VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

Virginia (VA) has the 19th highest rent in the country out of 56 states and territories. The Fair Market Rent in Virginia ranges from $701 for a 2-bedroom apartment in Grayson County, VA to $1,765 for a 2-bedroom unit in Washington-Arlington-Alexandria, DC-VA-MD HUD Metro FMR Area.

For FY 2024, the Washington-Arlington-Alexandria, DC-VA-MD HUD Metro FMR Area (Arlington County) rent for a studio or efficiency is $1,772 per month and $3,015 per month to rent a house or an apartment with 4 bedrooms. The average Fair Market Rent for a 2-bedroom home in Virginia is $1,056 per month.

Approximately 15% of Americans qualify for some level of housing assistance. The population in Virginia is around 2,038,847 people. So, there are around 305,827 people in Virginia who could be receiving housing benefits from the HUD. For FY 2025, the Washington-Arlington-Alexandria, DC-VA-MD HUD Metro FMR Area (Arlington County) rent for a studio or efficiency is $2,012 per month and $3,413 per month to rent a house or an apartment with 4 bedrooms. The average Fair Market Rent for a 2-bedroom home in Virginia is $1,059 per month.

Original Source: - Apartment Rental: https://www.kaggle.com/datasets/adithyaawati/apartments-for-rent-classified - Crime Data: https://www.kaggle.com/datasets/michaelbryantds/crimedata

Apartment Rent amenities -- 'basic' or 'luxury' bathrooms -- number of bathrooms bedrooms -- number of bedrooms has_photo -- photo of apartment pets_allowed -- True / False price -- rental price of an apartment square_feet -- size of the apartment cityname -- where the apartment is located state -- where the apartment is located latitude -- where the apartment is located longitude -- where the apartment is located source -- origin web of sourced data time -- data was sourced, originally in Unix format

Crime population -- Mean Population of the area racepctblack, racePctWhite, racePctAsian, racePctHisp-- Social background percentage of the area medIncome, medFamInc-- Median income, Median income of total family murdPerPop, rapesPerPop, robbbPerPop, assaultPerPop, burglPerPop, larcPerPop, autoTheftPerPop, arsonsPerPop, ViolentCrimesPerPop, nonViolPerPop-- Average number of each type of crimes avg_crime

Context

The spectrum of housing options in India is incredibly diverse, spanning from the opulent palaces once inhabited by maharajas of yore, to the contemporary high-rise apartment complexes in bustling metropolitan areas, and even to the humble abodes in remote villages, consisting of modest huts. This wide-ranging tapestry of residential choices reflects the significant expansion witnessed in India's housing sector, which has paralleled the upward trajectory of income levels in the country. According to the findings of the Human Rights Measurement Initiative, India currently achieves 60.9% of what is theoretically attainable, considering its current income levels, in ensuring the fundamental right to housing for its citizens. In the realm of housing arrangements, renting, known interchangeably as hiring or letting, constitutes an agreement wherein compensation is provided for the temporary utilization of a resource, service, or property owned by another party. Within this arrangement, a gross lease is one where the tenant is obligated to pay a fixed rental amount, and the landlord assumes responsibility for covering all ongoing property-related expenses. The concept of renting also aligns with the principles of the sharing economy, as it fosters the utilization of assets and resources among individuals or entities, promoting efficiency and access to housing solutions for a broad spectrum of individuals.

Content

Within this dataset, you will find a comprehensive collection of data pertaining to nearly 4700+ available residential properties, encompassing houses, apartments, and flats offered for rent. This dataset is rich with various attributes, including the number of bedrooms (BHK), rental rates, property size, number of floors, area type, locality, city, furnishing status, tenant preferences, bathroom count, and contact information for the respective point of contact.

Dataset Glossary (Column-Wise)

• BHK: Number of Bedrooms, Hall, Kitchen.
• Rent: Rent of the Houses/Apartments/Flats.
• Size: Size of the Houses/Apartments/Flats in Square Feet.
• Floor: Houses/Apartments/Flats situated in which Floor and Total Number of Floors (Example: Ground out of 2, 3 out of 5, etc.)
• Area Type: Size of the Houses/Apartments/Flats calculated on either Super Area or Carpet Area or Build Area.
• Area Locality: Locality of the Houses/Apartments/Flats.
• City: City where the Houses/Apartments/Flats are Located.
• Furnishing Status: Furnishing Status of the Houses/Apartments/Flats, either it is Furnished or Semi-Furnished or Unfurnished.
• Tenant Preferred: Type of Tenant Preferred by the Owner or Agent.
• Bathroom: Number of Bathrooms.
• Point of Contact: Whom should you contact for more information regarding the Houses/Apartments/Flats.

Structure of the Dataset

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Acknowledgement

This Dataset is created from https://www.magicbricks.com/. If you want to learn more, you can visit the Website.

Cover Photo by: Alexander Andrews on Unsplash

Rent Affordability: Average monthly private rent as a percentage of median monthly salary - (2 bedroom properties) *This indicator has been discontinued

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to Sep 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

This table contains data described by the following dimensions (Not all combinations are available): Geography (247 items: Carbonear; Newfoundland and Labrador; Corner Brook; Newfoundland and Labrador; Grand Falls-Windsor; Newfoundland and Labrador; Gander; Newfoundland and Labrador ...), Type of structure (4 items: Apartment structures of three units and over; Apartment structures of six units and over; Row and apartment structures of three units and over; Row structures of three units and over ...), Type of unit (4 items: Two bedroom units; Three bedroom units; One bedroom units; Bachelor units ...).

The Rental Report time series dataset provides detailed time-series statistics for some key Rental Report data from the June quarter of 1999 to the December quarter of 2017. This specific dataset presents the median rental costs of 2 bedroom flats by the 2016 Local Government Areas geographic level. The rent figures included in the Rental Report are weekly median rents. Median rents represent the midpoint in the distribution of all rents. Fifty per cent of rents are higher than the median and fifty per cent are below the median. The Rental Report provides the most accurate information on the private rental market in Victoria. The data come from records kept by the Residential Tenancies Bond Authority (RTBA). The RTBA is responsible for receiving, registering and refunding all bonds associated with private residential leases in Victoria. For more information please visit the Department of Health and Human Services.

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 !!!

Dataset Overview :

This dataset is created as part of a machine learning mini project on House Price Prediction in India. It includes key features commonly used to predict house prices such as:

1) Number of bedrooms 2) Property type (e.g., Apartment, House) 3) Location 4) Area in square feet 5) Price per square foot 6) Total price

Column Description :

Usage :

This dataset can be used to: --> Build a house price prediction model using ML algorithms --> Perform data visualization or feature correlation --> Understand real estate pricing trends in India

Data about the average market rent for one and two bedroom apartments in San Mateo County. This dataset includes apartment vacancy rates and the US Housing and Urban Development Department's fair market rent for each quarter.

Average asking rent price in select Census Metropolitan Areas by rental unit type. The breakdown by number of bedrooms is provided only for apartments. The results are based on an experimental approach, meaning they are derived from recent methodologies and may be subject to revisions. Quarterly data are available starting from the first quarter of 2019.

The Housing Data Extracted from Homes.com (USA) dataset is a comprehensive collection of 2 million real estate listings sourced from Homes.com, one of the leading real estate platforms in the United States. This dataset offers detailed insights into the U.S. housing market, making it an invaluable resource for real estate professionals, investors, researchers, and analysts.

The dataset contains extensive property details, including location, price, property type (single-family homes, condos, apartments), number of bedrooms and bathrooms, square footage, lot size, year built, and availability status. Organized in CSV format, it provides users with easy access to structured data for analyzing trends, developing investment strategies, or building real estate applications.

Key Features:

• Record Count: 2 million housing listings from across the USA.
• Data Fields: Property address, price, property type, bedrooms, bathrooms, square footage, lot size, year built, and availability.
• Format: CSV format for easy integration with data analysis platforms, machine learning models, and real estate tools.
• Source: Directly sourced from Homes.com’s USA real estate listings.
• Geographical Focus: Comprehensive coverage of properties across all regions of the United States.

Use Cases:

• Real Estate Market Research: Analyze property prices, market trends, and housing demand in various U.S. regions.
• Investment Analysis: Use data to identify high-potential properties and regions for real estate investments.
• Property Comparison: Compare listings by price, location, and features to evaluate market conditions across different cities and states.
• Machine Learning Models: Build predictive models for price forecasting, property valuation, and real estate recommendation systems.
• Content Creation: Create real estate-related content, reports, and insights for the U.S. housing market using up-to-date data.

### The Dataset Description

Pune is IT capital of india. Every software engineer from india wanted to work in this city.So many apartments has rented. I wanted to predict rent for both. 1. for owner who wanted to rent their home/ apartment 2. for customers who wanted to find home on rent

My aim is that predict home rent price on given data.

Content

This are the few columns which I have inside my dataset

• rent (Target Variable) - It indicates the monthly rent in INR.
• Number of bedrooms - represents bedrooms count
• Number of bathrooms - represent bathrooms count
• Number of Balconies - represent the number of balconies.
• Brokerage amount - real-estate charges in INR(once in agreement duration)
• Deposit Amount - Security amount of owner in INR. This will refunded as per agreement (once in agreement duration)
• Maintenance amount - Building maintence charges (monthly)
• Built-Up Area- Area in sq ft
• Super Built-Up Area - area in sq.ft
• Type of Furnishing - Indicates the furnishing type
• Availability for - house/ apartment availble for family, men , female, becholars
• Address - destination of location
• Floor Number - Floor number of appartment
• Home Facing - Direction of door (this is due to India has spritual backgroud & facing of our house is matter)
• Floor-type - Types of floor material used ex.marble etc
• Gate Community - Gate security available or not
• Corner Property - Corner_pro column indicate is property belongs to corner location of road
• Parking Count - How much vehicles can park
• WheelChairFacility - yes/no
• Pet-Friendly - yes/no
• Agreement Duration - In month
• Electricity Bill - Who is going to pay
• Power Backup Facility -
• Property age in years

Inspiration

I wanted answers following questions: 1. Predict a proper rent price 2. Which area has maximum infulace on data

This data was pulled from Rentler.com by Elizabeth on 7/12/2021, 8/12/2021, and 9/6/2021. Her addition to Kaggle from Rentler.com was further sorted by removing the no longer existing dead links column along with other columns such as the description, size of the property in acres, full address, population, and population density as they were not relevant to the work being done with this specific project.

This specific project is based on a hypothetical client who is looking to shop around for her next home and is very budget conscious. She has an idea that she may want a pet in the future and enjoys certain amenities as well, but wants to know how much of a difference in price those amenities will affect her bottom line. She also wants to know which areas of the US will be best for her to consider in terms of price an unit size (sqft).

Questions: Do 2 bed apartments with 1 bathroom have a disproportionate price compared to 2 bed apartments with 2 bathrooms? Do 1 bed apartments with 1 bathroom have a disproportionate price compared to Studio apartments with 1 bathroom? Does Air Conditioning play a role in overall price of a rental unit (regardless of size)? Does having a Dishwasher play a role in overall price of a rental unit (regardless of size)? Does having a Washer/Dryer play a role in overall price of a rental unit (regardless of size)? Does allowing pets play a role in overall price of a rental unit (regardless of size)? What are the top 10 cities to live in in regard to price per square foot? Price with the most amenities? What are the bottom 10 cities to live in in regard to price per square foot? Price with the most/least amenities?

It was also cleaned to minimize as many outliers and null values as possible to better support any hypotheses moving forward. The dataset here includes only listings for 1,2 and 3 bedroom rentals with between 1 and 3.5 baths. All duplicates comparing the fields of Street Address, Beds, Baths, SqFt and Price were removed as well as any fields that contained blanks in the category of SqFt. In order to avoid any unnecessary outliers in exploration, SqFt was limited to 2700, Price to under $3000 and the deposit could only be <= two times the price. The original dataset contained over 270k records and this was cleaned and sorted to just under 100k

Median monthly rental prices for the private rental market in England by bedroom category, region and administrative area, calculated using data from the Valuation Office Agency and Office for National Statistics.

FipeZap: House Asking Price Index: Rent: Pará: Belém: 2 Bedrooms data was reported at 175.940 Jan2022=100 in Jul 2024. This records an increase from the previous number of 170.818 Jan2022=100 for Jun 2024. FipeZap: House Asking Price Index: Rent: Pará: Belém: 2 Bedrooms data is updated monthly, averaging 147.127 Jan2022=100 from Jan 2022 (Median) to Jul 2024, with 31 observations. The data reached an all-time high of 178.598 Jan2022=100 in Feb 2024 and a record low of 100.000 Jan2022=100 in Jan 2022. FipeZap: House Asking Price Index: Rent: Pará: Belém: 2 Bedrooms data remains active status in CEIC and is reported by Institute of Economic Research Foundation. The data is categorized under Brazil Premium Database’s Real Estate Sector – Table BR.RKB005: Real Estate: FipeZap House Asking Price Index: Rent.

New Delhi Rental Property Dataset – Makaan.com

This dataset consists of detailed rental listings of 14,000 residential properties located in New Delhi, India, collected from the real estate portal makaan.com. The data was gathered using web scraping techniques involving BeautifulSoup4 and Regular Expressions (Regex). A total of 700 pages were scraped to compile this dataset.

This dataset is especially useful for beginners in data science looking to explore and practice concepts such as data cleaning, preprocessing, feature engineering, exploratory data analysis, and even machine learning. It contains a variety of real-world attributes related to rental properties, providing a solid foundation for understanding housing market trends in urban India.

📄 Dataset Features

Below are the features included in the dataset:

• Size : The size configuration of the property, usually indicating the number of rooms. For example: 1, 2, 3 BHK or RK (Room-Kitchen unit).

• Size_unit : The unit associated with the property size — either BHK (Bedroom-Hall-Kitchen) or RK (Room-Kitchen). Helps distinguish full apartments from studio-type accommodations.

• Property_type : The type or category of the property. Examples include Apartment, Independent House, Independent Floor, and other residential types listed on makaan.com.

• Location : The neighborhood or locality within New Delhi where the property is situated. Useful for geographic and locality-specific analysis.

• Seller_name : The name of the individual or organization who listed the property on the platform. This can help identify frequent sellers or real estate agencies.

• Seller_type : Classification of the seller into categories such as Owner, Agent, or Builder. Offers insights into listing authenticity and marketing patterns.

• Rent_price : The monthly rental cost of the property in Indian Rupees (INR). A core variable for price analysis and budget comparisons.

• Area_sqft : The built-up or carpet area of the property in square feet. Important for calculating price per square foot and comparing property sizes.

• Status : Indicates the current condition of the property. Can be one of:

  • Refurbished
  • Semi-refurbished
  • Unfurbished

• Security_deposit : The amount required as a refundable security deposit, often a multiple of the monthly rent.

• Bathroom : The total number of bathrooms in the property. Useful for assessing the comfort level, especially for families or shared accommodations.

• Facing_direction : The directional orientation of the property (e.g., East, West, North-East). This is a significant factor in Indian housing due to preferences based on sunlight, ventilation, and Vastu Shastra principles.

Feel free to use this dataset for hands-on practice in data exploration, visualization, modeling, or even creating a rental recommendation system. Let me know if you’d like help getting started! :

The Rental Report time series dataset provides detailed time-series statistics for some key Rental Report data from the March quarter of 2000 to the December quarter of 2017. This specific dataset presents statistics on affordable 2 bedroom rental properties by the 2016 Local Government Areas geographic level.

Affordable rental properties are those within 30 per cent of gross income for low-income households. The rental thresholds are taken from the household incomes for whom that number of bedrooms is a minimum:

• For one-bedroom properties, we have taken the income of singles on Newstart allowance;

• For two-bedroom properties, we have taken a single parent pensioner with one child aged under 5;

• For three-bedroom properties, we have taken a couple on Newstart with two children;

• For four-bedroom properties, we have taken a couple on Newstart with four children.

  The Rental Report provides the most accurate information on the private rental market in Victoria. The data come from records kept by the Residential Tenancies Bond Authority (RTBA). The RTBA is responsible for receiving, registering and refunding all bonds associated with private residential leases in Victoria.

For more information please visit the Department of Health and Human Services.

East Bay Housing Prices: Room Shares vs Apartments

A Tale of Two Cities

By [source]

About this dataset

This dataset contains information on housing prices in the East Bay area of California. It includes data on both room shares and apartments, as well as the square footage, number of bedrooms, and posted date for each listing. With this data, you can compare the average price of a room share to that of an apartment in each neighborhood, and see how housing prices have changed over time

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How to use the dataset

This dataset contains information on housing prices in the East Bay area of California. The data includes information on the type of housing (room shares vs. apartments), the price, the number of bedrooms, and the square footage.

To use this dataset, you can download it as a CSV file and then use a spreadsheet program to open it

Research Ideas

• This dataset can be used to predict housing prices in the East Bay area.
• This dataset can be used to study the trends in housing prices in the East Bay area.
• This dataset can be used to compare the prices of room shares and apartments in the East Bay area

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: eb_apts_1642_Jan_2_19_clean.csv | Column name | Description | |:--------------------|:-------------------------------------------------| | posted | The date the listing was posted. (Date) | | neighborhood | The neighborhood the listing is in. (String) | | post title | The title of the post. (String) | | number bedrooms | The number of bedrooms in the listing. (Integer) | | sqft | The square footage of the listing. (Integer) | | URL | The URL of the listing (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .