HUD's Real Estate Assessment Center conducts physical property inspections of properties that are owned, insured or subsidized by HUD, including public housing and multifamily assisted housing. About 20,000 such inspections are conducted each year to ensure that assisted families have housing that is decent, safe, sanitary and in good repair. This page provides a full historical view of the results of those inspections, providing point-in-time property scores. Results are available for download as a comma-delimited dataset. Separate datasets are available for public housing and for multifamily assisted properties. The results represent the inspections conducted from 2001 through January 2015. The dataset includes property identifiers and location information.

Detailed descriptions of the inspection processes can be found in Federal Register notices 66 FR 59084 for public housing and 65 FR77230 for Office of Housing programs.

Making these inspection details available will enable researchers, advocacy groups and the general public to 1) better understand the physical condition of the HUD-assisted housing stock, as well as changes in the stock over time; 2) hold providers accountable for housing quality; and 3) plan for future affordable housing needs.

The number of homes that being rated with the Home Energy Rating System (HERS) score in the United States has been increasing between 2019 and 2023. That last year, there were roughly ******* homes with a new HERS rating. The average score of those homes has been decreasing slowly, with lower scores indicating that those homes are more efficient. Texas had the highest number of homes rated by the HERS index.

This dataset includes information regarding HUD's (Housing & Urban Development) Real Estate Assessment Center conducts physical property inspections of properties that are owned, insured or subsidized by HUD, including public housing and multifamily assisted housing.

Minnesota and Massachusetts were among the U.S. states with the lowest average Home Energy Rating System (HERS) index scores of the certifications issued in 2024, which indicates a higher level of energy efficiency. Meanwhile, Indiana and North Carolina had less energy efficient homes, given that their HERS-certified homes had, on average, higher scores. With an average score of **, Texas had the highest number of homes rated by the HERS index.

Taiwan Housing Future Price Confidence Score data was reported at 89.900 Point in Dec 2014. This records a decrease from the previous number of 98.900 Point for Sep 2014. Taiwan Housing Future Price Confidence Score data is updated quarterly, averaging 106.350 Point from Mar 2003 (Median) to Dec 2014, with 45 observations. The data reached an all-time high of 138.300 Point in Mar 2013 and a record low of 52.740 Point in Dec 2008. Taiwan Housing Future Price Confidence Score data remains active status in CEIC and is reported by Construction and Planning Agency, Ministry of the Interior. The data is categorized under Global Database’s Taiwan – Table TW.EB016: Real Estate Confidence Score.

HUD's Real Estate Assessment Center conducts physical property inspections of properties that are owned, insured or subsidized by HUD, including public housing and multifamily assisted housing. About 20,000 such inspections are conducted each year to ensure that assisted families have housing that is decent, safe, sanitary and in good repair. This page provides a full historical view of the results of those inspections, providing point-in-time property scores. Results are available for download as a comma-delimited dataset. Separate datasets are available for public housing and for multifamily assisted properties. The results represent the inspections conducted from 2001 through January 2015. The dataset includes property identifiers and location information.

Detailed descriptions of the inspection processes can be found in Federal Register notices 66 FR 59084 for public housing and 65 FR77230 for Office of Housing programs.

Making these inspection details available will enable researchers, advocacy groups and the general public to 1) better understand the physical condition of the HUD-assisted housing stock, as well as changes in the stock over time; 2) hold providers accountable for housing quality; and 3) plan for future affordable housing needs.

Score for each LSOA in the Barriers to Housing and Services domain.

Score for each LSOA in the Barriers to Housing and Services domain.

The English Indices of Deprivation provide a relative measure of deprivation at small area level across England. Areas are ranked from least deprived to most deprived on seven different dimensions of deprivation and an overall composite measure of multiple deprivation. Most of the data underlying the 2010 indices are for the year 2008.

The indices have been constructed by the Social Disadvantage Research Centre at the University of Oxford for the Department for Communities and Local Government. All figures can only be reproduced if the source (Department for Communities and Local Government, Indices of Deprivation 2010) is fully acknowledged.

The domains used in the Indices of Deprivation 2010 are: income deprivation; employment deprivation; health deprivation and disability; education deprivation; crime deprivation; barriers to housing and services deprivation; and living environment deprivation. Each of these domains has its own scores and ranks, allowing users to focus on specific aspects of deprivation.

Because the indices give a relative measure, they can tell you if one area is more deprived than another but not by how much. For example, if an area has a rank of 40 it is not half as deprived as a place with a rank of 20.

The Index of Multiple Deprivation was constructed by combining scores from the seven domains. When comparing areas, a higher deprivation score indicates a higher proportion of people living there who are classed as deprived. But as for ranks, deprivation scores can only tell you if one area is more deprived than another, but not by how much.

This dataset was created from a spreadsheet provided by the Department of Communities and Local Government, which can be downloaded here.

The method for calculating the IMD score and underlying indicators is detailed in the report 'The English Indices of Deprivation 2010: Technical Report'.

The data is represented here as Linked Data, using the Data Cube ontology.

Housing Results - 2021 Census

In 2022, the average credit score of Gen Z mortgage applicants in the top 10 metros for Gen Z home buyers in the United States was between *** and ***. Salt Lake City, UT, which was the hottest market for Generation Z home buyers, had an average credit score of ***. Out of the ** most popular metros, Cincinnati, OH, was the most affordable.

Taiwan Housing Current Price Confidence Score: Home Seeker (HS) data was reported at 78.100 Point in Jun 2017. This records an increase from the previous number of 67.800 Point for Dec 2016. Taiwan Housing Current Price Confidence Score: Home Seeker (HS) data is updated quarterly, averaging 105.540 Point from Jun 2002 (Median) to Jun 2017, with 53 observations. The data reached an all-time high of 159.960 Point in Dec 2009 and a record low of 43.390 Point in Dec 2008. Taiwan Housing Current Price Confidence Score: Home Seeker (HS) data remains active status in CEIC and is reported by Construction and Planning Agency, Ministry of the Interior. The data is categorized under Global Database’s Taiwan – Table TW.EB016: Real Estate Confidence Score.

HUD's Real Estate Assessment Center conducts physical inspections of properties that are owned, insured or subsidized by HUD, including public housing and multifamily assisted housing. About 20,000 such inspections are conducted each year to ensure that assisted families have housing that is decent, safe, sanitary and in good repair. The Physical Inspection Scores datasets provide a full historical view of the results of those inspections, providing point-in-time property scores. Results are available for download as a comma-delimited dataset. Separate datasets are available for public housing and for multifamily assisted properties. The results represent the inspections conducted from 2001 through September 2009. The datasets include property identifiers and location information.

Taiwan Housing Future Price Confidence Score: HS: Tainan City data was reported at 79.300 Point in Jun 2017. This records an increase from the previous number of 64.200 Point for Dec 2016. Taiwan Housing Future Price Confidence Score: HS: Tainan City data is updated quarterly, averaging 115.555 Point from Jun 2010 (Median) to Jun 2017, with 24 observations. The data reached an all-time high of 143.600 Point in Dec 2010 and a record low of 64.200 Point in Dec 2016. Taiwan Housing Future Price Confidence Score: HS: Tainan City data remains active status in CEIC and is reported by Construction and Planning Agency, Ministry of the Interior. The data is categorized under Global Database’s Taiwan – Table TW.EB016: Real Estate Confidence Score.

The average credit score of apartment renters in the United States varies depending on the type of building and the age of the renter. In 2020, renters of mid-level apartments in the United States had an average credit score of 626 but for Baby Boomer renters the score was 675, and for Gen Z renters it was 583.

In March 2020, new apartments in Colombia had a housing price index score of 142.34. Meanwhile, new houses reported an index score of 142.22. In 2019, the number of financed residential units in Colombia added up to 125,973 units.

In 2024, Miami was the housing market most at risk, with a real estate bubble index score of ****. Tokyo and Zurich followed close behind with **** and ****, respectively. Any market with an index score of *** or higher was deemed to be a bubble risk zone.

Housing survey results for Georgetown, TX conducted by FlashVote

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a webmap of a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup. The map is visualized based on market typology score with strongest market in pink, and weakest market in dark blue.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to help inform development of the City's new Comprehensive Plan, Rochester 2034 , and retained czb, LLC - a firm with national expertise based in Alexandria, VA - to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. And, importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long appreciated that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment, and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/

In June of 2023, the new housing price index score in Colombia was *** percent higher than in June of the previous year. The index decreased to ****** in 2021, which represents the lowest value of the index since 2015. Nevertheless, the price of new housing increased a lot in 2022 and 2023.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.