Rents for industrial real estate in the U.S. have increased since 2017, with flexible/service space reaching the highest price per square foot in 2024. In just a year, the cost of, flex/service space rose by nearly *****U.S. dollars per square foot. Manufacturing facilities, warehouses, and distribution centers had lower rents and experienced milder growth. Los Angeles, Orange County, and Inland Empire, California, are some of the most expensive markets in the country. Office real estate is pricier Industrial real estate is far from being the most expensive commercial property type. For instance, average rental rates in major U.S. metros for office space are much higher than those for industrial space. This is most likely because office units are generally located in urban areas where there is limited space and thus higher demand, whereas industrial units are more suited to the outskirts of such urban areas. Industrial units, such as warehouses or factories, require much more space because they need to house large, heavy equipment or serve as a storage unit for future shipments. Big-box distribution space is gaining in importance Warehouses and distribution may currently command the lowest average rent per square foot among industrial space types, but the growing popularity of the asset class has earned it considerable gains over the past years. In 2021 and 2022, high occupier demand and insufficient supply led to soaring taking rent of big-box buildings. During that time, the vacancy rate of distribution centers fell below ****percent. The development of industrial and logistics facilities has accelerated since then, with the new supply coming to market, causing the vacancy rate to increase and the pressures on rent to ease.

Context

The purpose of this dataset is to provide updated data on the Zillow Observed Rent Index (ZORI). Most of the Zillow datasets on Kaggle have not been updated in four years, and no other dataset except one contains information related to rent. Providing updated data on this will also allow the community to analyze the effects of COVID-19 on rent prices, which could not be done with previous available data sets.

Content

Zillow Observed Rent Index (ZORI): A smoothed measure of the typical observed market rate rent across a given region. ZORI is a repeat-rent index that is weighted to the rental housing stock to ensure representativeness across the entire market, not just those homes currently listed for-rent. The index is dollar-denominated by computing the mean of listed rents that fall into the 40th to 60th percentile range for all homes and apartments in a given region, which is once again weighted to reflect the rental housing stock. Details available in ZORI methodology. https://www.zillow.com/research/methodology-zori-repeat-rent-27092/

This dataset contains two files. The Metro dataset looks at the median rent prices for large US cities. The ZIP code dataset breaks the US cities down by their ZIP codes. Note that the region IDs in both datasets are only used for tracking purposes. Also, some of the ZIP codes under the Region Name are less than the standard five-digit zip code and unreliable. Even if you add zeros in accounting for possible formatting mistakes. It is recommended to remove these entries since there is no way to identify which ZIP code the entry actually represents. These entries are left in here in case some analyst can solve the issue.

Acknowledgements

Zillow provides many useful open source datasets that relate to housing, which can be found at Zillow Research Data. https://www.zillow.com/research/data/ This dataset was also prompted by an older dataset I came across that only lacked updated data. https://www.kaggle.com/zillow/rent-index Thumbnail and banner picture is from this pixabay artist https://pixabay.com/users/pexels-2286921/

Inspiration

1. Where are the cheapest and most expensive ZIP codes to live?
2. We all know rent increases overtime, but has it been increasing at a faster rate since 2014?
3. If rent has been increasing at a faster rate, what year did it increase the fastest?
4. What cities or ZIP codes are increasing rent the fastest and by how much?
5. Did rent continue to increase during the COVID-19 pandemic, and was it at a faster or slower rate than previous years?

What is Rental Data?

Rental data encompasses detailed information about residential rental properties, including single-family homes, multifamily units, and large apartment complexes. This data often includes key metrics such as rental prices, occupancy rates, property amenities, and detailed property descriptions. Advanced rental datasets integrate listings directly sourced from property management software systems, ensuring real-time accuracy and eliminating reliance on outdated or scraped information.

Additional Rental Data Details

The rental data is sourced from over 20,000 property managers via direct feeds and property management platforms, covering over 30 percent of the national rental housing market for diverse and broad representation. Real-time updates ensure data remains current, while verified listings enhance accuracy, avoiding errors typical of survey-based or scraped datasets. The dataset includes 14+ million rental units with detailed descriptions, rich photography, and amenities, offering address-level granularity for precise market analysis. Its extensive coverage of small multifamily and single-family rentals sets it apart from competitors focused on premium multifamily properties.

Rental Data Includes:

• Property Types
• Single-Family Rentals
• Small Multi-family Units
• Premium Apartments
• 16,000+ ZIP Codes
• 800+ MSAs
• Pricing Trends
• Lease Terms Amenities

Among the ** markets with the largest industrial and logistics real estate inventory in the U.S., Orange County, CA, had the highest rental rate in the first quarter of 2025. The square footage rent of warehouse and distribution centers was ***** U.S. dollars, while for manufacturing sites it was ***** U.S. dollars. In the largest market, Chicago, IL, rents were significantly lower, at ****U.S. dollars.

Context

Zillow operates an industry-leading economics and analytics bureau led by Zillow’s Chief Economist, Dr. Stan Humphries. At Zillow, Dr. Humphries and his team of economists and data analysts produce extensive housing data and analysis covering more than 500 markets nationwide. Zillow Research produces various real estate, rental and mortgage-related metrics and publishes unique analyses on current topics and trends affecting the housing market.

At Zillow’s core is our living database of more than 100 million U.S. homes, featuring both public and user-generated information including number of bedrooms and bathrooms, tax assessments, home sales and listing data of homes for sale and for rent. This data allows us to calculate, among other indicators, the Zestimate, a highly accurate, automated, estimated value of almost every home in the country as well as the Zillow Home Value Index and Zillow Rent Index, leading measures of median home values and rents.

Content

The Zillow Rent Index is the median estimated monthly rental price for a given area, and covers multifamily, single family, condominium, and cooperative homes in Zillow’s database, regardless of whether they are currently listed for rent. It is expressed in dollars and is seasonally adjusted. The Zillow Rent Index is published at the national, state, metro, county, city, neighborhood, and zip code levels.

Zillow produces rent estimates (Rent Zestimates) based on proprietary statistical and machine learning models. Within each county or state, the models observe recent rental listings and learn the relative contribution of various home attributes in predicting prevailing rents. These home attributes include physical facts about the home, prior sale transactions, tax assessment information and geographic location as well as the estimated market value of the home (Zestimate). Based on the patterns learned, these models estimate rental prices on all homes, including those not presently for rent. Because of the availability of Zillow rental listing data used to train the models, Rent Zestimates are only available back to November 2010; therefore, each ZRI time series starts on the same date.

Acknowledgements

The rent index data was calculated from Zillow's proprietary Rent Zestimates and published on its website.

Inspiration

What city has the highest and lowest rental prices in the country? Which metropolitan area is the most expensive to live in? Where have rental prices increased in the past five years and where have they remained the same? What city or state has the lowest cost per square foot?

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

SF Rents

The data comes from Kate Pennington, data.sfgov.org, Vital Signs.

If using Dr. Pennington's data, please cite:

Pennington, Kate (2018). Bay Area Craigslist Rental Housing Posts, 2000-2018. Retrieved from https://github.com/katepennington/historic_bay_area_craigslist_housing_posts/blob/master/clean_2000_2018.csv.zip.

Her methodology can be found at her website.

What impact does new housing have on rents, displacement, and gentrification in the surrounding neighborhood? Read our interview with economist Kate Pennington about her article, "Does Building New Housing Cause Displacement?:The Supply and Demand Effects of Construction in San Francisco." - Kate Pennington on Gentrification and Displacement in San Francisco

All building permits can be found at the Socrata API endpoint.

Data Dictionary

rent.csv

variable	class	description
post_id	character	Unique ID
date	double	date
year	double	year
nhood	character	neighborhood
city	character	city
county	character	county
price	double	price in USD
beds	double	n of beds
baths	double	n of baths
sqft	double	square feet of rental
room_in_apt	double	room in apartment
address	character	address
lat	double	latitude
lon	double	longitude
title	character	title of listing
descr	character	description
details	character	additional details

sf_permits.csv

variable	class	description
permit_number	character	permit_number
permit_type	double	permit_type
permit_type_definition	character	permit_type_definition
permit_creation_date	double	permit_creation_date
block	character	block
lot	character	lot
street_number	double	street_number
street_number_suffix	character	street_number_suffix
street_name	character	street_name
street_suffix	character	street_suffix
unit	double	unit
unit_suffix	character	unit_suffix
description	character	description
status	character	status
status_date	double	status_date
filed_date	double	filed_date
issued_date	double	issued_date
completed_date	double	completed_date
first_construction_document_date	double	first_construction_document_date
structural_notification	character	structural_notification
number_of_existing_stories	double	number_of_existing_stories
number_of_proposed_stories	double	number_of_proposed_stories
voluntary_soft_story_retrofit	character	voluntary_soft_story_retrofit
fire_only_permit	character	fire_only_permit
permit_expiration_date	double	permit_expiration_date
estimated_cost	double	estimated_cost
revised_cost	double	revised_cost
existing_use	character	existing_use
existing_units ...

Context

In order to guarantee transparency in the housing rental market, the result of the exploitation of tax sources of the data on leases of habitual residence is published.

Content

1. BI_ALVHEPCO_TVC_18: Group GGT01 VC rent subset count: Collective Housing as a typology of more surface
2. BI_ALVHEPCO_TVU_18: Rent subset group GGT01 VU: Single Family or Rural Housing as a typology of more surface
3. ALQM2mes_LV_M_VC_18: Monthly rent m2 medium m2 Group GGT01 VC: Collective Housing as a typology of more surface
4. ALQM2mes_LV_25_VC_18: Monthly rent m2 25th percentile Group GGT01 VC: Collective Housing as a typology of more surface
5. ALQM2mes_LV_75_VC_18: Monthly rent m2 75th percentile Group GGT01 VC: Collective Housing as a typology of more surface
6. ALQM2mes_LV_M_VU_18: Monthly rent m2 medium m2 Group GGT01 VU: Single-Family or Rural Housing as a typology of more surface
7. ALQM2mes_LV_25_VU_18: Monthly rent m2 25th percentile Group GGT01 VC: Single-Family or Rural Housing as a typology of more surface
8. ALQM2mes_LV_75_VU_18: Monthly rent m2 75th percentile Group GGT01 VC: Single-Family or Rural Housing as a typology of more surface
9. ALQTBID12_M_VC_18: Monthly rental of all medium-sized real estate Group GGT01 VC: Collective Housing as a typology of more surface
10. ALQTBID12_25_VC_18: Monthly rental of all real estate 25th percentile Group GGT01 VC: Collective Housing as a typology of more surface
11. ALQTBID12_75_VC_18: Monthly rental of all the 75th percentile real estate Group GGT01 VC: Collective Housing as a typology of more surface
12. ALQTBID12_M_VU_18: Monthly rental of all medium-sized real estate Group GGT01 VU: Single-Family or Rural Housing as a typology of more surface
13. ALQTBID12_25_VU_18: Monthly rental of all real estate 25th percentile Group GGT01 VC: Single-Family or Rural Housing as a typology of more surface
14. ALQTBID12_75_VU_18: Monthly rental of all the real estate 75th percentile Group GGT01 VC: Single-Family or Rural Housing as a typology of more surface
15. SLVM2_M_VC_18: Surface m2 medium Group GGT01 VC: Collective Housing as a typology of more surface
16. SLVM2_25_VC_18: Area m2 25th percentile Group GGT01 VC: Collective Housing as a typology of more area
17. SLVM2_75_VC_18: Area m2 75th percentile Group GGT01 VC: Collective Housing as a typology of more area
18. SLVM2_M_VU_18: Surface m2 medium Group GGT01 VU: Single-Family or Rural Housing as a typology of more surface
19. SLVM2_25_VU_18: Area m2 25th percentile Group GGT01 VU: Single-Family or Rural Housing as a typology of more surface

20. SLVM2_75_VU_18: Area m2 75th percentile Group GGT01 VU: Single-Family or Rural Housing as a typology of more surface

21. CUSEC_R: Unique Code of Census Section Population Census VARCHAR(10)

22. CUSEC: Unique Code of Census Section Population Census INT

23. CUMUN: Unique Municipality Code Census Population INT

24. CSEC: Population Census Section Code INT

25. CDIS: District Code Census Population Census INT

26. CMUN: Municipality Code Census Population INT

27. CPRO: Province Code Population Census INT

28. CUSEC: Population Census Section Code INT

29. CCA: Unique Code of the Autonomous Community Population Census INT

30. CUDIS: Unique District Code Census Population Census INT

31. NPRO: Province Name Census Population VARCHAR(50)

32. NCA: Autonomous Community Name Population Census VARCHAR(50)

33. NMUN: Municipality Name Census Population VARCHAR(50)

Acknowledgements

This dataset comes directly from https://www.mitma.gob.es/vivienda/alquiler/indice-alquiler. I have just adapted it for Kaggle.

VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

Inflation-adjusted data are presented to illustrate how home prices 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.

This dataset provides comprehensive information about rental house prices across various locations in India. It includes details such as house type, size, location, city, latitude, longitude, price, currency, number of bathrooms, number of balconies, negotiability of price, price per square foot, verification date, description of the property, security deposit, and status of furnishing (furnished, unfurnished, semi-furnished).

Note: This is Recently scraped data of April 2024.

Dataset Glossary (Column-Wise)

• House Type: Type of house (e.g., apartment, villa, duplex).
• House Size: Size of the house in square feet or square meters.
• Location: Specific area or neighborhood where the property is located.
• City: City in India where the property is situated.
• Latitude: Geographic latitude coordinates of the property location.
• Longitude: Geographic longitude coordinates of the property location.
• Price: Rental price of the house.
• Currency: Currency in which the price is denoted (e.g., INR - Indian Rupees).
• Number of Bathrooms: Total number of bathrooms in the house.
• Number of Balconies: Total number of balconies in the house.
• Negotiability: Indicates whether the price is negotiable (Yes/No).
• Price per Square Foot: Price of the house per square foot.
• Verification Date: Date when the rental information was verified.
• Description: Additional description or details about the property.
• Security Deposit: Amount of security deposit required for renting the property.
• Status: Indicates the furnishing status of the property (furnished, unfurnished, semi-furnished).

Usage

This dataset aims to provide valuable insights into the rental housing market in India, enabling analysis of rental trends, comparison of prices across different locations and property types, and understanding the impact of various factors on rental prices. Researchers, analysts, and policymakers can utilize this dataset for a wide range of applications, including real estate market analysis, urban planning, and economic research.

Acknowledgement

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

Cover Photo by: Playground.ai

About this Dataset

The Zillow Rent Index is the median estimated monthly rental price for a given area, and covers multifamily, single family, condominium, and cooperative homes in Zillow’s database, regardless of whether they are currently listed for rent. It is expressed in dollars and is seasonally adjusted. The Zillow Rent Index is published at the national, state, metro, county, city, neighborhood, and zip code levels. Zillow produces rent estimates (Rent Zestimates) based on proprietary statistical and machine learning models. Within each county or state, the models observe recent rental listings and learn the relative contribution of various home attributes in predicting prevailing rents. These home attributes include physical facts about the home, prior sale transactions, tax assessment information and geographic location as well as the estimated market value of the home (Zestimate). Based on the patterns learned, these models estimate rental prices on all homes, including those not presently for rent. Because of the availability of Zillow rental listing data used to train the models, Rent Zestimates are only available back to November 2010; therefore, each ZRI time series starts on the same date. Acknowledgement The rent index data was calculated from Zillow's proprietary Rent Zestimates and published on its website.

Category

Economic

Keywords

Home,cities,rent,Real Estate,Mortgage

Row Count

13131

Price

Free

Context

Zillow's Economic Research Team collects, cleans and publishes housing and economic data from a variety of public and proprietary sources. Public property record data filed with local municipalities -- including deeds, property facts, parcel information and transactional histories -- forms the backbone of our data products, and is fleshed out with proprietary data derived from property listings and user behavior on Zillow.

The large majority of Zillow's aggregated housing market and economic data is made available for free download at zillow.com/data.

Content

Variable Availability:

Zillow Home Value Index (ZHVI): A smoothed seasonally adjusted measure of the median estimated home value across a given region and housing type. A dollar denominated alternative to repeat-sales indices. Find a more detailed methodology here: http://www.zillow.com/research/zhvi-methodology-6032/

Zillow Rent Index (ZRI): A smoothed seasonally adjusted measure of the median estimated market rate rent across a given region and housing type. A dollar denominated alternative to repeat-rent indices. Find a more detailed methodology here: http://www.zillow.com/research/zillow-rent-index-methodology-2393/

For-Sale Listing/Inventory Metrics: Zillow provides many variables capturing current and historical for-sale listings availability, generally from 2012 to current. These variables include median list prices and inventory counts, both by various property types. Variables capturing for-sale market competitiveness including share of listings with a price cut, median price cut size, age of inventory, and the days a listing spend on Zillow before the sale is final.

Home Sales Metrics: Zillow provides data on sold homes including median sale price by various housing types, sale counts (methodology here: http://www.zillow.com/research/home-sales-methodology-7733/), and a normalized view of sale volume referred to as turnover. The prevalence of foreclosures is also provided as ratio of the housing stock and the share of all sales in which the home was previously foreclosed upon.

For-Rent Listing Metrics: Zillow provides median rents prices and median rent price per square foot by property type and bedroom count.

Housing type definitions:

All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.

Condo/Co-op: Condominium and co-operative homes.

Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.

Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Tiers: By metro, we determine price tier cutoffs that divide the all homes housing stock into thirds using the full distribution of estimated home values. We then estimate real estate metrics within the property sets, Bottom, Middle, and Top, defined by these cutoffs. When reported at the national level, all Bottom Tier homes defined at the metro level are pooled together to form the national bottom tier. The same holds for Middle and Top Tier homes.

Regional Availability:

Zillow metrics are reported for common US geographies including Nation, State, Metro (2013 Census Defined CBSAs), County, City, ZIP code, and Neighborhood.

We provide a crosswalk between colloquial Zillow region names and federally defined region names and linking variables such as County FIPS codes and CBSA codes. Cities and Neighborhoods do not match standard jurisdictional boundaries. Zillow city boundaries reflect mailing address conventions and so are often visually similar to collections of ZIP codes. Zillow neighborhood boundaries can be found here.

Suppression Rules: To ensure reliability of reported values the Zillow Economic Research team applies suppression rules triggered by low sample sizes and excessive volatility. These rules are customized to the metric and region type and explain most missingness found in the provided datasets.

Additional Data Products

The following data products and more are available for free download exclusively at [Zillow.com/Data][1]:

• Zillow Home Value Forecast
• Zillow Rent Forecast
• Negative Equity (the share of mortgaged properties worth less than mortgage balance)
• Zillow Home Price Expectations Survey
• Zillow Housing Aspirations Report
• Zillow Rising Sea Levels Research
• Cash Buyers Time Series
• Buy vs. Rent Breakeven Horizon
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio
• Conventional 30-year Fixed Mortgage Rate, Weekly Time Series
• Jumbo 30-year Fixed Mortgage Rates, Weekly Time Series

Acknowledgements

The mission of the Zillow Economic Research Team is to be the most open, authoritative source for timely and accurate housing data and unbiased insight. We...

VITAL SIGNS INDICATOR
Home Prices (EC7)

FULL MEASURE NAME
Home Prices

LAST UPDATED
December 2022

DESCRIPTION
Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE
Zillow: Zillow Home Value Index (ZHVI) - http://www.zillow.com/research/data/
2000-2021

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2000-2021

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2000-2021

Bureau of Labor Statistics: Consumer Price Index - http://data.bls.gov
2000-2021

US Census ZIP Code Tabulation Areas (ZCTAs) - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html
2020 Census Blocks

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Housing price estimates at the regional-, county-, city- and zip code-level come from analysis of individual home sales by Zillow based upon transaction records. Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. ZHVI is computed from public record transaction data as reported by counties. All standard real estate transactions are included in this metric, including REO sales and auctions. Zillow makes a substantial effort to remove transactions not typically considered a standard sale. Examples of these include bank takeovers of foreclosed properties, title transfers after a death or divorce and non arms-length transactions. Zillow defines all homes as single-family residential, condominium and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that can be owned in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums in that the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Data is adjusted for inflation using Bureau of Labor Statistics metropolitan statistical area (MSA)-specific series. Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index (CPI) 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 the CPI itself.

VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

Inflation-adjusted data are presented to illustrate how home prices 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.

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

The Inventory of Owned and Leased Properties (IOLP) allows users to search properties owned and leased by the General Services Administration (GSA) across the United States, Puerto Rico, Guam and American Samoa.

The Owned and Leased Data Sets include the following data except where noted below for Leases:

• Location Code - GSA’s alphanumeric identifier for the building
• Owned or Leased - Indicates the building is Federally Owned (F) or Leased (L)
• GSA Region - GSA assigned region for building location.
• Street Address/City/State/Zip Code - Building Address.
• Latitude and Longitude - Map coordinates of the building.
• Building Rentable Square Feet - Total Rentable Square Feet in building.
• Available Square Feet - Vacant Space in building.
• Construction Date - Date of year built.
• Congressional District - Congressional District building is located. - Senator/Representative/URL - Senator/Representative of the Congressional District and their web address.

The Leased Data set also includes the following:

• Lease Number - GSA’s alphanumeric identifier for the lease.
• Lease Effective Date - Date lease starts.
• Lease Expiration Date - Date lease expires.

Context

This dataset contains information on rent pricing surrounding Kuala Lumpur and Selangor region, Malaysia. The information was scraped from mudah.my

Content

There are 13 features with one unique ids (ads_id) and one target feature (monthly_rent)

• ads_id: the listing ids (unique)
• prop_name: name of the building/ property
• completion_year: completion/ established year of the property
• monthly_rent: monthly rent in ringgit malaysia (RM)
• location: property location in Kuala Lumpur region
• property_type:property type such as apartment, condominium, flat, duplex, studio, etc
• rooms: number of rooms in the unit
• parking: number of parking space for the unit
• bathroom: number of bathrooms in the unit
• size: total area of the unit in square feet
• furnished: furnishing status of the unit (fully, partial, non-furnished)
• facilities: main facilities available
• additional_facilities: additional facilities (proximity to attraction area, mall, school, shopping, railways, etc)

Acknowledgements The data was scraped from mudah.my

Inspiration I have been living in Kuala Lumpur, Malaysia since 2017, and in the past there was no easy way to understand whether certain unit pricing is making sense or not. With this dataset, I wanted to be able to answer the following questions:

• What are the biggest factor affecting the unit/rent pricing?
• Which location in Kuala Lumpur/ Selangor region that has the highest rent price? etc?

Creating a rental market database for data analysis and machine learning.

How does it work ?

You scrape the property ads (sale or rent) on internet and you get a dataset.

Then 3 fancy solutions are possible:

• Run your webcrawler everyday for a specific place, upload the data in your data warehouse, and monitor the trends in real estate market prices.

• Apply machine learning to your database and get a sense of the relative expensiveness of the properties.

• Localize every property ads on a Google map using color-coded points in order to visualize the most cheap and expensive neighborhoods.

Original Data Source

For the sake of example, and for proximity reasons, we fetched information from a mid-sized Swiss city, called Lausanne, based in the south of Switzerland. The country has the particularity that people get often puzzled by the level of prices swarming almost everywhere in the rental markets. This is mostly related to the very high living standards prevailing over here. So we used one of the public property ads available in this french-speaking part of the country : https://www.homegate.ch/

Because the booming Swiss housing market is mainly a rental market (foreign investments have been riding high for the sales of property, and mortgage loans are closed to record low), I focused on real estate for rent ads in the Homegate website.

Building a webcrawler

In the Kernels section, you will find out how the Python looks like. I used BeautifulSoup and Urllib Python libraries to grab data from the website. As you can figure out, the code is simple, but really efficient.

What you get

In this example, I extracted data as of 03/17/2017, and I named the DataFrame "Output", available in CSV format to make the data compatible with most commonly preferred tools for analysis. It allows you to get a DataFrame with 12 columns:

• the date

• is it a rent or a buy

• the location

• the address of the property

• the zip code

• the available description of the property

• the number of rooms

• the surface

• the floor

• the price

• the source

Machine learning

In the Kernels section, you will see a very simple ML algorithm applied to the dataset in order to the "theoretical" price of each asset, at the end of the code. For the sake of simplicity, I ran a very straightforward linear regression using only 3 features (the 3 only quantitative factors I have at hand) :

• the number of rooms

• the floor

• the surface

I know what you're thinking right at the moment : those 3 features can barely explain the price of a property. Other determinants, such as the location, the neighborhood, the fact that it is outdated, badly maintained by a students roommate partying every night, ... , are of interest when it comes to assessing an appartment. But straightaway, I reduced the model to this.

Google Map display of the property ads and their relative expensiveness

cf Capture.PNG file

Upcoming improvements

• Add new features to machine learning process, especially a dummy variable accounting for the neighborhood to which the property pertains.

• See to what extent a logistic regression could overcome a linear regressor.

• Test more complex machine learning algorithms.

• Display trends in rental property prices, for each neighborhood, after establishing a larger database (with a few weeks of scraped data).

This dataset contains detailed information about rental properties across various locations in the UK. The data was collected by scraping Rightmove, a popular real estate platform. Each entry in the dataset includes the property's address, subdistrict code, rental price, deposit amount, letting type, furnish type, council tax details, property type, number of bedrooms and bathrooms, size in square feet, average distance to the nearest train station, and the count of nearest stations.

Researchers and analysts interested in the UK rental market can utilize this dataset to explore rental trends, pricing variations based on location and property type, amenities preferences, and more. The dataset provides a valuable resource for machine learning models, statistical analysis, and market research in the real estate sector.

Metadata: Source: The data was collected by scraping the Rightmove real estate platform, a leading source for property listings in the UK. Date Range: The dataset covers rental property listings available during the scraping period. Geographical Coverage: Primarily focused on various locations across the UK, providing insights into regional rental markets. Data Fields: Address: The location of the rental property. Subdistrict Code: A code representing the subdistrict or area of the property. Rent: The monthly rental price in GBP (£) for the property. Deposit: The deposit amount required for renting the property. Let Type: Indicates whether the property is available for short-term or long-term rental. Furnish Type: Describes the furnishing status of the property (e.g., furnished, unfurnished, or flexible options). Council Tax: Information about the council tax associated with the property. Property Type: Specifies the type of property, such as apartment, flat, maisonette, etc. Bedrooms: The number of bedrooms in the property. Bathrooms: The number of bathrooms in the property. Size: The size of the property in square feet (sq ft). Average Distance to Nearest Station: The average distance (in miles) to the nearest train station from the property. Nearest Station Count: The count of nearest train stations within a certain distance from the property. Data Quality: The data may contain missing values or "Ask agent" placeholders, which require direct inquiry with agents or landlords for specific information. Potential Uses: The dataset can be used for market analysis, rental price prediction models, understanding property preferences, and exploring the impact of location and amenities on rental properties in the UK.

As the name suggests, this is an Indian rental housing price dataset of Bengaluru, Mumbai, Nagpur, New Delhi, and Pune city. The objective behind creating this dataset was to provide a new indian dataset for practicing with linear regression, ridge regression and random forest regressor.

The dataset has 10 feature columns namely:

• house_type: Title of the property.
• locality: Locality of the property.
• city: City to with the property belong.
• area: Property area in sq ft.
• beds: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• balconies: Number of balconies in the property.
• furnishing: Furnishing status of the property.
• area_rate: Property area rate in Indian Rupees (₹)/sqft.
• rent: Monthly property rent in Indian Rupees (₹).