This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. Table Name Indicator Name Universe Timeframe Source Race Notes Source URL

homeownership_pct % Homeownership Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

renters_pct % Renters Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

mean_home_value Mean Home Value Households 2021 Public Use Microdata Sample (PUMS) All races are Non-Hispanic LA County eGIS-Demography

accepted_mortgage_pct Accepted Mortgate Rate Mortgage Applications 2021 Home Mortgage Disclosure Act HMDA categories - https://files.consumerfinance.gov/f/documents/cfpb_reportable-hmda-data_regulatory-and-reporting-overview-reference-chart-2019.pdf https://ffiec.cfpb.gov/data-browser/data/2021

rent_burden_pct Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

rent_burden_severe_pct Severely Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

eviction_per_100_hh Eviction Rate Renter Households 2014-2017 The Eviction Lab at Princeton University

https://data-downloads.evictionlab.org/#data-for-analysis/

homeless_count Homeless Count Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_homeless_pct % Homeless Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_county_pct % County Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

unable_pay_mortgage_rent% Delayed or Were Unable to Pay Mortgage or Rent in the past 2 Years Households 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

homeless_ever% Who Reported Ever Being Homeless or Not Having Their Own Place to Live or Sleep in the past Five Years Adults 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

D.C.'s median rent for a one bedroom apartment stands at $2,495, significantly higher than the national median rent of approximately $1,567. Click on different U.S. cities to see the median rent for a one bedroom apartment2.The map on the left side shows the percentage of people by census tract that are considered "cost burdened" by housing costs, by paying 30% or more of their household income on rent and utilities3. The map on the right side shows the median household income by census tract4. You can click on the "list" icon in the lower left corner to see the map legend, and meanings of map symbology. Areas that are cost burdened are often areas with the lowest median household incomes. There are also areas in wards where median incomes are high, but the cost of living is also high, leading to a greater cost burden.

This map shows housing costs as a percentage of household income. Severe housing cost burden is described as when over 50% of income in a household is spent on housing costs. For renters it is over 50% of household income going towards gross rent (contract rent plus tenant-paid utilities). Miami, Florida accounts for the having the highest population of renters with severe housing burden costs.The map's topic is shown by tract and county centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. Current Vintage: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Los Angeles Index of Displacement PressureThe Los Angeles Index of Displacement Pressure combines measures that past research efforts and our own original research have shown correlate with future change and displacement pressure. Created in 2015/2016, the index primarily uses data from 2012-2015.These seven measures are applied at the Census Tract level for tracts where >=40% of households earn less than the City's median income. The measures are grouped into two classes: change factors and displacement pressure factors.Change factor measures are those that suggest future revitalization is likely due to investment, projected housing price gains, and proximity to recently changed areas. On the other hand, displacement pressure factors capture areas with a high concentration of existing residents who may have difficulty absorbing massive rent increases that often accompany revitalization. The Los Angeles Index of Displacement Pressure captures the intersection between these two classes.Change Measures Transportation InvestmentMeasure 1: Distance to current rail stations (within a 1/2 mile radius. Tracts beyond 1/2 mile receive no score for this measure). Source: LA MetroMeasure 2: Distance to rail stations under construction/recently opened in 2016 (within a 1/2 mile radius. Tracts beyond 1/2 mile receive no score for this measure)Source: LA Metro Proximity to Rapidly Changing NeighborhoodsMeasure 3: Distance to the closest "top tier" changing neighborhood, as defined by the Los Angeles Index of Neighborhood Change (within a 1 mile radius. Tracts beyond 1 mile receive no score for this measure)Source: The Los Angeles Index of Neighborhood Change Housing MarketMeasure 4: Change in housing price projections from 2015 to 2020 Source: ESRI Community Analyst Displacement Pressure FactorsMeasure 5: Percent of households that rentSource: American Community Survey, Five-Year Estimate, 2014Measure 6: Percent of households that are extremely rent burdened (pay >=50% of household income on rent)Source: American Community Survey, Five-Year Estimate, 2014Measure 7: The number of affordable properties and housing units that are due to expire by 2023.Source: The Los Angeles Housing Element, 2012Date updated: April 7, 2018Refresh rate: Never - Historical data

Residential conduct, satisfaction with residential area and housing desires of residents of a newly established settlement in Bochum.

Topics: 1. Survey part: time of moving and first impression of the new city settlement; today´s judgement on the new municipal area; difficulties adjusting; judgement on the building surface and paint colors; neighborhood contacts; judgement on group facilities in the building; judgement on building management; judgement on child-orientation of the settlement as well as of the residence; detailed judgement on the plan of the residence as well as of all rooms; judgement on the basic equipment of the residence and attitude to greater amenities; judgement on the floor and temperature of the residence; perceived noise pollution from co-residents or stairway and elevator; comparison of current residence with earlier residence; amount of rent and subjectively perceived rent burden; recept of rent aid; time expended and means of transport used for the way to work; judgement on the location of Hustadt relative to the university and the center of Bochum; frequency of trips to the center of town and visits to cultural facilities or events; shopping habits; judgement on the distance of the buildings from each other and the ´openness´ of the residence to view from outside; general judgement on the external appearance of the building; satisfaction with life in Hustadt; inclination to move.

1. Observation part: The interviewer conducted an intensive observation of the residence in part during the interview but primarily not until after the end of the actual interview. Recorded were: type, style and extent of room furniture; number of books and pictures in the living room; books in other rooms; detailed recording of all pieces of furniture in every room of the residence.

Demography: age; family composition; number of children; age of children; age and number of siblings; household income; household size; characteristics of spouse; self-assessment of social class.

Jewelry Rental Market Outlook

The global jewelry rental market size was valued at approximately USD 1.2 billion in 2023, and it is projected to reach around USD 3.5 billion by 2032, growing at a robust compound annual growth rate (CAGR) of 12.5% during the forecast period. This rapid growth can be attributed to several factors, including increasing consumer preference for affordable luxury, the influence of social media and fashion trends, and the rising awareness and acceptance of the sharing economy.

One of the key growth drivers of the jewelry rental market is the changing consumer behavior towards purchasing luxury goods. Millennials and Gen Z consumers, who make up a significant portion of the market, are increasingly valuing experiences over ownership. They prefer to rent luxury items, including jewelry, for special occasions rather than investing in expensive pieces they may not frequently use. This shift in consumer mindset is supported by the economic advantages of renting, allowing consumers to access a variety of high-end jewelry without the financial burden of ownership.

The influence of social media and fashion trends is another significant growth factor. Platforms like Instagram and Pinterest have made it possible for individuals to showcase their style and fashion choices to a global audience. This has increased the demand for unique and trendy jewelry pieces that can complement different outfits for various occasions. Jewelry rental services provide an affordable way for consumers to keep up with these ever-changing trends without the need for a permanent commitment to specific pieces.

Additionally, the rising awareness and acceptance of the sharing economy are driving the growth of the jewelry rental market. The sharing economy promotes the idea of utilizing resources more efficiently by sharing goods and services among a community. This concept has gained traction in various industries, including transportation, hospitality, and now, luxury goods. Consumers are becoming more comfortable with the idea of renting high-value items, contributing to the expansion of the jewelry rental market.

The concept of Wedding Dress Rental is gaining popularity alongside jewelry rental, as more individuals seek cost-effective and sustainable options for special occasions. Just like jewelry, wedding dresses are often worn once, making rental a practical choice for brides looking to save money and reduce waste. This trend is particularly appealing to environmentally conscious consumers who appreciate the benefits of the sharing economy. Wedding dress rental services offer a wide range of styles and designs, allowing brides to choose their dream dress without the financial burden of ownership. The flexibility of rental options also means that brides can select dresses that suit their personal style and the theme of their wedding, ensuring a memorable and unique experience.

From a regional perspective, North America and Europe are currently the largest markets for jewelry rental services. The presence of a large number of high-net-worth individuals, coupled with a strong fashion culture and the widespread acceptance of the sharing economy, has driven the demand in these regions. Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by increasing disposable incomes, growing urbanization, and a burgeoning middle class that is more open to rental services.

Product Type Analysis

Necklaces constitute one of the most significant segments of the jewelry rental market. These items are particularly popular for formal events, weddings, and special occasions where a statement piece can complement the attire. The high cost of owning luxury necklaces makes them ideal for rental services. Companies offering these services provide a wide range of options, from traditional designs to modern, trendy pieces, catering to diverse consumer preferences. Moreover, the easy return and exchange policies make it convenient for consumers to try different styles without a long-term commitment.

Earrings are another highly sought-after segment in the jewelry rental market. The popularity of earrings can be attributed to their versatility and appeal across various age groups and occasions. From simple studs to elaborate chandelier earrings, rental services offer a plethora of choices to match different outfits and events. The rising trend of mix-and-match fashion has further boosted the dema

This data set represents American Community Survey (ACS) 2014-2018 tract information related to Equity Priority Communities (EPCs) for Plan Bay Area 2050+.The Plan Bay Area 2050+ Equity Priority Communities incorporate EPCs identified with 2014-2018 ACS data, as well as EPCs identified with 2018-2022 ACS data into a single consolidated map of Plan Bay Area 2050+ Equity Priority Communities.This data set was developed using American Community Survey 2014-2018 data for eight variables considered.This data set represents all tracts within the San Francisco Bay Region, and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities tract-level variables for exploratory purposes. Equity Priority Communities are defined by MTC Resolution No. 4217-Equity Framework for Plan Bay Area 2040.As part of the development of the [DRAFT] Equity Priority Communities - Plan Bay Area 2050+ features, the source Census tracts had portions that overlapped either the Pacific Ocean or San Francisco Bay removed. The result is this feature set has fewer Census tracts than the unclipped tract source data.Plan Bay Area 2050+ Equity Priority Communities (tract geography) are based on eight ACS 2014-2018 (ACS 2018) tract-level variables:People of Color (70% threshold)Low-Income (less than 200% of Federal poverty level, 28% threshold)Level of English Proficiency (12% threshold)Seniors 75 Years and Over (8% threshold)Zero-Vehicle Households (15% threshold)Single-Parent Households (18% threshold)People with a Disability (12% threshold)Rent-Burdened Households (14% threshold)If a tract exceeds both threshold values for Low-Income and People of Color shares OR exceeds the threshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

Agricultural Equipment Rental Market Outlook

The global agricultural equipment rental market size was valued at approximately USD 48.4 billion in 2023 and is projected to reach USD 82.1 billion by 2032, growing at a CAGR of 6.1% during the forecast period. The increasing demand for cost-effective farming solutions and the growing emphasis on mechanization in agriculture are significant factors driving market growth. Additionally, the need to optimize operational efficiencies and reduce the financial burden of equipment ownership is further propelling the market.

One of the primary growth factors for the agricultural equipment rental market is the high cost associated with purchasing agricultural machinery. For many small and medium-sized farmers, the capital investment required to buy new equipment is prohibitive. By opting for rental services, these farmers can access advanced machinery without committing to large expenditures. This model is proving particularly beneficial in developing regions where financial constraints are more pronounced. Moreover, technological advancements in machinery, such as GPS-enabled tractors and automated harvesters, are becoming accessible to a broader range of farmers through rental arrangements.

Additionally, the flexibility offered by rental services is a significant growth driver. Farmers can rent equipment for specific periods based on seasonal agricultural needs, which allows them to manage expenses more effectively. This flexibility is particularly advantageous for farmers engaged in multiple cropping systems or those dealing with varying types of produce. Seasonal demand for different types of equipment, such as planting equipment during sowing periods and harvesters during the harvest season, can be met effectively through rental services, thus optimizing the use of resources and improving overall productivity.

Environmental sustainability is another key factor contributing to the growth of the agricultural equipment rental market. Renting equipment rather than purchasing reduces the overall number of machinery produced and utilized, which, in turn, lowers the environmental impact associated with manufacturing and disposal. With increasing awareness about sustainable farming practices and governmental regulations pushing for environmentally friendly solutions, the trend of renting over owning is gaining traction. This shift not only helps in reducing carbon footprints but also supports the adoption of the latest, more efficient technologies without the burden of ownership.

From a regional perspective, the market shows significant potential across various geographies. North America and Europe, with their advanced agricultural sectors, are early adopters of rental services. However, rapid growth is observed in the Asia Pacific region due to the increasing mechanization of agriculture and the rising number of small and medium-sized farms that benefit from cost-effective rental solutions. Latin America and the Middle East & Africa are also expected to witness substantial growth due to similar trends in agricultural modernization and economic constraints among farmers.

Cultivator Rentals have emerged as a popular choice among farmers looking to enhance soil preparation without the heavy investment of purchasing new equipment. Cultivators are essential for breaking up soil, preparing seedbeds, and controlling weeds, making them a critical component in efficient farming practices. By opting for rentals, farmers can access the latest models with advanced features that improve soil aeration and nutrient mixing, leading to better crop yields. This approach not only reduces the financial burden on farmers but also allows them to adapt to changing agricultural practices and technologies. The flexibility of renting cultivators means that farmers can choose equipment that best suits their specific soil and crop requirements, ensuring optimal performance and productivity. As the demand for sustainable and efficient farming solutions grows, cultivator rentals offer a practical and cost-effective alternative for farmers worldwide.

Equipment Type Analysis

The agricultural equipment rental market is segmented by equipment type, including tractors, harvesters, haying equipment, planting equipment, irrigation equipment, and others. Tractors are among the most commonly rented agricultural machinery due to their versatile applications in various farming activities. They are pivotal in plowing, tilling, planting, and transporti

SVI MetadataWhen considering natural hazards, vulnerability generally refers to susceptibility or potential for experiencing the harmful impacts of a hazard event. The foundation of vulnerability analysis, a hazards assessment, generally focuses on a community’s exposure to hazard agents such as floods, surge, wave action, or winds.Social vulnerability (SV) is defined as ‘‘the characteristics of a person or group in terms of their capacity to anticipate, cope with, resist and recover from the impacts of a natural hazard.’’ Overall Social Vulnerability is determined by combining childcare, eldercare, transportation, shelter, and civic capacity needs factors. Factor county quintile is determined from statewide percentile score.Field DescriptionsPOPe – estimate of populationHHe – estimate of householdsVEHe - estimate of vehiclesSVI - Composite Social Vulnerability IndexCHILDCARE - Childcare needsELDERCARE - Eldercare needsTRANSNEED - Transportation needsSHELTNEED - Shelter needsCIVICCAP - Civic capacity needsCHILDCARECHILD – Children under 5 years populationSPHWC – Single parent householdsELDERCAREELDERHH – Elder householdsELDERHHPV – Elder households in povertyTRANSNEEDPTD – Public transportation dependent householdsHUNOVEL – Housing units with no vehicleSHELTNEEDVACHU – Vacant housing unitsRENTER – Renter householdsNONWHITE – Nonwhite populationGQ – Population in group quartersYEAR20 – Housing units older than 20 yearsMOBILE - Mobile home housing unitsPOPV - Population in povertyCIVICCAP HUNOTEL – Housing units with no telephoneNOHS – No high school diploma populationUNEMP – Unemployed civilian workforce population 16 years plusSPENW – Speak English poorly or not at all populationOTHER FACTORS (Not included in SVI calculation)RENTBURDN – Rent burdened households (more than 30% of monthly income spent on rent)NOINTNET – No broadband Internet access householdsMEDHVAL – Median home valueAll factor fields use following naming codes:e - estimate from ACS 5-year averagesi - index calculated as percent of table universez - statewide standard scorep - statewide percentile scoreq - county quintilec – quintile by USDA commerce zoneExample index field names:CHILDe – estimate of CHILD populationCHILDi – index calculated from proportion of CHILD in total populationCHILDz – z-score of CHILDCHILDp – statewide percentile rankCHILDq – county quintileSVI Calculation Steps 1. ACS data downloaded (R script) a. 5-year estimate data for 1st order indices, b. Tables and fields identified and selected by county, tract, and block group, c. Fields for each first order estimate (e)and table universe assembled to CSV file, 2. CSV imported to SQL Server database (SQL query), a. Index (i) calculated as percentage), z-score (z), statewide percentile rank (p) calculated, b. Quintiles (q) calculated by ranking percentiles within county, c. For some years, quintiles (c) by commerce zone (USDA) also calculated, d. 2nd order indices calculated as mean percentile rank and grouped by county quintile, e. 3rd order index calculated as mean of 2nd order indices and grouped by county quintile.

Endoscopy Equipment Rental Market Outlook

The global endoscopy equipment rental market size is projected to grow significantly from USD 1.5 billion in 2023 to USD 3.8 billion by 2032, at a compound annual growth rate (CAGR) of 11.2%. This growth is driven by the increasing demand for minimally invasive surgical procedures and the cost-effectiveness of rental solutions compared to outright purchases.

The increasing prevalence of chronic diseases such as gastrointestinal, urological, and respiratory disorders is a significant growth factor for the endoscopy equipment rental market. These conditions often require frequent diagnostic and therapeutic procedures, which, in turn, boost the demand for endoscopic equipment. As healthcare facilities aim to provide advanced medical care, the need for high-quality, up-to-date endoscopic equipment becomes essential. However, the high cost of purchasing such equipment can be prohibitive for many institutions, particularly smaller clinics and diagnostic centers. Renting provides an economically viable alternative, allowing these facilities to access the latest technology without the burden of a significant upfront investment.

Technological advancements in endoscopic equipment are another crucial driver for the market. Innovations such as high-definition imaging, 3D imaging, and the integration of artificial intelligence (AI) in endoscopy have significantly improved the accuracy and efficacy of diagnostic and therapeutic procedures. However, these advanced systems come at a high cost, making them inaccessible to many healthcare providers if purchased outright. The rental model enables healthcare facilities to stay current with technological advancements without the need for large capital expenditures, thereby ensuring that patients receive the best possible care.

Another growth factor is the increasing focus on outpatient care and the rising number of ambulatory surgical centers (ASCs). ASCs offer a cost-effective alternative to hospital-based procedures, leading to their growing popularity. These centers often perform a high volume of endoscopic procedures but may not have the financial resources to invest in expensive equipment. Renting endoscopic equipment allows ASCs to operate efficiently while maintaining high standards of care. Moreover, the flexibility of rental agreements enables these centers to upgrade their equipment as needed, ensuring that they can continue to provide state-of-the-art medical care.

ERCP Equipment, or Endoscopic Retrograde Cholangiopancreatography equipment, plays a pivotal role in the diagnosis and treatment of conditions related to the biliary or pancreatic ductal systems. As a specialized form of endoscopy, ERCP combines endoscopic and fluoroscopic techniques to address issues such as gallstones, tumors, or strictures in the bile or pancreatic ducts. The complexity and precision required for ERCP procedures necessitate the use of advanced equipment, which can be a significant financial burden for healthcare facilities. By opting for rental solutions, hospitals and clinics can access cutting-edge ERCP equipment without the substantial capital investment, thus enabling them to provide comprehensive care for patients with pancreaticobiliary disorders.

Regionally, North America is expected to dominate the endoscopy equipment rental market over the forecast period, owing to well-established healthcare infrastructure and high adoption rates of advanced medical technologies. However, significant growth is also anticipated in the Asia Pacific region due to increasing healthcare investments, growing awareness about minimally invasive procedures, and the rising prevalence of chronic diseases. Europe is another key region, with countries like Germany, France, and the UK showing substantial growth potential due to their robust healthcare systems and increasing focus on cost-effective medical solutions.

Product Type Analysis

The endoscopy equipment rental market is segmented by product type into flexible endoscopes, rigid endoscopes, capsule endoscopes, robot-assisted endoscopes, and others. Flexible endoscopes are among the most commonly rented equipment due to their versatility and wide range of applications, including gastrointestinal, urological, and respiratory endoscopy. These devices offer high maneuverability and can navigate complex anatomical structures, making them invaluable for both diagnostic and therapeutic procedures.

Rigid endoscopes