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Vital Signs: Time in Congestion - Corridor (Updated October 2018)
data.bayareametro.gov | Last Updated 2018-10-24T00:31:33.000ZVITAL SIGNS INDICATOR Time Spent in Congestion (T7) FULL MEASURE NAME Time Spent in Congestion LAST UPDATED October 2018 DATA SOURCE MTC/Iteris Congestion Analysis No link available CA Department of Finance Forms E-8 and E-5 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/ http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/ CA Employment Division Department: Labor Market Information http://www.labormarketinfo.edd.ca.gov/ CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) Time spent in congestion measures the hours drivers are in congestion on freeway facilities based on traffic data. In recent years, data for the Bay Area comes from INRIX, a company that collects real-time traffic information from a variety of sources including mobile phone data and other GPS locator devices. The data provides traffic speed on the region’s highways. Using historical INRIX data (and similar internal datasets for some of the earlier years), MTC calculates an annual time series for vehicle hours spent in congestion in the Bay Area. Time spent in congestion is defined as the average daily hours spent in congestion on Tuesdays, Wednesdays and Thursdays during peak traffic months on freeway facilities. This indicator focuses on weekdays given that traffic congestion is generally greater on these days; this indicator does not capture traffic congestion on local streets due to data unavailability. This congestion indicator emphasizes recurring delay (as opposed to also including non-recurring delay), capturing the extent of delay caused by routine traffic volumes (rather than congestion caused by unusual circumstances). Recurring delay is identified by setting a threshold of consistent delay greater than 15 minutes on a specific freeway segment from vehicle speeds less than 35 mph. This definition is consistent with longstanding practices by MTC, Caltrans and the U.S. Department of Transportation as speeds less than 35 mph result in significantly less efficient traffic operations. 35 mph is the threshold at which vehicle throughput is greatest; speeds that are either greater than or less than 35 mph result in reduced vehicle throughput. This methodology focuses on the extra travel time experienced based on a differential between the congested speed and 35 mph, rather than the posted speed limit. To provide a mathematical example of how the indicator is calculated on a segment basis, when it comes to time spent in congestion, 1,000 vehicles traveling on a congested segment for a 1/4 hour (15 minutes) each, [1,000 vehicles x ¼ hour congestion per vehicle= 250 hours congestion], is equivalent to 100 vehicles traveling on a congested segment for 2.5 hours each, [100 vehicles x 2.5 hour congestion per vehicle = 250 hours congestion]. In this way, the measure captures the impacts of both slow speeds and heavy traffic volumes. MTC calculates two measures of delay – congested delay, or delay that occurs when speeds are below 35 miles per hour, and total delay, or delay that occurs when speeds are below the posted speed limit. To illustrate, if 1,000 vehicles are traveling at 30 miles per hour on a one mile long segment, this would represent 4.76 vehicle hours of congested delay [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 35 miles per hour) = 33.33 vehicle hours – 28.57 vehicle hours = 4.76 vehicle hours]. Considering that the posted speed limit on the segment is 60 miles per hour, total delay would be calculated as 16.67 vehicle hours [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 60 miles per hour) = 33.33 vehicle hours – 16.67 vehicle hours = 16.67 vehicle hours]. Data sources listed above were used to calculate per-capita and per-worker statistics. Top congested corridors are ranked by total vehicle hours of delay, meaning that the highlighted corridors reflect a combination of slow speeds and heavy t
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RHNA Draft Performance Measures - Categorized v2
data.bayareametro.gov | Last Updated 2020-05-13T23:48:02.000ZDataset describes jurisdictions according to 8 measures which will be used to gauge RHNA performance. Each measure has been categorized into discrete buckets, for use in summarizing the jurisdiction-specific RHNA allocation. The core metrics mapping directly to CA HCD objective metrics include: Measure 1a: Lower Income RHNA in High Cost Areas Measure 1b: Lower Income RHNA in Single-Family Home Areas Measure 2a: Household Growth in Job Centers Measure 3a: Lower Income RHNA in Jobs-Housing Fit Imbalanced Areas Measure 4a: Lower Income RHNA in Areas with High Share of Low-Income Households Measure 4b: Lower Income RHNA in Areas with High Share of High-Income Households Measure 5a: Lower Income RHNA in High Opportunity Areas Measure 5b: Household Growth in High Divergence Score Areas with High-Income Households Measure 6b: Household Growth in High Hazard Risk Areas
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Vital Signs: Greenhouse Gas Emissions - by county
data.bayareametro.gov | Last Updated 2018-07-10T01:36:51.000ZVITAL SIGNS INDICATOR Greenhouse Gas Emissions (EN3) FULL MEASURE NAME Greenhouse gas emissions from primary sources LAST UPDATED August 2017 DESCRIPTION Greenhouse gas emissions refer to carbon dioxide and other chemical compounds that contribute to global climate change. Vital Signs tracks greenhouse gas emissions linked to consumption from the three largest sources in the region: surface transportation, electricity consumption, and natural gas consumption. This measure helps track progress towards achieving regional greenhouse gas reduction targets, including the region's per-capita greenhouse gas target for surface transportation under Senate Bill 375. This dataset includes emissions estimates on the regional and county levels. DATA SOURCE California Energy Commission: Retail Fuel Outlet Annual Reporting 2010-2012, 2015 Form CEC-A15 http://www.energy.ca.gov/almanac/transportation_data/gasoline/piira_retail_survey.html Energy Information Administration: CO2 Conversion Data 2015 conversion purposes only; consistent over time http://www.eia.gov/tools/faqs/faq.cfm?id=307&t=11 California Energy Commission: Electricity Consumption by County 2003-2015 http://www.ecdms.energy.ca.gov/elecbycounty.aspx Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2003-2013 audited by the Climate Registry; conversion purposes only https://www.pge.com/includes/docs/pdfs/shared/environment/calculator/pge_ghg_emission_factor_info_sheet.pdf Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2014-2015 audited by the Climate Registry; conversion purposes only http://www.pgecurrents.com/2017/02/09/pge-cuts-carbon-emissions-with-clean-energy-2/ California Energy Commission: Natural Gas Consumption by County 1990-2015 http://www.ecdms.energy.ca.gov/gasbycounty.aspx Pacific Gas & Electric Company: Climate Footprint Calculator 2015 conversion purposes only; consistent over time https://www.pge.com/includes/docs/pdfs/about/environment/calculator/assumptions.pdf California Department of Finance: Population and Housing Estimates 1990-2015 http://www.dof.ca.gov/research/demographic/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) For surface transportation, the dataset is based on a survey of fueling stations, the vast majority of which respond to the survey; the Energy Commission corrects for non-response bias by imputing the remaining share of fuel sales. Note that 2014 data was excluded to data abnormalities for several counties in the region; methodology improvements in 2012 affected estimated by +/- 5% according to CEC estimates. For years 2013 and 2014, a linear trendline assumption was used instead between 2012 and 2015 data points. Greenhouse gas emissions are calculated based on the gallons of gasoline and diesel sales, relying upon standardized Energy Information Administration conversion rates for E10 fuel (gasoline with 10% ethanol) and standard diesel. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; there may be a slight bias in the data given that a fraction of fuel sold in a given county may be purchased by non-residents. For electricity consumption, the dataset is based on electricity consumption data for the nine Bay Area counties; note that this is different than electricity production as the region imports electricity. Because such data is not disaggregated by utility provider, a simple assumption is made that electricity consumed has the greenhouse gas emissions intensity (on a kilowatt-hour basis) of Pacific Gas & Electric, the primary electricity provider in the Bay Area. For this reason, with the small but growing market share of low- and zero-GHG community choice aggregation (CCA) providers, the greenhouse gas emissions estimate in more recent years may be slightly overe
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Vital Signs: Travel Time Reliability – by corridor
data.bayareametro.gov | Last Updated 2018-07-06T18:04:55.000ZVITAL SIGNS INDICATOR Travel Time Reliability (T9) FULL MEASURE NAME Freeway buffer time index LAST UPDATED May 2017 DESCRIPTION Transportation planners quantify the travel time reliability of a given route by means of a buffer time index (BTI). BTI is a measure of the amount of time, over and above the average travel time, that a driver would need to budget to ensure on-time arrival at the desired destination, with a 95 percent confidence rate. BTI is expressed as a fraction of the average travel time – the lower the BTI, the more reliable the trip. This measure focuses solely on the regional freeway system, as no comparable data is available on the local street network. The dataset includes metropolitan area, regional and freeway corridor tables. DATA SOURCE Metropolitan Transportation Commission/INRIX: Freeway Reliability Analysis California Department of Transportation: Annual Traffic Volume Reports http://traffic-counts.dot.ca.gov CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) Buffer time index was calculated based on the average reliability of each freeway segment over the course of one-hour time windows. Peak periods were defined as 6 AM to 10 AM and 3 PM to 7 PM. Regional BTI was calculated using traffic volumes on each segment and weighting BTI accordingly across the network.
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Vital Signs: Time in Congestion - Corridor Shapefile (Updated October 2018)
data.bayareametro.gov | Last Updated 2018-10-24T00:30:32.000ZVITAL SIGNS INDICATOR Time Spent in Congestion (T7) FULL MEASURE NAME Time Spent in Congestion LAST UPDATED October 2018 DATA SOURCE MTC/Iteris Congestion Analysis No link available CA Department of Finance Forms E-8 and E-5 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/ http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/ CA Employment Division Department: Labor Market Information http://www.labormarketinfo.edd.ca.gov/ CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) Time spent in congestion measures the hours drivers are in congestion on freeway facilities based on traffic data. In recent years, data for the Bay Area comes from INRIX, a company that collects real-time traffic information from a variety of sources including mobile phone data and other GPS locator devices. The data provides traffic speed on the region’s highways. Using historical INRIX data (and similar internal datasets for some of the earlier years), MTC calculates an annual time series for vehicle hours spent in congestion in the Bay Area. Time spent in congestion is defined as the average daily hours spent in congestion on Tuesdays, Wednesdays and Thursdays during peak traffic months on freeway facilities. This indicator focuses on weekdays given that traffic congestion is generally greater on these days; this indicator does not capture traffic congestion on local streets due to data unavailability. This congestion indicator emphasizes recurring delay (as opposed to also including non-recurring delay), capturing the extent of delay caused by routine traffic volumes (rather than congestion caused by unusual circumstances). Recurring delay is identified by setting a threshold of consistent delay greater than 15 minutes on a specific freeway segment from vehicle speeds less than 35 mph. This definition is consistent with longstanding practices by MTC, Caltrans and the U.S. Department of Transportation as speeds less than 35 mph result in significantly less efficient traffic operations. 35 mph is the threshold at which vehicle throughput is greatest; speeds that are either greater than or less than 35 mph result in reduced vehicle throughput. This methodology focuses on the extra travel time experienced based on a differential between the congested speed and 35 mph, rather than the posted speed limit. To provide a mathematical example of how the indicator is calculated on a segment basis, when it comes to time spent in congestion, 1,000 vehicles traveling on a congested segment for a 1/4 hour (15 minutes) each, [1,000 vehicles x ¼ hour congestion per vehicle= 250 hours congestion], is equivalent to 100 vehicles traveling on a congested segment for 2.5 hours each, [100 vehicles x 2.5 hour congestion per vehicle = 250 hours congestion]. In this way, the measure captures the impacts of both slow speeds and heavy traffic volumes. MTC calculates two measures of delay – congested delay, or delay that occurs when speeds are below 35 miles per hour, and total delay, or delay that occurs when speeds are below the posted speed limit. To illustrate, if 1,000 vehicles are traveling at 30 miles per hour on a one mile long segment, this would represent 4.76 vehicle hours of congested delay [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 35 miles per hour) = 33.33 vehicle hours – 28.57 vehicle hours = 4.76 vehicle hours]. Considering that the posted speed limit on the segment is 60 miles per hour, total delay would be calculated as 16.67 vehicle hours [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 60 miles per hour) = 33.33 vehicle hours – 16.67 vehicle hours = 16.67 vehicle hours]. Data sources listed above were used to calculate per-capita and per-worker statistics. Top congested corridors are ranked by total vehicle hours of delay, meaning that the highlighted corridors reflect a combination of slow speeds and heavy t
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Vital Signs: Greenhouse Gas Emissions - Bay Area
data.bayareametro.gov | Last Updated 2018-07-10T01:38:19.000ZVITAL SIGNS INDICATOR Greenhouse Gas Emissions (EN3) FULL MEASURE NAME Greenhouse gas emissions from primary sources LAST UPDATED August 2017 DESCRIPTION Greenhouse gas emissions refer to carbon dioxide and other chemical compounds that contribute to global climate change. Vital Signs tracks greenhouse gas emissions linked to consumption from the three largest sources in the region: surface transportation, electricity consumption, and natural gas consumption. This measure helps track progress towards achieving regional greenhouse gas reduction targets, including the region's per-capita greenhouse gas target for surface transportation under Senate Bill 375. This dataset includes emissions estimates on the regional and county levels. DATA SOURCE California Energy Commission: Retail Fuel Outlet Annual Reporting 2010-2012, 2015 Form CEC-A15 http://www.energy.ca.gov/almanac/transportation_data/gasoline/piira_retail_survey.html Energy Information Administration: CO2 Conversion Data 2015 conversion purposes only; consistent over time http://www.eia.gov/tools/faqs/faq.cfm?id=307&t=11 California Energy Commission: Electricity Consumption by County 2003-2015 http://www.ecdms.energy.ca.gov/elecbycounty.aspx Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2003-2013 audited by the Climate Registry; conversion purposes only https://www.pge.com/includes/docs/pdfs/shared/environment/calculator/pge_ghg_emission_factor_info_sheet.pdf Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2014-2015 audited by the Climate Registry; conversion purposes only http://www.pgecurrents.com/2017/02/09/pge-cuts-carbon-emissions-with-clean-energy-2/ California Energy Commission: Natural Gas Consumption by County 1990-2015 http://www.ecdms.energy.ca.gov/gasbycounty.aspx Pacific Gas & Electric Company: Climate Footprint Calculator 2015 conversion purposes only; consistent over time https://www.pge.com/includes/docs/pdfs/about/environment/calculator/assumptions.pdf California Department of Finance: Population and Housing Estimates 1990-2015 http://www.dof.ca.gov/research/demographic/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) For surface transportation, the dataset is based on a survey of fueling stations, the vast majority of which respond to the survey; the Energy Commission corrects for non-response bias by imputing the remaining share of fuel sales. Note that 2014 data was excluded to data abnormalities for several counties in the region; methodology improvements in 2012 affected estimated by +/- 5% according to CEC estimates. For years 2013 and 2014, a linear trendline assumption was used instead between 2012 and 2015 data points. Greenhouse gas emissions are calculated based on the gallons of gasoline and diesel sales, relying upon standardized Energy Information Administration conversion rates for E10 fuel (gasoline with 10% ethanol) and standard diesel. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; there may be a slight bias in the data given that a fraction of fuel sold in a given county may be purchased by non-residents. For electricity consumption, the dataset is based on electricity consumption data for the nine Bay Area counties; note that this is different than electricity production as the region imports electricity. Because such data is not disaggregated by utility provider, a simple assumption is made that electricity consumed has the greenhouse gas emissions intensity (on a kilowatt-hour basis) of Pacific Gas & Electric, the primary electricity provider in the Bay Area. For this reason, with the small but growing market share of low- and zero-GHG community choice aggregation (CCA) providers, the greenhouse gas emissions estimate in more recent years may be slightly ov
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Vital Signs: Time in Congestion - Bay Area (updated October 2018)
data.bayareametro.gov | Last Updated 2018-10-24T00:32:13.000ZVITAL SIGNS INDICATOR Time Spent in Congestion (T7) FULL MEASURE NAME Time Spent in Congestion LAST UPDATED October 2018 DATA SOURCE MTC/Iteris Congestion Analysis No link available CA Department of Finance Forms E-8 and E-5 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/ http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/ CA Employment Division Department: Labor Market Information http://www.labormarketinfo.edd.ca.gov/ CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) Time spent in congestion measures the hours drivers are in congestion on freeway facilities based on traffic data. In recent years, data for the Bay Area comes from INRIX, a company that collects real-time traffic information from a variety of sources including mobile phone data and other GPS locator devices. The data provides traffic speed on the region’s highways. Using historical INRIX data (and similar internal datasets for some of the earlier years), MTC calculates an annual time series for vehicle hours spent in congestion in the Bay Area. Time spent in congestion is defined as the average daily hours spent in congestion on Tuesdays, Wednesdays and Thursdays during peak traffic months on freeway facilities. This indicator focuses on weekdays given that traffic congestion is generally greater on these days; this indicator does not capture traffic congestion on local streets due to data unavailability. This congestion indicator emphasizes recurring delay (as opposed to also including non-recurring delay), capturing the extent of delay caused by routine traffic volumes (rather than congestion caused by unusual circumstances). Recurring delay is identified by setting a threshold of consistent delay greater than 15 minutes on a specific freeway segment from vehicle speeds less than 35 mph. This definition is consistent with longstanding practices by MTC, Caltrans and the U.S. Department of Transportation as speeds less than 35 mph result in significantly less efficient traffic operations. 35 mph is the threshold at which vehicle throughput is greatest; speeds that are either greater than or less than 35 mph result in reduced vehicle throughput. This methodology focuses on the extra travel time experienced based on a differential between the congested speed and 35 mph, rather than the posted speed limit. To provide a mathematical example of how the indicator is calculated on a segment basis, when it comes to time spent in congestion, 1,000 vehicles traveling on a congested segment for a 1/4 hour (15 minutes) each, [1,000 vehicles x ¼ hour congestion per vehicle= 250 hours congestion], is equivalent to 100 vehicles traveling on a congested segment for 2.5 hours each, [100 vehicles x 2.5 hour congestion per vehicle = 250 hours congestion]. In this way, the measure captures the impacts of both slow speeds and heavy traffic volumes. MTC calculates two measures of delay – congested delay, or delay that occurs when speeds are below 35 miles per hour, and total delay, or delay that occurs when speeds are below the posted speed limit. To illustrate, if 1,000 vehicles are traveling at 30 miles per hour on a one mile long segment, this would represent 4.76 vehicle hours of congested delay [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 35 miles per hour) = 33.33 vehicle hours – 28.57 vehicle hours = 4.76 vehicle hours]. Considering that the posted speed limit on the segment is 60 miles per hour, total delay would be calculated as 16.67 vehicle hours [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 60 miles per hour) = 33.33 vehicle hours – 16.67 vehicle hours = 16.67 vehicle hours]. Data sources listed above were used to calculate per-capita and per-worker statistics. Top congested corridors are ranked by total vehicle hours of delay, meaning that the highlighted corridors reflect a combination of slow speeds and heavy t
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Vital Signs: Housing Affordability - Metro
data.bayareametro.gov | Last Updated 2019-10-25T20:42:43.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.
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RHNA Draft Performance Measures - Categorized v3
data.bayareametro.gov | Last Updated 2020-09-08T05:03:52.000ZDataset describes jurisdictions according to 8 measures which will be used to gauge RHNA performance. Each measure has been categorized into two groups, for most the top 25 cities in a category versus the remainder. The core metrics mapping directly to CA HCD objective metrics include: Percent of RHNA as lower income units for jurisdictions with the highest housing costs. Measure: Housing costs Share of homeowners living in units valued above $750,000. Threshold grouping: upper third vs rest Percent of RHNA as lower income units for jurisdictions with highest percent of single-family homes. Measure: Percent single family Threshold grouping: upper third vs rest Total unit allocations for jurisdictions with the most jobs. Measure: Total Jobs Threshold grouping: upper third vs rest Allocations of lower income units for jurisdictions with the most low-wage jobs. Measure: Low wage jobs Threshold grouping: upper third vs rest Percent of RHNA as lower income units for jurisdictions with the highest ratio of low-wage jobs to housing units affordable to low-wage workers. Measure: jobs-housing fit Threshold grouping: upper third vs rest Percent of RHNA as lower income units for low-income jurisdictions. Measure: median household income. Low income threshold: bottom third Percent of RHNA as lower income units for high-income jurisdiction. Measure: median household income. High income threshold: upper third Percent of RHNA as lower income units for jurisdictions with the most households in High Resource/Highest Resource tracts. Measure: Share households in HRAs Threshold grouping: upper third vs rest
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Vital Signs: Housing Affordability - County Overall
data.bayareametro.gov | Last Updated 2019-10-25T20:43:28.000ZHousing Affordability (EQ2) FULL MEASURE NAME Housing Affordability LAST UPDATED October 2018 DATA SOURCE U.S Census Bureau: Decennial Census Form STF3 – https://nhgis.org (1980-1990) Form SF3a – https://nhgis.org (2000) U.S. Census Bureau: American Community Survey Form B25074 (2009-2017) Form B25095 (2009-2017) http://api.census.gov Image: Flickr (Creative Commons license), Photographer: Frank Kehren, https://www.flickr.com/photos/fkehren/8481894011 CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) The share of income brackets used for different Census and ACS forms varied over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above. Historical data for Napa County is unavailable due to an insufficient sample size for renters in a number of years, making it impossible to calculate affordability for all households. All ACS data is for a single year, rather than a rolling average. Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations.