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.

Multi-family programs that support the creation of new affordable multi-family housing units or the preservation of existing affordable units. Units are counted when financial commitments are approved by the City Council.

Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions:The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Median rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

Analysis of ‘Performance Metrics - Housing & Economic Development - Affordable Multi Family Homes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5f128aa0-ae85-44bb-8324-c0a1ccd2c797 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Multi-family programs that support the creation of new affordable multi-family housing units or the preservation of existing affordable units. Units are counted when financial commitments are approved by the City Council.

--- Original source retains full ownership of the source dataset ---

The Austin City Council approved the Energy Conservation Audit and Disclosure ordinance in 2008 and revised the initiative in April 2011 to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. Multifamily properties older than 10 years are required to perform an audit and report the results to the City of Austin and all residents living in those communities. * The affected area within the City of Austin that is served by Austin Energy

The Commercial Vacancy Multi-Family (MF) dataset shows a variety of current and historical data points regarding the commercial real estate availability, vacancy, and absorption across the entire City of Mesa for all MF properties. This dataset was collected from a third-party source, CoStar, which is a commercial real estate database. CoStar is widely accepted as the trusted, industry standard for commercial real estate data, and while the City of Mesa believes this information to be accurate, we do not claim to have verified every and all information provided. If you require further explanation of some of the real estate terms used in the dataset, please visit the CoStar Terms Glossary below, which explains each term in greater detail. CoStar Terms Glossary: https://www.costar.com/about/support/costar-glossary

VITAL SIGNS INDICATOR
Rent Payments (EC8)

FULL MEASURE NAME
Median rent payment

LAST UPDATED
January 2023

DESCRIPTION
Rent payments refer to the cost of leasing an apartment or home and serves as a measure of housing costs for individuals who do not own a home. The data reflect the median monthly rent paid by Bay Area households across apartments and homes of various sizes and various levels of quality. This differs from advertised rents for available apartments, which usually are higher. Note that rent can be presented using nominal or real (inflation-adjusted) dollar values; data are presented inflation-adjusted to reflect changes in household purchasing power over time.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 2 (1970)
Form STF1 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B25058 (2005-2021; median contract rent)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Rent data reflects median rent payments rather than list rents (refer to measure definition above). American Community Survey 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

1970 Census data for median rent payments has been imputed from quintiles using methodology from California Department of Finance as the source data only provided the mean, rather than the median, monthly rent. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Inflation-adjusted data are presented to illustrate how rent payments 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 CPI itself.

Austin Resource Recovery (ARR) provides the following curbside collection services for single-family homes and multifamily properties with four units or fewer:

Displacement risk indicator classifying community reporting areas according to apartment vacancy rates. Vacancy rates are calculated at the Community Reporting Area level, which are a combination of one or more census tracts. We visualize them as census tracts here, but columns should not be summed to make a total. We include both vacancy rates and change in year over year vacancy rates.Note: Vacancy rate calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

This report is the result of the Austin City Code 6-7’s Energy Conservation Audit and Disclosure Ordinance approved in November 2008 (amended in April 2011) to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. The ordinance meets one of the goals of the Austin Climate Protection Plan, which is to offset 800 megawatts of peak energy demand by 2020. In addition, this report contains information on multi-family properties older than 10 years that are required to perform an energy audit and report the results to the City of Austin and all residents living in those communities. The Austin Energy report quantifies the 2015 energy efficiency findings and the progress towards meeting City Council goals of Resolution 20081106-048.

This dataset is comprised of 21 city owned properties that are within TOD areas city wide for use in the 1000 housing unit challenge. These were ranked and scored from a larger dataset to determine suitability for redevelopment into multi-family housing.

This report is the result of the Austin City Code 6-7’s Energy Conservation Audit and Disclosure Ordinance approved in November 2008 (amended in April 2011) to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. The ordinance meets one of the goals of the Austin Climate Protection Plan, which is to offset 800 megawatts of peak energy demand by 2020. In addition, this report contains information on multi-family properties older than 10 years that are required to perform an energy audit and report the results to the City of Austin and all residents living in those communities. The Austin Energy report quantifies the 2014 energy efficiency findings and the progress towards meeting City Council goals of Resolution 20081106-048.

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023. The dwelling data is based on the Council's property rates database, using a simplified classification schema of Residential Apartment, House/Townhouse and Student Apartment. The count of dwellings per residential building is shown.

For more information about CLUE see http://www.melbourne.vic.gov.au/clue

This EnviroAtlas dataset estimates the percent urban land for each 12-digit hydrologic unit code (HUC) in the conterminous United States. For the purposes of this map, urban land cover includes a variety of development, such as open spaces, parks, golf courses, single family homes, multifamily housing units, retail, commercial, industrial sites, and associated infrastructure. Urban land cover is not confined to city limits. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

VITAL SIGNS INDICATOR
Housing Permits (LU3)

FULL MEASURE NAME
Permitted housing units

LAST UPDATED
February 2023

DESCRIPTION
Housing growth is measured in terms of the number of units that local jurisdictions permit throughout a given year. A permitted unit is a unit that a city or county has authorized for construction.

DATA SOURCE
California Housing Foundation/Construction Industry Research Board (CIRB) - https://www.cirbreport.org/
Construction Review report (1967-2022)

Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) - https://data.bayareametro.gov/Development/HCD-Annual-Progress-Report-Jurisdiction-Summary/nxbj-gfv7
Housing Permits Database (2014-2021)

Census Bureau Building Permit Survey - https://www2.census.gov/econ/bps/County/
Building permits by county (annual, monthly)

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Bay Area housing permits data by single/multi family come from the California Housing Foundation/Construction Industry Research Board (CIRB). Affordability breakdowns from 2014 to 2021 come from the Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) Housing Permits Database.

Single-family housing units include detached, semi-detached, row house and town house units. Row houses and town houses are included as single-family units when each unit is separated from the adjacent unit by an unbroken ground-to-roof party or fire wall. Condominiums are included as single-family units when they are of zero-lot-line or zero-property-line construction; when units are separated by an air space; or, when units are separated by an unbroken ground-to-roof party or fire wall. Multi-family housing includes duplexes, three-to-four-unit structures and apartment-type structures with five units or more. Multi-family also includes condominium units in structures of more than one living unit that do not meet the single-family housing definition.

Each multi-family unit is counted separately even though they may be in the same building. Total units is the sum of single-family and multi-family units. County data is available from 1967 whereas city data is available from 1990. City data is only available for incorporated cities and towns. All permits in unincorporated cities and towns are included under their respective county’s unincorporated total. Permit data is not available for years when the city or town was not incorporated.

Affordable housing is the total number of permitted units affordable to low and very low income households. Housing affordable to very low income households are households making below 50% of the area median income. Housing affordable to low income households are households making between 50% and 80% of the area median income. Housing affordable to moderate income households are households making below 80% and 120% of the area median income. Housing affordable to above moderate income households are households making above 120% of the area median income.

Permit data is missing for the following cities and years:
Clayton, 1990-2007
Lafayette, 1990-2007
Moraga, 1990-2007
Orinda, 1990-2007
San Ramon, 1990

Building permit data for metropolitan areas for each year is the sum of non-seasonally adjusted monthly estimates from the Census Building Permit Survey. The Bay Area values are the sum of the San Francisco-Oakland-Hayward MSA and the San Jose-Sunnyvale-Santa Clara MSA. The counties included in these areas are: San Francisco, Marin, Contra Costa, Alameda, San Mateo, Santa Clara, and San Benito.

Permit values reflect the number of units permitted in each respective year. Note that the data columns come from difference sources. The columns (SFunits, MFunits, TOTALunits, SF_Share and MF_Share) are sourced from CIRB. The columns (VeryLowunits, Lowunits, Moderateunits, AboveModerateunits, VeryLow_Share, Low_Share, Moderate_Share, AboveModerate_Share, Affordableunits and Affordableunits_Share) are sourced from the ABAG Housing Permits Database. Due to the slightly different methodologies that exist within each of those datasets, the total units from each of the two sources might not be consistent with each other.

As shown, three different data sources are used for this analysis of housing permits issued in the Bay Area. Data from the Construction Industry Research Board (CIRB) represents the best available data source for examining housing permits issued over time in cities and counties across the Bay Area, dating back to 1967. In recent years, Annual Progress Report (APR) data collected by the California Department of Housing and Community Development has been available for analyzing housing permits issued by affordability levels. Since CIRB data is only available for California jurisdictions, the U.S. Census Bureau provides the best data source for comparing housing permits issued across different metropolitan areas. Notably, annual permit totals for the Bay Area differ across these three data sources, reflecting the limitations of needing to use different data sources for different purposes.

This dataset outlines the percent changes in median Estimated Market Values and Parcel Counts of apartments by Location (City of Saint Paul vs suburbs) from 2022 to 2023.

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

The percentage of residential properties that have been classified as being vacant and abandoned by the Baltimore City Department of Housing out of all properties. Properties are classified as being vacant and abandoned if: the property is not habitable and appears boarded up or open to the elements; the property was designated as being vacant prior to the current year and still remains vacant; and the property is a multi-family structure where all units are considered to be vacant. Source: Baltimore City Department of Housing and Community Development Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022

The following information will allow you to understand the intent of data provided. This report is in conjunction with Austin City Code 6-7’s Energy Conservation Audit and Disclosure Ordinance approved in November 2008 (amended in April 2011) to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. The ordinance meets one of the primary goals of the Austin Climate Protection Plan which is to offset 800 megawatts of peak energy demand by 2020 to help reduce Austin's carbon footprint. In addition, this report contains information on multi-family properties older than 10 years that are required to perform an energy audit and report the results to the City of Austin and all residents living in those communities. The Austin Energy report quantifies the 2013 energy efficiency findings and the progress towards meeting City Council goals of Resolution 20081106-048.