5 datasets found
  1. Property Rental Listings Dataset

    • kaggle.com
    zip
    Updated Aug 17, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Harshal H (2023). Property Rental Listings Dataset [Dataset]. https://www.kaggle.com/datasets/harshalhonde/property-rental-listings-dataset
    Explore at:
    zip(1010467 bytes)Available download formats
    Dataset updated
    Aug 17, 2023
    Authors
    Harshal H
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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 !!!

  2. t

    SELECTED MONTHLY OWNER COSTS (SMOC) - DP04_MAN_P - Dataset - CKAN

    • portal.tad3.org
    Updated Jul 23, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2023). SELECTED MONTHLY OWNER COSTS (SMOC) - DP04_MAN_P - Dataset - CKAN [Dataset]. https://portal.tad3.org/dataset/selected-monthly-owner-costs-smoc-dp04_man_p
    Explore at:
    Dataset updated
    Jul 23, 2023
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    SELECTED HOUSING CHARACTERISTICS SELECTED MONTHLY OWNER COSTS (SMOC) - DP04 Universe - Housing units with a mortgage and Housing units without a mortgage Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Selected monthly owner costs are the sum of payments for mortgages, deeds of trust, contracts to purchase, or similar debts on the property (including payments for the first mortgage, second mortgages, home equity loans, and other junior mortgages); real estate taxes; fire, hazard, and flood insurance on the property; utilities (electricity, gas, and water and sewer); and fuels (oil, coal, kerosene, wood, etc.). It also includes, where appropriate, the monthly condominium fee for condominiums and mobile home costs (personal property taxes, site rent, registration fees, and license fees). Selected monthly owner costs were tabulated for all owner occupied units, and usually are shown separately for units “with a mortgage” and for units “not mortgaged.”

  3. Census of Population and Housing, 1990: Public Use Microdata Sample: 1/1,000...

    • archive.ciser.cornell.edu
    Updated Jan 2, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Inter-university Consortium for Political and Social Research (2020). Census of Population and Housing, 1990: Public Use Microdata Sample: 1/1,000 Sample [Dataset]. http://doi.org/10.6077/st3z-0740
    Explore at:
    Dataset updated
    Jan 2, 2020
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Bureau of the Census
    Variables measured
    HousingUnit, Individual
    Description

    This dataset, prepared by the Inter-university Consortium for Political and Social Research, comprises 2 percent of the cases in the second release of CENSUS OF POPULATION AND HOUSING, 1990 UNITED STATES: PUBLIC USE MICRODATA SAMPLE: 5-PERCENT SAMPLE (ICPSR 9952). As 2 percent of the 5-percent Public Use Microdata Sample (PUMS), it constitutes a 1-in-1,000 sample, and contains all housing and population variables in the original 5-percent PUMS. Housing variables include area type, state and area of residence, farm/nonfarm status, type of structure, year structure was built, vacancy and boarded-up status, number of rooms and bedrooms, presence or absence of a telephone, presence or absence of complete kitchen and plumbing facilities, type of sewage, water source, and heating fuel used, property value, tenure, year moved into housing unit, type of household/family, type of group quarters, household language, number of persons, related children, own/adopted children, and stepchildren in the household, number of persons and workers in the family, status of mortgage, second mortgage, and home equity loan, number of vehicles available, household income, sales of agricultural products, payments for rent, mortgage, and property tax, condominium fees, mobile home costs, and cost of electricity, water, heating fuel, and flood/fire/hazard insurance. Person variables cover age, sex, relationship to householder, educational attainment, school enrollment, race, Hispanic origin, ancestry, language spoken at home, citizenship, place of birth, year of immigration, place of residence in 1985, marital status, number of children ever born, presence and age of own children, military service, mobility and personal care limitation, work limitation status, employment status, employment status of parents, occupation, industry, class of worker, hours worked last week, weeks worked in 1989, usual hours worked per week, temporary absence from work, place of work, time of departure for work, travel time to work, means of transportation to work, number of occupants in vehicle during ride to work, total earnings, total income, wages and salary income, farm and nonfarm self-employment income, Social Security income, public assistance income, retirement income, and rent, dividends, and net rental income. (Source: downloaded from ICPSR 7/13/10)

    Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06497.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

  4. c

    Census of Population and Housing, 1990 [United States]: Public Use Microdata...

    • archive.ciser.cornell.edu
    • icpsr.umich.edu
    Updated Dec 30, 2019
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Bureau of the Census (2019). Census of Population and Housing, 1990 [United States]: Public Use Microdata Sample: 3-Percent Elderly Sample [Dataset]. http://doi.org/10.6077/3qnt-ap60
    Explore at:
    Dataset updated
    Dec 30, 2019
    Dataset authored and provided by
    Bureau of the Census
    Area covered
    United States
    Variables measured
    HousingUnit, Individual
    Description

    These data from the 1990 Census comprise a sample of households with at least one person 60 years and older, plus a sample of persons 60 years and older in group quarters. The data are grouped into housing variables and person variables. Housing variables include area type, state and area of residence, farm/nonfarm status, type of structure, year structure was built, vacancy and boarded-up status, number of rooms and bedrooms, presence or absence of a telephone, presence or absence of complete kitchen and plumbing facilities, type of sewage facilities, type of water source, type of heating fuel used, property value, tenure, year moved into house/apartment, type of household/family, type of group quarters, household language, number of persons in the household, number of persons and workers in the family, status of mortgage, second mortgage, and home equity loan, number of vehicles available, household income, sales of agricultural products, payments for rent, mortgage and property tax, condominium fees, mobile home costs, and cost of electricity, water, heating fuel, and flood/fire/hazard insurance. Person variables cover age, sex, relationship to householder, educational attainment, school enrollment, race, Hispanic origin, ancestry, language spoken at home, citizenship, place of birth, year of immigration, place of residence in 1985, marital status, number of children ever born, military service, mobility and personal care limitation, work limitation status, employment status, occupation, industry, class of worker, hours worked last week, weeks worked in 1989, usual hours worked per week, temporary absence from work, place of work, time of departure for work, travel time to work, means of transportation to work, total earnings, total income, wages and salary income, farm and nonfarm self-employment income, Social Security income, public assistance income, retirement income, and rent, dividends, and net rental income. (Source: downloaded from ICPSR 7/13/10)

    Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06219.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

  5. c

    Annual Housing Survey, 1983: SMSA Files

    • archive.ciser.cornell.edu
    Updated Feb 8, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Bureau of the Census (2024). Annual Housing Survey, 1983: SMSA Files [Dataset]. http://doi.org/10.6077/wqqq-w694
    Explore at:
    Dataset updated
    Feb 8, 2024
    Dataset authored and provided by
    Bureau of the Census
    Variables measured
    HousingUnit
    Description

    This data collection provides information on the characteristics of the housing inventory in 13 Standard Metropolitan Statistical Areas (SMSAs). Data include year the structure was built, type and number of living quarters, occupancy status, presence of commercial establishments on the property, presence of a garage, and property value. Additional data focus on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air conditioning equipment. Information about housing expenses includes mortgage or rent payments, utility costs, garbage collection fees, property insurance, and real estate taxes as well as repairs, additions, or alterations to the property. Similar data are provided for housing units previously occupied by respondents who had recently moved. Indicators of housing and neighborhood quality are also supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, presence of cracks or holes in walls, ceilings, or floor, reliability of plumbing and heating equipment, and concealed electrical wiring. The presence of storm doors and windows and insulation was also noted. Neighborhood quality variables indicate presence of and objection to street noise, odors, crime, litter, and rundown and abandoned structures, as well as the adequacy of street lighting, public transportation, public parks, schools, shopping facilities, and police and fire protection. Extra information is provided on mobile homes and condominiums including mortgage payments, purchase price, and real estate taxes. In addition to housing characteristics, demographic data for household members are provided, including sex, age, race, income, marital status, and household relationship. Additional data are available for the household head, including Hispanic origin, length of residence, and travel-to-work information. (Source: downloaded from ICPSR 7/13/10)

    Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR08420.v1. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Harshal H (2023). Property Rental Listings Dataset [Dataset]. https://www.kaggle.com/datasets/harshalhonde/property-rental-listings-dataset
Organization logo

Property Rental Listings Dataset

Includes information on property type, location, price, and other details

Explore at:
13 scholarly articles cite this dataset (View in Google Scholar)
zip(1010467 bytes)Available download formats
Dataset updated
Aug 17, 2023
Authors
Harshal H
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

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 !!!

Search
Clear search
Close search
Google apps
Main menu