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.

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...

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.

AU and NZ Real Estate Advisory Service Market size was valued at USD 6.0 Billion in 2024 and is projected to reach USD 6.2 Billion by 2032, growing at a CAGR of 2.8% from 2026 to 2032.

AU and NZ Real Estate Advisory Service Market Drivers

1. Urbanisation and Economic Growth

In major cities like Sydney, Melbourne, Auckland, and Wellington, the economy is still expanding, which is driving up demand for real estate consultancy services.Opportunities are being created in both the residential and commercial sectors by urban sprawl and infrastructure development (such as transportation and commercial zones).2. Migration and Population Growth The need for housing, rental units, and mixed-use complexes is increasing due to the high rate of population increase, especially from immigration.

Advisory firms are assisting customers in locating investment possibilities and places with strong demand.

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.

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.

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/

Dataset Description

This dataset contains information about real estate listings, including various features of properties and their surrounding areas. The data can be used for analysis, prediction, and insights into the real estate market.

Features

• airports_nearest: Distance to the nearest airport in meters
• balcony: Number of balconies
• ceiling_height: Ceiling height in meters
• cityCenters_nearest: Distance to the city center in meters
• days_exposition: Number of days the listing was active (from publication to removal)
• first_day_exposition: Publication date
• floor: Floor number of the property
• floors_total: Total number of floors in the building
• is_apartment: Boolean indicating if the property is an apartment
• kitchen_area: Kitchen area in square meters
• last_price: Price at the time of listing removal
• living_area: Living area in square meters
• locality_name: Name of the locality
• open_plan: Boolean indicating if the property has an open floor plan
• parks_around3000: Number of parks within a 3 km radius
• parks_nearest: Distance to the nearest park in meters
• ponds_around3000: Number of ponds/water bodies within a 3 km radius
• ponds_nearest: Distance to the nearest pond/water body in meters
• rooms: Number of rooms
• studio: Boolean indicating if the property is a studio apartment
• total_area: Total area of the property in square meters
• total_images: Number of photos in the listing

Additional Economic Data

1. Currency Exchange Rate:

   • USD to RUB exchange rate for the period 2014-2019. This data allows for analysis of how currency fluctuations impact the real estate market.

2. Central Bank of Russia Data:

   • Key interest rate: The central bank's key policy rate, which influences lending rates and overall economic conditions.
   • Inflation rate: The rate of price increases in the economy, which can affect real estate values and investment decisions.

Potential Use Cases

1. Price Prediction: Develop machine learning models to predict property prices based on various features, including economic indicators.
2. Market Trend Analysis: Analyze how different factors, including macroeconomic variables, influence property values over time.
3. Geographical Insights: Study how location and proximity to amenities affect property desirability and pricing.
4. Listing Optimization: Investigate the relationship between listing characteristics (e.g., number of images) and time-to-sell.
5. Property Type Comparison: Compare different types of properties (e.g., studios vs. multi-room apartments) in terms of pricing and demand.
6. Urban Planning: Analyze the impact of parks, water bodies, and other urban features on property values.
7. Temporal Analysis: Study seasonal trends and long-term changes in the real estate market, considering economic factors.
8. Economic Impact Analysis: Investigate how changes in currency exchange rates, interest rates, and inflation affect the real estate market.
9. Investment Strategy: Develop models to identify optimal times for real estate investment based on economic indicators.
10. Risk Assessment: Analyze the volatility of the real estate market in relation to economic factors.

This rich dataset provides ample opportunities for both beginner and advanced data scientists to explore various aspects of the Russian real estate market. It allows for:

• Practice in data cleaning and preprocessing, especially in handling time series data
• Feature engineering, including creating derived features from economic indicators
• Applying different machine learning algorithms for regression and classification tasks
• Conducting time series analysis and forecasting
• Performing multivariate analysis to understand the complex relationships between property characteristics, location features, and economic factors

Researchers and analysts can use this dataset to gain insights into the dynamics of the Russian real estate market, understand the impact of macroeconomic factors on property values, and develop sophisticated models for price prediction and market analysis.

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 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.

snapshot-tab-pane Real Estate Market Size 2026-2030The real estate market size is valued to increase by USD 1325.6 billion, at a CAGR of 5.6% from 2025 to 2030. Acceleration of institutional capital inflows and favorable monetary policy will drive the real estate market.Major Market Trends & InsightsAPAC dominated the market and accounted for a 58.2% growth during the forecast period.By Type - Residential segment was valued at USD 1677.7 billion in 2024By Business Segment - Rental segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 2172.7 billionMarket Future Opportunities: USD 1325.6 billionCAGR from 2025 to 2030 : 5.6%Market SummaryThe real estate market is navigating a structured transition, shifting from broad valuation adjustments to a performance-driven environment. This recalibration is propelled by the integration of property technology and a deepening commitment to environmental social governance standards, which are becoming prerequisites for institutional capital.A key driver remains the stabilization of global interest rates, allowing for more predictable debt and borrowing costs and encouraging capital inflows into core assets like industrial logistics and multifamily housing. These sectors benefit from structural tailwinds such as e-commerce expansion and urban housing shortages.As a boardroom-level decision, asset managers now utilize predictive analytics for sophisticated portfolio optimization, assessing risk and identifying opportunities in real-time.For instance, a global investment firm might model the impact of hybrid work models on its office portfolio, deciding to divest underperforming assets and reinvest in adaptive reuse projects, thereby enhancing overall asset performance and rental resilience without direct new construction.This data-centric approach to capital allocation highlights the industry's evolution toward operational efficiency and strategic risk management in a complex macroeconomic landscape.What will be the Size of the Real Estate Market during the forecast period? Get Key Insights on Market Forecast (PDF) Get Free SampleHow is the Real Estate Market Segmented?The real estate industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD billion" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.TypeResidentialCommercialIndustrialBusiness segmentRentalSalesManufacturing typeNew constructionRenovation and redevelopmentLand developmentGeographyAPACChinaJapanIndiaNorth AmericaUSCanadaMexicoEuropeGermanyFranceUKSouth AmericaBrazilArgentinaMiddle East and AfricaSaudi ArabiaTurkeyUAERest of World (ROW)By Type InsightsThe residential segment is estimated to witness significant growth during the forecast period.The residential segment is undergoing a structural transition defined by institutional investment and a pronounced shift toward premiumization.Global investment firms and private equity real estate funds are channeling capital into build-to-rent and single-family rental portfolios, viewing them as resilient alternative asset classes.This trend is driven by persistent housing shortages and a rising demand for professionally managed, amenity-rich spaces.To ensure rental resilience and optimize returns, operators are leveraging proptech integration for streamlined tenant acquisition and lease management, with a focus on enhancing the user experience.Advanced platforms have demonstrated the ability to reduce vacancy periods by up to 15% through targeted marketing and efficient application processing, solidifying the move towards data-driven asset management. Get Free SampleThe Residential segment was valued at USD 1677.7 billion in 2024 and showed a gradual increase during the forecast period. Get Free SampleRegional AnalysisAPAC is estimated to contribute 58.2% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. See How Real Estate Market Demand is Rising in APAC Get Free SampleThe geographic landscape is increasingly shaped by environmental social governance criteria, which heavily influence capital allocation by institutional capital and real estate investment trust funds.A pronounced focus on decarbonization and climate risk mitigation is driving investment toward assets with green building certification and those aligned with stringent energy efficiency regulations. In several key regions, certified net zero buildings now command a 5-10% rental premium.Investment management strategies are prioritizing purpose-built living solutions and adaptive reuse projects over new-b

Building land is scarce – especially in university towns and growth regions in North Rhine-Westphalia. In doing so, the identification of new residential development areas competes – depending on the Region – with the legitimate claims of agriculture and the protection of open spaces in the sense of a Sustainable land policy.
One approach to creating more affordable housing is the use of the quoting tool for publicly funded housing construction at the municipal level. The question "Which cities in the state of North Rhine-Westphalia work with the instrument of quoting for publicly funded housing construction or where is the use of the instrument in planning?" was the subject of a nationwide non-scientific survey: Between 23 February 2022 and 29 April 2022, the Ministry of Home Affairs: Municipality, construction and digitalisation of the state of North Rhine-Westphalia (department "residential development") and the state-owned development bank, the NRW.BANK, surveyed all 53 granting authorities for public housing promotion with regard to the use and design of municipal quotas for public-funded housing construction in North Rhine-Westphalia. All licensing authorities have participated in the survey. This results in 396 municipalities. All results can be found under PRELIMINARY 18/1172 https://www.landtag.nrw.de/portal/WWW/dokumentenarchiv/Dokument/MMV18-1172.pdf

Techsalerator’s Real Estate & Airbnb Data for the United States of America

Techsalerator’s Real Estate & Airbnb Data for the United States of America delivers a comprehensive and data-driven view of the country’s residential property landscape and short-term rental market. This dataset is designed to support investors, developers, researchers, travel platforms, and policy analysts seeking insights into property values, rental trends, occupancy performance, and market dynamics across the United States’ major metropolitan areas and tourism-driven destinations.

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

Top 5 Key Data Fields

1. Property Location (City, State, Latitude & Longitude)
   Identifies precise geographic placement of residential and short-term rental properties.

2. Property Type
   Categorizes listings by apartment, single-family home, condominium, townhouse, vacation rental, shared space, or commercial-use properties.

3. Listing Price & Estimated Value
   Captures sale prices, long-term rental rates, Airbnb nightly prices, and estimated market valuations.

4. Occupancy & Availability Rate
   Tracks how frequently Airbnb and short-term rental properties are booked across seasons, regions, and events.

5. Host & Property Performance Metrics
   Includes reviews, ratings, booking frequency, cancellation rates, and host responsiveness.

Top 5 Real Estate & Airbnb Market Trends in the United States of America

1. High Activity in Major Urban & Vacation Markets
   New York City, Los Angeles, Miami, Orlando, and Las Vegas lead in real estate and Airbnb demand.

2. Strong Growth in Short-Term Rental Investments
   Investors are increasingly targeting vacation rentals in tourist-heavy and high-yield markets.

3. Regulatory Variations Across Cities & States
   Short-term rental laws differ significantly, impacting supply, pricing, and operations.

4. Rise of Remote Work & Mid-Term Rentals
   Increased demand for 30+ day stays driven by remote workers and relocating professionals.

5. Expansion of Luxury & Experiential Stays
   Growth in premium listings, unique properties, and experience-driven accommodations.

Top 5 Applications of Real Estate & Airbnb Data in the United States of America

1. Investment & Market Feasibility Studies
   Helps investors identify high-performing markets, rental yields, and pricing benchmarks.

2. Tourism & Hospitality Strategy Optimization
   Enables businesses to align offerings with seasonal demand, events, and traveler preferences.

3. Urban Planning & Housing Policy Development
   Supports policymakers in managing housing supply and regulating short-term rentals.

4. Regulatory Compliance & Market Monitoring
   Assists in tracking local laws and their impact on property availability and pricing.

5. Property Management & Revenue Optimization
   Helps hosts and property managers maximize occupancy rates and rental income.

Accessing Techsalerator’s Real Estate & Airbnb Data

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

Included Data Fields

• Property Location (Latitude & Longitude)
• City & State Names
• Property Type
• Sale Price & Rental Price
• Airbnb Daily Rate
• Occupancy Rate
• Availability Calendar
• Number of Reviews
• Guest Ratings
• Estimated Revenue
• Host Activity Metrics
• Data Source Platform
• Timestamp of Listing Update

Contact Information

For actionable insights into property performance, rental trends, and Airbnb activity in the United States of America, Techsalerator’s Real Estate & Airbnb Data empowers investors, researchers, developers, hospitality platforms, and policymakers with reliable, structured, and scalable intelligence.

📩 Email: info@techsalerator.com

Explore Twin Falls, ID rental market 2026. The average long-term prices $1,549 and short-term $1,958, with trends shaping housing in a city of 53,219 residents.

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.

In 2025, Turkey had the highest inflation-adjusted house price index out of the ** European countries under observation, making it the country where house prices have increased the most since 2010. In Turkey, the house price index exceeded *** index points in the second quarter of 2025, showing an increase in real terms of *** percent since 2010, the baseline year for the index. Iceland and Hungary completed the top three, with an index value of *** and *** index points. In the past year, however, many European countries saw house prices decline in real terms. Where can I find other metrics on different housing markets in Europe? To assess the valuation in different housing markets, one can compare the house-price-to-income ratios of different countries worldwide. These ratios are calculated by dividing nominal house prices by nominal disposable income per head. There are also ratios that look at how residential property prices relate to domestic rents, such as the house-price-to-rent ratio for the United Kingdom. Unfortunately, these numbers are not available in a European overview. An overview of the price per square meter of an apartment in the EU-28 countries is available, however. One region, different markets An important trait of the European housing market is that there is not one market, but multiple. Property policy in Europe lies with the domestic governments, not with the European Union. This leads to significant differences between European countries, which shows in, for example, the homeownership rate (the share of owner-occupied dwellings of all homes). These differences also lead to another problem: the availability of data. Non-Europeans might be surprised to see that house price statistics vary in depth, as every country has their own methodology and no European body exists that tracks this data for the whole continent.

This dataset provides a comprehensive overview of new housing price indexes in Canada. The data is sourced from a reliable statistical survey, offering a detailed breakdown of housing prices across different components such as total house and land, house only, and land only. The dataset is structured to include key metrics such as geographical location, price index classification, and specific price values, providing a robust foundation for analyzing housing price dynamics within the country.

Key Features

• Price Index Metrics: The dataset includes price indexes for total house and land, house only, and land only, providing a complete picture of housing price dynamics across different components.
• Geographical Focus: Data is specific to Canada, providing insights into national housing price trends and patterns.
• Unit of Measurement: Information is presented in index units (201612=100), allowing for straightforward analysis and comparison.
• Temporal Precision: The data is time-stamped for January 1981, ensuring relevance and accuracy for temporal analysis.

Potential Uses

• Real Estate Market Analysis: Assist in understanding the housing price dynamics in Canada, which is crucial for real estate market forecasting and planning.
• Investment Decisions: Provide insights into optimal investment strategies for real estate in various regions.
• Economic Policy: Support policymakers in monitoring and ensuring compliance with housing market trends and economic standards.
• Market-Specific Insights: Evaluate the impact of housing price trends on specific regions and potential growth or decline areas.
• Strategic Planning: Inform strategic planning for real estate developers and policymakers by providing a clear snapshot of current housing price levels and trends.

Explore Winter Haven, FL rental market 2026. The average long-term prices $1,819 and short-term $2,170, with trends shaping housing in a city of 52,846 residents.

Single-Family Rental Analytics Market Outlook

According to our latest research, the Global Single-Family Rental Analytics market size was valued at $2.3 billion in 2024 and is projected to reach $8.7 billion by 2033, expanding at a robust CAGR of 15.6% during the forecast period of 2025–2033. The primary factor driving this remarkable growth is the increasing institutionalization of the single-family rental (SFR) sector, which is fueling demand for advanced analytics to optimize property management, investment strategies, and tenant experiences. As the SFR market matures, industry stakeholders are leveraging sophisticated analytics platforms to enhance portfolio performance, mitigate risk, and capitalize on emerging investment opportunities, further accelerating the adoption of single-family rental analytics solutions globally.

Regional Outlook

North America currently commands the largest share of the global single-family rental analytics market, accounting for approximately 48% of the total market value in 2024. This dominance is underpinned by a mature real estate market, widespread technological adoption, and the presence of several leading analytics software providers. The United States, in particular, has witnessed a surge in institutional investment in single-family rental properties, with large-scale operators relying heavily on data-driven solutions for portfolio management and operational efficiency. Additionally, favorable regulatory frameworks and a heightened focus on transparency have fostered an environment conducive to analytics innovation. The North American market is further bolstered by a robust ecosystem of proptech startups, established real estate agencies, and a tech-savvy tenant base, all of which contribute to the region’s sustained growth and leadership in the SFR analytics space.

Asia Pacific is emerging as the fastest-growing region in the single-family rental analytics market, with a projected CAGR of 18.2% from 2025 to 2033. This rapid expansion is driven by increasing urbanization, rising disposable incomes, and a growing appetite for real estate investment diversification among institutional and individual investors. Countries such as China, Australia, and Japan are witnessing a transformation in their residential rental markets, with analytics solutions being adopted to manage risk, assess property values, and streamline tenant screening processes. The proliferation of cloud-based analytics platforms and the integration of artificial intelligence are further accelerating adoption rates across the region. As governments in Asia Pacific implement policies to encourage housing market transparency and data-driven decision-making, the demand for advanced SFR analytics is expected to escalate, positioning the region as a key engine of future market growth.

In emerging economies across Latin America, the Middle East, and Africa, the adoption of single-family rental analytics remains in its nascent stages but is gaining momentum. Localized challenges such as fragmented property markets, limited data infrastructure, and regulatory uncertainties have historically impeded the widespread implementation of analytics solutions. However, increasing digitalization, the entry of global proptech firms, and policy initiatives aimed at modernizing the real estate sector are gradually bridging these gaps. In these regions, analytics adoption is being driven by the need for improved transparency, risk mitigation, and portfolio optimization, particularly among institutional investors seeking to capitalize on untapped rental markets. As data availability improves and regulatory frameworks evolve, emerging economies are poised to play a more significant role in the global SFR analytics landscape over the forecast period.

Report Scope

Global short-term rental intelligence sourced from leading Online Travel Agencies (OTAs). The OTA Real Estate Dataset provides a comprehensive view of the global vacation rental market by combining verified OTA property listings with valuation, tax, and physical asset data. Each record includes unique property identifiers, geolocation details, pricing indicators, and occupancy metrics—enabling robust market analysis and investment-grade insights.

Continuously sourced from major OTA platforms and refined through proprietary data-cleaning models, this dataset ensures accuracy, consistency, and comparability across regions. Available globally with flexible delivery via API or flat files, it serves as a foundational dataset for those analyzing market performance, forecasting development potential, or conducting housing research.

Key Highlights: Granular Real Estate Intelligence: Combines OTA listing data with valuation, tax, and property attributes for a holistic view of market activity.

Global and Standardized: Harmonized schema and coverage across countries, cities, and neighborhoods for cross-market comparability.

High-Fidelity Data: Proprietary normalization removes duplicates and outliers to ensure analytical precision.

Flexible Access: Delivered through API or CSV, updated regularly for timely decision-making.

Ideal For: Real Estate Investors: Identify high-performing short-term rental markets and assess yield potential.

Developers & Urban Planners: Evaluate spatial demand patterns and inform development feasibility studies.

Financial Institutions: Integrate standardized OTA data into underwriting, risk, and valuation models.

Tourism Economists & Market Researchers: Quantify the impact of vacation rentals on local housing and tourism dynamics.

Use It To: Benchmark short-term rental performance by region or property type.

Analyze shifts in rental demand and pricing over time.

Support market-entry, site-selection, and feasibility studies with real OTA-backed data.

Enhance research and policy analysis with consistent, globally comparable property-level insights.