifier for the city in which the property is located.
• CITY: The city where the property is located.
• CLASS_HEADING: A heading for the property class.
• CLASS_LABEL: A label representing the property class.
• CLASS: A classification label for the property.
• COMMON_FURNISHING_ATTRIBUTES: Attributes related to the furnishings and amenities commonly found in the property.
• CONTACT_COMPANY_NAME: The name of the company or agency responsible for the property listing.
• CONTACT_NAME: The name of the contact person associated with the property listing.
• DEALER_PHOTO_URL: URL to a photo or image associated with the property dealer.
• DESCRIPTION: A description of the property listing.
• EXPIRY_DATE: The date when the listing expires.
• FACING: Indicates the direction the property is facing.
• FEATURES: Describes the features of the property.
• FLOOR_NUM: The floor number of the property.
• FORMATTED_LANDMARK_DETAILS: Details of nearby landmarks.
• FORMATTED: Formatted information related to the property.
• FSL_Data: Data related to the property, possibly specific to a particular real estate agency.
• FURNISH: Indicates whether the property is furnished.
• FURNISHING_ATTRIBUTES: Attributes describing the level of furnishing in the property.
• GROUP_NAME: The name of the group or organization to which the property may belong.
• LISTING: Information about the property listing, possibly including its status and other details.
• LOCALITY_WO_CITY: The locality name without the city information.
• LOCALITY: The specific locality or neighborhood where the property is situated.
• location: Additional location information.
• MAP_DETAILS: Contains latitude and longitude information.
• MAX_AREA_SQFT: The maximum area of the property in square feet.
• MAX_PRICE: The maximum price of the property.
• MEDIUM_PHOTO_URL: URL to a medium-sized photo or image of the property.
• metadata: Additional metadata or information about the dataset.
• MIN_AREA_SQFT: The minimum area of the property in square feet.
• MIN_PRICE: The minimum price of the property.
• OWNTYPE: An integer representing the ownership type.
• PD_URL: URL to additional property details.
• PHOTO_URL: URL to photos or images associated with the property.
• POSTING_DATE: The date when the property listing was posted.
• PREFERENCE: Indicates the preference type for the property listing (e.g., "S" for sale).
• PRICE_PER_UNIT_AREA: The price per unit area of the property.
• PRICE_SQFT: The price per square foot of the property.
• PRICE: The price of the property. This is target column for ML.
• PRIMARY_TAGS: Primary tags or labels.
• PRODUCT_TYPE: The type of product listing.
• profile: Profile information related to the property or listing.
• PROJ_ID: An integer identifier for the project.
• PROP_DETAILS_URL: URL to detailed property information.
• PROP_HEADING: A heading or title for the property.
• PROP_ID: A ...

Description This dataset contains detailed real estate listings from the Chittagong Division, Bangladesh, collected in August 2025. It includes key property attributes such as location, price, size, number of bedrooms, number of bathrooms, and additional features. The data is clean, structured, and ready for analysis, making it ideal for machine learning, market trend analysis, and investment research.

Dataset Highlights 1. Region: Chittagong Division, Bangladesh 2. Date Range: August 2025 3. Data Type: Tabular (XLSX format)

Fields Included:

• Property Location (city, area)
• Price (in BDT)
• Area (sq. ft.)
• Property Type (apartment, house, commercial)
• Bedrooms & Bathrooms count
• Additional property features

Possible Use Cases

1. Price Prediction Models: Build regression or machine learning models to forecast property values.
2. Market Trend Analysis: Identify emerging real estate trends in Chittagong.
3. Geospatial Insights: Map property distribution and pricing by location. 4.Comparative Studies: Compare Chittagong’s market with other regions in Bangladesh.

Why This Dataset is Valuable The Bangladeshi real estate market is rapidly growing, and the Chittagong Division is one of its most active hubs. Having structured, up-to-date, and region-specific property data enables analysts, developers, and researchers to make data-driven decisions with confidence.

Column Descriptions sku – Unique identifier for each property listing in the dataset.

price_value – Total listed price of the property (in Bangladeshi Taka).

category – Main property category, such as residential, commercial, or land.

subcategories – Specific property type within the main category (e.g., apartment, house, shop, plot).

floor_area_sqft – Floor area of the property in square feet (sq. ft.).

bedrooms – Number of bedrooms in the property (blank if not applicable, e.g., commercial plots).

bathrooms – Number of bathrooms in the property (blank if not applicable).

occupancy_status – Current occupancy state of the property, such as vacant or occupied.

geo_point – Combined latitude and longitude coordinates in the format longitude,latitude.

link_url – Direct link to the property listing on the source platform.

title – Short headline or title from the property listing.

address – Street name, neighborhood, or locality of the property.

description – Detailed property description as provided by the listing source.

longitude – Longitude coordinate of the property’s location.

latitude – Latitude coordinate of the property’s location.

price_per_sqft – Price of the property per square foot, calculated as price_value / floor_area_sqft.

invalid_data_flag – Data quality indicator:

0 = Valid entry

1 = Potentially invalid or incomplete entry

area_zone – Classified zone or region within Chittagong Division where the property is located.

nearest_hospital – Name of the closest hospital to the property.

dist_to_hospital_km – Distance from the property to the nearest hospital, in kilometers.

nearest_school – Name of the closest school to the property.

dist_to_school_km – Distance from the property to the nearest school, in kilometers.

nearest_shopping – Name of the closest shopping center, plaza, or market.

dist_to_shopping_km – Distance from the property to the nearest shopping area, in kilometers.

nearest_station – Name of the nearest public transportation hub (bus terminal or train station).

dist_to_station_km – Distance from the property to the nearest station, in kilometers.

Special Scoring Fields walkability_score – Measures how pedestrian-friendly the property location is (0–1 scale):

0 = Poor walkability (very few amenities within walking distance)

0.5 = Moderate walkability (some amenities nearby)

1 = Excellent walkability (most amenities within walking distance)

population_density_band – Classification of the surrounding area’s population density:

Low = Sparse population, more open space

Medium = Balanced population density

High = Densely populated, urbanized area

competitive_price_score – Indicates how competitive the property’s price is compared to similar listings:

0 = Above market average (less competitive)

1 = At or below market average (more competitive)

popularity_score – Reflects public interest in the property based on engagement signals (0–1 scale):

0 = Low interest

0.5 = Moderate interest

1 = High interest

lead_hotness_score – Predicts the likelihood of generating buyer leads (0–1 scale):

Values closer to 0 = Low chance of generating leads

Values closer to 1 = High chance of generating leads

Graph and download economic data for Housing Inventory: Active Listing Count in Florida (ACTLISCOUFL) from Jul 2016 to Oct 2025 about active listing, FL, listing, and USA.

The data was scraped from the Magicbricks website. The following are the details of the dataset:

• Title: The title of the property listing.
• Price: The monthly rent of the property.
• Area: The total area of the property in square feet.
• BHK: The number of bedrooms in the property.
• Bathrooms: The number of bathrooms on the property.
• Furnished: Whether the property is furnished or not.
• Balconies: The number of balconies in the property.
• Floor: The floor number of the property.
• Ownership: The type of ownership of the property (i.e., freehold, leasehold, etc.).
• Facing: The direction the property faces.
• Amenities: The amenities that are available in the property or the surrounding area.
• Transaction Type: Whether the property is for sale or rent.
• Property Type: The type of property (i.e., apartment, house, villa, etc.).
• Location: The location of the property.
• Year of Construction: The year the property was built.
• Is Luxury: Whether the property is considered to be a luxury property.
• Description: A brief description of the property.
• Property Image: A link to the property image.

Key points in the dataset are :

1) This dataset can be used to gain insights into the rental market in Mumbai. For example, you could use the data to analyze the average rent for different types of properties, the most popular neighborhoods for renters, or the factors that affect the price of rent. You could also use the data to identify trends in the rental market, such as the increasing popularity of furnished apartments or the rising prices of luxury properties.

2) The dataset could also be used by real estate agents to help their clients find rental properties that meet their needs and budget. Additionally, the data could be used by developers to make informed decisions about the types of properties to build in Mumbai.

3) Overall, this dataset is a valuable resource for anyone who is interested in the rental market in Mumbai. It can be used to gain insights into the market, identify trends, and make informed decisions.

(Disclaimer: The data in this dataset has been gathered from publicly available sources. While the data is believed to be reliable and all privacy policies have been observed, No personal information such as email addresses, mobile numbers, or physical addresses hasn't been collected. I scrape data from the website Magicbricks to study the real estate market of Mumbai. ) Thank you !!!

Graph and download economic data for Housing Inventory: Active Listing Count in California (ACTLISCOUCA) from Jul 2016 to Oct 2025 about active listing, CA, listing, and USA.