Multifamily Housing receiving insured financing through the Federal Housing Administration (FHA). The FHA insured Multifamily Housing portfolio consists primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also be nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. Please note that this dataset overlaps the Multifamily Properties Assisted layer. The Multifamily property locations represent the approximate location of the property. The FHA insured Multifamily Housing portfolio consists primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also be nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. Please note that this dataset overlaps the Multifamily Properties Assisted layer. The Multifamily property locations represent the approximate location of the property. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about HUD Insured Multifamily Properties visit: https://www.hud.gov/program_offices/housing/mfh. Data Dictionary: DD_HUD Insured Multifamilly Properties Date of Coverage: 06/2026 Last Updated: 07/2026

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.

Techsalerator’s MLS Real Estate Data for United States of America

Techsalerator’s MLS Real Estate Data for United States of America aggregates comprehensive insights from Multiple Listing Service (MLS) systems and related real estate sources to provide a detailed view of the country’s residential and commercial property market. This dataset captures property listings, pricing trends, transaction activity, housing inventory, and market dynamics across the United States, helping organizations analyze real estate performance at scale.

This dataset is designed to support real estate brokerages, proptech companies, investors, financial institutions, researchers, and government agencies seeking insights into property markets, valuation trends, and housing supply-demand behavior across the United States.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Top 5 Key Data Fields

1. Property Listings (Type, Status, Location, Listing Date)
   Captures structured MLS listings across residential, commercial, and rental properties, including active, pending, and sold statuses.

2. Pricing & Valuation Data (Listing Price, Sold Price, Price per Sq Ft)
   Provides detailed pricing information and valuation metrics for comparative market analysis and investment decisions.

3. Property Characteristics (Beds, Baths, Size, Lot Area, Year Built)
   Includes standardized property attributes for accurate filtering, segmentation, and modeling.

4. Transaction History & Market Activity
   Tracks listing changes, price reductions, time on market, and historical sales performance.

5. Geospatial & Neighborhood Indicators
   Includes location-level data such as ZIP code, school district, walkability, and regional market classification.

Top 5 MLS Real Estate Data Trends in United States of America

1. High Regional Variation in Housing Prices and Demand
   Real estate markets vary significantly between urban, suburban, and rural regions.

2. Increased Demand for Suburban and Secondary Markets
   Migration trends continue to drive growth outside major metropolitan centers.

3. Rising Importance of Time-on-Market Metrics
   Days on market is increasingly used to evaluate liquidity and pricing competitiveness.

4. Growth of Institutional Real Estate Investment Activity
   Large-scale investors are expanding participation in residential property markets.

5. Digitization of Real Estate Transactions and Listings
   MLS systems are increasingly integrated with digital platforms and AI-driven valuation tools.

Top 5 Applications of MLS Real Estate Data in United States of America

1. Real Estate Investment Analysis
   Supports property valuation, portfolio optimization, and market timing decisions.

2. Proptech Platform Development
   Powers listing platforms, recommendation engines, and automated valuation models (AVMs).

3. Mortgage & Lending Risk Assessment
   Helps financial institutions evaluate collateral value and lending risk.

4. Market Research & Housing Economics
   Enables analysis of affordability, supply-demand dynamics, and regional trends.

5. Urban Planning & Policy Development
   Supports government and institutions in housing policy and infrastructure planning.

Accessing Techsalerator’s MLS Real Estate Data

To obtain Techsalerator’s MLS Real Estate Data for the United States of America, contact us at info@techsalerator.com with your specific data requirements. Custom datasets, historical records, and real-time feeds are available, with delivery within 24 hours and flexible access agreements upon request.

Included Data Fields

• Property Listing ID
• Listing Status (Active, Pending, Sold, Off-Market)
• Property Type (Residential, Commercial, Rental)
• Listing & Sale Price
• Price per Square Foot
• Property Size (Sq Ft / Sq M)
• Bedrooms & Bathrooms
• Lot Size
• Year Built
• Listing Date & Sold Date
• Days on Market
• Geographic Location (Address, ZIP, City, State)
• School District Information
• Neighborhood & Market Indicators
• Historical Price Changes
• Agent & Brokerage Information

Contact Information

For actionable insights into MLS real estate data, housing market trends, and property analytics in the United States of America, Techsalerator’s MLS Real Estate Data empowers investors, enterprises, researchers, and policymakers with reliable, structured, and scalable intelligence.

📩 Email: info@techsalerator.com

The stable and healthy development of the residential market has always been one of the important tasks of the Government, and is of great significance to the maintenance of social stability and the well-being of residents. This paper utilizes the VAR model, the housing filtering model and the four-quadrant theoretical model to explore the comprehensive impact and mechanism of the land supply system on China’s housing market from the logical framework and institutional environment of land and housing. The results of the study show that the price of residential land supply has a positive effect on house prices, while the quantity of residential land supply has a negative effect on house prices; The land transfer patterns of “Restricted Land Price, on-site Lottery” and “Restricted Land Price, Restricted Selling Price and Compete for Quality” are conducive to the healthy development of the real estate market, but an excessive supply of land for leasing may push up the prices of commercial properties. Finally, in combination with China’s policy objective of “stabilizing land prices, housing prices and expectations”, it puts forward a number of policy recommendations to rationally control and guide the healthy development of real estate.

The statistic shows the support for potential housing policies among adults in the United Kingdom (UK) as of March 2015. The most popular policy was a requirement for developers to market homes within the UK, before marketing them overseas, with 80 percent of respondents supporting it. The second most popular housing policy (70 percent support) was charging of the higher council tax rates on homes not occupied all year round.

This dataset tracks nominal residential property price movements across 48 countries and regions, providing a standardized measure of how house values have shifted over time relative to a 2015 baseline. Compiled by the OECD from its Analytical House Price Indicators program, it serves economists, policy analysts, and real estate researchers who need to compare housing affordability and investment trends across borders and decades. The dataset spans 65 years of annual observations, from 1960 through 2025, capturing 1766 individual data points. Most countries have data current through 2025, with a few exceptions like Saudi Arabia (2023) and India, South Korea, Luxembourg, and Chile (2024). The long historical range allows analysts to examine both recent price movements and multigenerational housing market cycles. House prices have grown remarkably consistent across major economies, with the top performers like Norway, Austria, and Belgium each posting compound annual growth rates of 0.05 percent from their respective starting points through 2025. This uniformity masks important regional variation: some markets recovered faster after the 2008 financial crisis, while others faced different pressures from immigration, construction policy, and interest rates. By indexing all countries to the same 2015 baseline, the dataset makes it easy to spot which nations experienced explosive growth, stagnation, or decline in housing valuations. Researchers and journalists use this data to contextualize housing affordability debates, trace the impact of monetary policy on asset prices, and benchmark one country's market against global peers. Property investors rely on it to identify long-term trends and validate timing assumptions for market entry or exit decisions across different geographies.

Maps and data from our low-use properties dataset, includes maps at national and small area levels within all returning (205) local authorities across the UK. Data for Scotland is complete, no data available for Northern Ireland.This repository contains the digital outputs of the Project 'Low-use homes in rural and coastal areas of the UK: Geography impact and responses'.The report and executive summary are available from the White Rose RepositoryContentsThis repository contains the following:- Map folders- Dataset files## Maps- maps_absolute: Maps for each local authority for which the project had data, coloured by the low-use decile of each LSOA of the local authority relative to the entire dataset- maps_relative: Maps for each local authority for which the project had data, coloured by the low-use decile of each LSOA of the local authority relative to the local authority.- maps_country: Maps that the cover each of the countries in Great Britain i.e. England, Scotland, Wales. Their are two sets of maps, maps coloured by decile, and maps coloured by whether the price difference between low-use homes and occupied homes is statistically significant.### Explanation on the difference between the relative and absolute decilesWhen looking at the absolute and relative maps of the local authority of Dorset, it is clear they are very different. In the absolute map Dorset has a lot of D10, whilst in the relative map there is an even spread. This is because deciles always represent 10% of the data, so the relative map has 10% of the Dorset LSOA's in each of the 10 deciles. However, Dorset has a much higher number of low-use properties than the average local authority, so the absolute map has significantly more LSOA in the 10th decile. You can see in the legend of the maps that the decile boundaries are different. The table below compares the national level deciles (absolute) with the deciles for Dorset (relative) which has more than average low-use homes, and the decile boundaries for Colchester, which has less than average low-use homes.Datasets- ghost_enclave_20.csv: List of LSOA with at least 20% low-use properties- ghost_enclave_25.csv List of LSOA with at least 20% low-use properties- highest_30_LUP_LADS.csv: The top 30 local authorities in terms of low-use properties- low_use_price_extremes.csv: Most extreme ratios of house price to low-use for the three Nations- low_use_price_extremes.xlsx: Most extreme ratios of house price to low-use for the three Nations- weighted_price_LAD.csv: The entire dataset aggregated to local authority level

This dataset contains 15,000 records and 42 columns designed for research on real estate market stability, housing price bubbles, and early risk warning analysis. It combines housing market conditions, economic indicators, financial variables, demographic characteristics, urban development factors, and policy-related information. The dataset represents observations collected across different regions and time periods. It provides comprehensive information about housing prices, property sales, housing inventory, mortgage conditions, household debt, economic growth, inflation, employment, financial market conditions, population changes, urbanization, infrastructure development, land prices, and housing policies. The categorical target column, Bubble_Risk_Level, represents the overall level of housing market risk. It contains four categories: Low, Moderate, High, and Critical. The dataset can support research related to housing market monitoring, economic risk assessment, financial stability analysis, regional market comparison, bubble risk identification, and real estate early warning systems.

Dataset Count

Total Records: 15,000

Total Columns: 42

Column Description

Record_ID: Unique identification number assigned to each dataset record.

Region_ID: Unique code representing the geographical region associated with each observation.

Observation_Period: Date representing the monthly observation period of the market record.

Region_Type: Classification of the region as Urban, Suburban, Metropolitan, or Developing.

Housing_Price_Index: Numerical indicator representing changes in residential property price levels.

House_Price_Growth_Rate: Percentage change in residential property prices over time.

Price_to_Income_Ratio: Ratio between average housing prices and household income levels.

Price_to_Rent_Ratio: Ratio comparing residential property prices with rental values.

Residential_Sales_Volume: Total number of residential property transactions recorded in the region.

Housing_Inventory_Level: Number of residential properties available in the housing market.

Inventory_to_Sales_Ratio: Ratio between available housing inventory and residential property sales.

New_Housing_Starts: Number of new residential construction projects initiated during the observation period.

Vacancy_Rate: Percentage of residential properties that remain unoccupied.

Average_Rent_Growth: Percentage change in average residential rental prices.

Mortgage_Interest_Rate: Average interest rate applied to residential mortgage loans.

Mortgage_Credit_Growth: Percentage change in the volume of mortgage lending.

Loan_to_Value_Ratio: Percentage relationship between the mortgage loan amount and property value.

Household_Debt_to_Income: Ratio comparing total household debt with household income.

Mortgage_Delinquency_Rate: Percentage of mortgage loans with delayed or missed payments.

Bank_Real_Estate_Exposure: Percentage of banking-sector financial exposure associated with real estate activities.

GDP_Growth_Rate: Percentage change in regional economic output.

Inflation_Rate: Percentage change in the general level of prices for goods and services.

Unemployment_Rate: Percentage of the labor force that is unemployed.

Disposable_Income_Growth: Percentage change in household disposable income.

Consumer_Confidence_Index: Numerical indicator representing consumer confidence in economic conditions.

Stock_Market_Return: Percentage gain or loss recorded in the financial market.

Market_Volatility_Index: Numerical measure representing the level of uncertainty and fluctuations in financial markets.

Credit_Growth_Rate: Percentage change in overall credit availability within the economy.

Money_Supply_Growth: Percentage change in the total amount of money circulating within the economy.

Population_Growth_Rate: Percentage change in the regional population.

Net_Migration_Rate: Difference between the number of people entering and leaving a region.

Urbanization_Rate: Percentage of the population residing in urban areas.

Population_Density: Number of people residing within a specific geographical area.

Infrastructure_Investment_Index: Numerical indicator representing the level of investment in regional infrastructure development.

Land_Price_Growth_Rate: Percentage change in residential and commercial land prices.

Construction_Cost_Index: Numerical indicator representing changes in construction material and development costs.

Foreign_Investment_Growth: Percentage change in foreign investment activities.

Housing_Policy_Index: Numerical indicator representing the intensity of housing-related policy measures.

Interest_Rate_Policy_Change: Categorical indicator representing Tightening, Neutral, or Easing interest rate policy conditions.

Market_Stress_Score: Numerical score representing the overall level of instability and pressure within the housing an...

This dataset tracks the All-Transactions House Price Index for Nebraska on a quarterly basis, providing a comprehensive measure of residential property value appreciation across the state since 1975. The index is maintained by the US Federal Housing Finance Agency and distributed through FRED, making it a reliable resource for understanding long-term housing market trends in the Great Plains. Over the 50-year period from 1975 to 2025, Nebraska's house price index has risen dramatically from an initial value of 63.71 to 541.9 as of the third quarter of 2025. This represents a compound annual growth rate of 4.37 percent, demonstrating steady and consistent appreciation in the state's residential real estate market. The dataset contains 204 quarterly observations, with an average index value of 215.27 across the entire measurement period. These figures reveal important insights about housing affordability and wealth accumulation in Nebraska. The sustained growth trajectory shows that homeownership has become increasingly valuable as an investment over the decades, though the pace of appreciation has remained moderate compared to some coastal markets. Analysts can use this quarterly data to identify economic turning points, compare Nebraska's performance to national trends, and understand how local housing markets respond to broader economic cycles. Researchers, real estate professionals, and policymakers rely on this index to inform decisions about development, lending, and housing policy. The granular quarterly frequency allows for detection of seasonal patterns and short-term market shifts while the 50-year depth supports rigorous historical analysis and forecasting.

Over the five years through 2026, revenue for the Direct Real Estate Activities industry is set to inch down at a compound annual rate of 0.2% to €655.3 billion. As the European Central Bank (ECB) has attempted to curtail inflation, its key interest rate - the deposit facility rate - has fluctuated severely, jumping from -0.5% in July 2022 to 4% in September 2023. Surging borrowing costs dissuaded households from buying new homes, leaving many to stay within the rental market. This damaged revenue pipelines and hit profitability in 2023, as high-value residential property purchases slumped. Simultaneously, commercial clients were reluctant to commit to property investments amid high borrowing costs, exacerbating barriers to agencies' income in 2023. In 2026, revenue is slated to sink by 1.6%. After a period of easing monetary policy in 2024 and 2025, the ECB elevated its deposit facility rate from 2% to 2.25% in June 2026, amid fears of spiralling inflation related to the US conflict with Iran, ramping up borrowing costs and adding to investor and buyer caution. Tighter sustainability standards, spearheaded by the EU Energy Performance of Buildings Directive, have pushed property owners to invest in retrofits targeting energy efficiency to retain portfolio quality and align with regulation. The costs tied to these improvements are set to weigh on short-term profitability, but could solidify long-term revenue streams by preserving asset values. New private rental regulation in the UK (the Renters' Rights Act) and Ireland (The Residential Tenancies (Miscellaneous Provisions) Act 2026) is set to shake up the lettings market, with bans on "no-fault evictions" and tighter caps on rent price hikes prompting landlords to sell up or boost rents prior to enforcement. Compliance-related costs are set to amplify profit struggles in 2026, but long-term rental market stability through improved renters' rights could secure revenue streams. Over the five years through 2031, revenue is expected to swell at a compound annual rate of 3% to €761.2 billion. Geopolitical instability could disrupt real estate activities, as uncertainty surrounding monetary policy decisions weighs on property purchases and investment. Nonetheless, Europe-wide efforts to bolster housing supply could spur transactions and leasing for residential property owners, despite potential shifts in rental and house price dynamics as supply chases demand. The industrial and logistics sector offers respite from revenue volatility, with strong manufacturing and retail activities pushing third-party logistics companies to expand their networks.

The stable and healthy development of the residential market has always been one of the important tasks of the Government, and is of great significance to the maintenance of social stability and the well-being of residents. This paper utilizes the VAR model, the housing filtering model and the four-quadrant theoretical model to explore the comprehensive impact and mechanism of the land supply system on China’s housing market from the logical framework and institutional environment of land and housing. The results of the study show that the price of residential land supply has a positive effect on house prices, while the quantity of residential land supply has a negative effect on house prices; The land transfer patterns of “Restricted Land Price, on-site Lottery” and “Restricted Land Price, Restricted Selling Price and Compete for Quality” are conducive to the healthy development of the real estate market, but an excessive supply of land for leasing may push up the prices of commercial properties. Finally, in combination with China’s policy objective of “stabilizing land prices, housing prices and expectations”, it puts forward a number of policy recommendations to rationally control and guide the healthy development of real estate.

This dataset contains structured information about affordable and subsidized housing developments across Indonesia. It includes detailed attributes such as property characteristics, construction materials, pricing, developer information, and geographic coordinates.

The dataset is designed to support research and analysis in real estate economics, urban planning, housing affordability, and machine learning applications such as price prediction and spatial analysis.

Context Affordable housing remains a critical policy and socioeconomic topic in Indonesia. Government-supported subsidized housing programs aim to improve home ownership accessibility for low- and middle-income households. However, publicly accessible structured datasets are limited.

This dataset aggregates housing project information into a standardized tabular format to facilitate data-driven research and modeling.

Content Each row represents a housing project or development entry with the following categories: - Administrative location (province, city/regency, district, village) - Geographic coordinates (latitude, longitude) - Pricing information (total price, price per square meter) - Property specifications (land area, building area, bedrooms, bathrooms, floors) - Construction materials (roof, wall, floor, foundation) - Developer information and associations - Total unit counts

Potential Use Cases - Housing price prediction models - Geospatial affordability analysis - Urban development research - Socioeconomic studies - Construction material trend analysis - Investment and market insights - Machine learning benchmarking datasets

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/

This map shows households that spend at least 30 percent of their income on housing, a threshold widely used by many affordable housing advocates and official government sources including Housing and Urban Development and Census. Census asks about income and housing costs to understand whether housing is affordable in local communities. When housing is not sufficient or not affordable, income data helps communities: Enroll eligible households in programs designed to assist them.Qualify for grants from the Community Development Block Grant (CDBG), HOME Investment Partnership Program, Emergency Solutions Grants (ESG), Housing Opportunities for Persons with AIDS (HOPWA), and other programs.When rental housing is not affordable, the Department of Housing and Urban Development (HUD) uses rent data to determine the amount of tenant subsidies in housing assistance programs.Definitions:Housing costs are defined as burdensome if they exceed 30 percent of monthly income, a widely-used definition by HUD and others in affordable housing discussions. For owners, monthly housing costs include payments for mortgages and all other debts on the property; real estate taxes; fire, hazard, and flood insurance; utilities; fuels; and condominium or mobile home fees.For renters, monthly housing costs include contract rent plus the estimated average monthly cost of utilities (electricity, gas, and water and sewer) and fuels (oil, coal, kerosene, wood, etc.) if these are paid by the renter.Income is defined as the sum of wage/salary income; net self-employment income; interest/dividends/net rental/royalty income/income from estates & trusts; Social Security/Railroad Retirement income; Supplemental Security Income (SSI); public assistance/welfare payments; retirement/survivor/disability pensions; & all other income.Related maps available:Relationship-style map of owners and renters with burdensome housing costsPredominance-style map of seniors (age 65 and over) who are owners and renters with burdensome housing costs Color-and-size-style map of renter households that are affected by severely burdensome housing costsColor-and-size-style map of owner households that are affected by severely burdensome housing costsThis map is multi-scale, with data for states, counties, and tracts. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Context

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

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

Content

Variable Availability:

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

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

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

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

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

Housing type definitions:

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

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

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

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

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

Regional Availability:

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

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

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

Additional Data Products

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

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

Acknowledgements

The mission of the Zillow Economic Research Team is to be the most open, authoritative source for timely and accurate housing data and unbiased insight. We aim to empower consumers, industry professionals, policy makers and researchers looking to better understand the housing market.

To see more of our mission in action, we invite you to learn more about us and to check out our collection of research briefs, stories, data tools and past presentations at https://www.zillow.com/research/

Inspiration

Zillow, and the Zillow Economic Research Team, firmly believe that not only do data want to be free, data are going to be free. Instead of simply publishing raw data, we believe in the power of pushing data up the ladder from raw data bits, to actionable information and finally to unique insight. We aim to answer questions of all kinds, even questions our users may not have known they had before coming to us. When done right, we firmly believe this process of turning data into insight can be transformational in people's lives.

Please join us on this journey, and we're excited to see what insights you can discover hidden amongst our data!

Factors Affecting USA National Home Prices:

Overview:

This dataset contains a comprehensive collection of indicators which dictate the housing prices in the United States.

1. US Mortgage Rates:

• The average interest rates on mortgage loans in the United States.
• Used to track the cost of borrowing for housing and its impact on the real estate market.

2. Gross Domestic Product (GDP):

• The total monetary value of all goods and services produced within the United States during a specified period.
• A fundamental measure of economic performance, reflecting the overall economic health and growth trends of the country.

3. Unemployment Rates:

• The percentage of the labor force that is currently unemployed and actively seeking employment.
• A crucial indicator of labor market health and economic stability, influencing government policies and social welfare programs.

4. FED Funds Rate:

• The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, as set by the Federal Reserve.
• This rate is a primary tool for monetary policy, influencing borrowing costs and, subsequently, overall economic activity.

5. Population Growth:

• The annual rate at which the U.S. population is changing, reflecting births, deaths, and migration.
• Offers insights into demographic trends, which have implications for labor force, consumer markets, and social services planning.

6. Consumer Price Index (CPI):

• A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
• A key indicator for assessing inflation or deflation, influencing consumer spending behavior and economic policy decisions.

S&P Case-Shiller Housing Price Index (USA):

• Measures changes in the prices of residential real estate properties over time, offering insight into the health and trends of the housing market in the United States.
• Crucial for assessing the state of the housing market, including property values, trends, and their impact on the broader economy.

The Multifamily Assistance and Section 8 Contracts Database was created to provide HUD partners/clients with a way of measuring the potential impact of expiring project-based subsidy contracts in their communities. PolicyMap linked the HUD Multifamily Assistance Properties to the HUD Multifamily Assistance and Section 8 Contracts to show the details for up to four contracts per property. The most recent four contracts are shown; if a property has more than four contracts, the number of additional older contracts is displayed.

PolicyMap downloaded and geocoded data on HUD’s multifamily sites from three different resources at HUD and, wherever possible, PolicyMap linked the data using the property ID. The three datasets included are the Multifamily Assistance and Section 8 Contracts, A Picture of Subsidized Households and the REAC assessment scores report. PolicyMap was able to locate 92% of multifamily properties on a map. This data is displayed with HUD’s A Picture of Subsidized Households, which shows data about the subsidized households at these properties. Picture data is provided by HUD at the contract level, not property level. For PolicyMap, contracts are aggregated together to create single values for each property. All contracts available in the Picture data are included, regardless of the contracts listed in the Multifamily Assistance database. Only properties listed in the HUD Multifamily database are included.

This dataset tracks the average sales price of houses sold in the United States, compiled by the US Census Bureau and distributed through FRED, the Federal Reserve Economic Data platform. It is a foundational economic indicator that reflects housing market conditions, affordability trends, and broader inflation dynamics across six decades of American real estate history. The data spans from January 1963 to January 2026, covering 254 monthly observations. Over this period, the average home sales price rose from $19,300 to $514,600, representing a compound annual growth rate of 5.35 percent. The dataset shows a low point of $19,200 and a high point of $541,300, with an overall historical average of approximately $193,901. For economists and analysts, these figures reveal long-term patterns in housing affordability and wealth accumulation. The steady upward trajectory underscores how housing has appreciated as an asset class while simultaneously raising questions about homeownership accessibility for new buyers. Researchers can use this series to correlate housing prices with employment, interest rates, household income, and consumer spending behavior. Journalists, real estate professionals, policy researchers, and investment analysts rely on this data to contextualize current market conditions within historical perspective. Whether examining generational housing affordability, regional price divergence, or the relationship between monetary policy and property values, this comprehensive US Census Bureau dataset provides essential benchmarking for informed discussion and analysis.

Over the five years through 2026, revenue for the Direct Real Estate Activities industry is set to inch down at a compound annual rate of 0.2% to €655.3 billion. As the European Central Bank (ECB) has attempted to curtail inflation, its key interest rate - the deposit facility rate - has fluctuated severely, jumping from -0.5% in July 2022 to 4% in September 2023. Surging borrowing costs dissuaded households from buying new homes, leaving many to stay within the rental market. This damaged revenue pipelines and hit profitability in 2023, as high-value residential property purchases slumped. Simultaneously, commercial clients were reluctant to commit to property investments amid high borrowing costs, exacerbating barriers to agencies' income in 2023. In 2026, revenue is slated to sink by 1.6%. After a period of easing monetary policy in 2024 and 2025, the ECB elevated its deposit facility rate from 2% to 2.25% in June 2026, amid fears of spiralling inflation related to the US conflict with Iran, ramping up borrowing costs and adding to investor and buyer caution. Tighter sustainability standards, spearheaded by the EU Energy Performance of Buildings Directive, have pushed property owners to invest in retrofits targeting energy efficiency to retain portfolio quality and align with regulation. The costs tied to these improvements are set to weigh on short-term profitability, but could solidify long-term revenue streams by preserving asset values. New private rental regulation in the UK (the Renters' Rights Act) and Ireland (The Residential Tenancies (Miscellaneous Provisions) Act 2026) is set to shake up the lettings market, with bans on "no-fault evictions" and tighter caps on rent price hikes prompting landlords to sell up or boost rents prior to enforcement. Compliance-related costs are set to amplify profit struggles in 2026, but long-term rental market stability through improved renters' rights could secure revenue streams. Over the five years through 2031, revenue is expected to swell at a compound annual rate of 3% to €761.2 billion. Geopolitical instability could disrupt real estate activities, as uncertainty surrounding monetary policy decisions weighs on property purchases and investment. Nonetheless, Europe-wide efforts to bolster housing supply could spur transactions and leasing for residential property owners, despite potential shifts in rental and house price dynamics as supply chases demand. The industrial and logistics sector offers respite from revenue volatility, with strong manufacturing and retail activities pushing third-party logistics companies to expand their networks.

The Office of Housing & Community Development Focuses on the full range of land development activities, including vacant property initiatives, housing & community development programs.Data Dictionary:“LBA” is the abbreviation for the Louisville and Jefferson County LBA Authority, Inc."Parcel ID" is an identification code assigned to a piece of real estate by the Jefferson County Property Valuation Administration. The Parcel ID is used for record keeping and tax purposes.“IMPROV” stands for whether or not the real estate parcel had an “improvement” (i.e., a structure) situated on it at the time it was sold. “1” indicates that a structure existed when the parcel was sold and “0” indicates that the parcel was an empty, piece of land.“APPLICANT” is the individual(s) or active business entity that submitted an Application to Purchase the real estate parcel and whose application was presented to and approved by the LBA’s Board of Directors. The Board of Directors must approve each application before a transfer deed is officially recorded with the Office of the County Clerk of Jefferson County, Kentucky.“SALE DATE” is the date that the Applicant signed the transfer deed for the respective real estate parcel.“SALE AMOUNT” is the amount that the Applicant paid to purchase the respective real estate parcel.“SALE PROGRAM” is the LBA’s disposition program that the Applicant participated in to acquire the real estate parcel.The Office of Community Development defines each “Sale Program” as follows:Budget Rate (“Budget Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land.Cut It Keep It - Applicant requested to maintain the empty piece of land situated on the same block as a real estate parcel owned by the Applicant. Applicant must retain ownership of the lot for three (3) years before the Applicant can sell it.Demo for Deed (“Last Look – Demo for Deed”) – Applicant requested to demolish the structure situated on the real estate parcel and retain the land for a future use.Flex Rate (“Flex Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land but did not have proof of funding or a timeline as to when the project would be completed.Metro Redevelopment – The real estate parcel was part of a redevelopment project being considered by Metro Government.Minimum Pricing Policy – The pricing policy that was approved by the LBA’s Board of Directors and in effect as of the real estate parcel’s sale date.RFP (“Request for Proposals”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Save the Structure (“Last Look – Save the Structure”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Side Yard – The Applicant requested to acquire the LBA’s adjoining piece of land to make the Applicant’s occupied, real estate parcel larger and more valuable.SOI (“Solicitation of Interest”) – The LBA assembled two (2) or more real estate parcels and the Applicant submitted a redevelopment project for the subject parcels.For more information about each of the current disposition programs that the LBA offers, please refer to the following website pages:https://louisvilleky.gov/government/community-development/vacant-lot-sales-programshttps://louisvilleky.gov/government/community-development/vacant-structures-saleContact:Connie Suttonconnie.sutton@louisvilleky.gov