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.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

The market is segmented by Type (Apartments and Condominiums, Villas and landed houses) and by Cities (Tokyo, Kyoto, Osaka and Other Cities). The report offers market size and forecasts for luxury residential real estate market in Japan for all above segments.

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q1 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

Year, administrative area, number of land parcels, land area, number of buildings, building area

In 2021, Allegheny County Economic Development (ACED), in partnership with Urban Redevelopment Authority of Pittsburgh(URA), completed the a Market Value Analysis (MVA) for Allegheny County. This analysis services as both an update to previous MVA’s commissioned separately by ACED and the URA and combines the MVA for the whole of Allegheny County (inclusive of the City of Pittsburgh). The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies. This MVA utilized data that helps to define the local real estate market. The data used covers the 2017-2019 period, and data used in the analysis includes: * Residential Real Estate Sales * Mortgage Foreclosures * Residential Vacancy * Parcel Year Built * Parcel Condition * Building Violations * Owner Occupancy * Subsidized Housing Units The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources. Please refer to the presentation and executive summary for more information about the data, methodology, and findings.

The Vietnam Residential Real Estate Market is Segmented by Property Type (Apartments and Condominiums, and Villas and Landed Houses), by Price Band (Affordable, Mid-Market and Luxury), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Key Cities and Provinces (Ho Chi Minh City, Hanoi, Danang, Hai Phong and More). The Market Forecasts are Provided in Terms of Value (USD)

China Real Estate Investment: 35 City data was reported at 5,769,036.860 RMB mn in 2018. This records an increase from the previous number of 5,486,506.680 RMB mn for 2017. China Real Estate Investment: 35 City data is updated yearly, averaging 1,760,467.885 RMB mn from Dec 1999 (Median) to 2018, with 20 observations. The data reached an all-time high of 5,769,036.860 RMB mn in 2018 and a record low of 285,547.330 RMB mn in 1999. China Real Estate Investment: 35 City data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Real Estate Sector – Table CN.RKE: Real Estate Investment: City.

This research data file contains the necessary software and the dataset for estimating the missing prices of house units. This approach combines several machine learning techniques (linear regression, support vector regression, the k-nearest neighbors and a multi-layer perceptron neural network) with several dimensionality reduction techniques (non-negative factorization, recursive feature elimination and feature selection with a variance threshold). It includes the input dataset formed with the available house prices in the center of Teruel city (Spain) in December 30, 2016 from Idealista website.

This dataset supports the research of the authors in the improvement of the setup of agent-based simulations about real-estate market. The work about this dataset has been submitted for consideration for publication to a scientific journal.

The open source python code is composed of all the files with the “.py” extension. The main program can be executed from the “main.py” file. The “boxplotErrors.eps” is a chart generated from the execution of the code, and compares the results of the different combinations of machine learning techniques and dimensionality reduction methods.

The dataset is in the “data” folder. The input raw data of the house prices are in the “dataRaw.csv” file. These were shuffled into the “dataShuffled.csv” file. We used cross-validation to obtain the estimations of house prices. The outputted estimations alongside the real values are stored in different files of the “data” folder, in which each filename is composed by the machine learning technique abbreviation and the dimensionality reduction method abbreviation.

In 2023, the UK Real Estate Market reached a value of USD 816.7 million, and it is projected to surge to USD 919.0 million by 2030.

This City Owned Property data has been compiled from deeds, maps, assessor records, and other public records on file in the City of Hartford. The intent of this data layer is to depict a graphical representation of real property information relative to the planimetric features for the City of Hartford and is subject to change as a more accurate survey may disclose.

Indian Metropolitan Cities Real Estate Market Outlook to 2016" provides a comprehensive analysis of the various aspects such as market size of the real estate industry and segments such as residential, commercial, retail and the hotel acros

In 2024, around *** percent of Perth's industrial and logistics real estate inventory was vacant. In comparison, the vacancy rate of industrial and logistics real estate in Ho Chi Minh City was about ** percent that year.

The Denmark Luxury Residential Real Estate Market Report is Segmented by Type (Apartments and Condominiums, Villas, and Landed Houses) and by Cities (Copenhagen, Aarhus, Odense, Aalborg, and the Rest of Denmark). The Report Offers Market Size and Forecasts for the Denmark Luxury Homes Market in Value (USD Billion) for all the Above Segments.

China Real Estate Investment: 35 City: Commercial Bldg data was reported at 736,128.380 RMB mn in 2017. This records a decrease from the previous number of 737,688.290 RMB mn for 2016. China Real Estate Investment: 35 City: Commercial Bldg data is updated yearly, averaging 166,013.730 RMB mn from Dec 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 737,688.290 RMB mn in 2016 and a record low of 31,407.500 RMB mn in 1999. China Real Estate Investment: 35 City: Commercial Bldg data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Real Estate Sector – Table CN.RKE: Real Estate Investment: City.

We used the open-access Zillow Inc. GetSearchResults API to sample house data for each ZPID in accordance with daily API call limits. For more information on the API see the official documentation page: https://www.zillow.com/howto/api/GetSearchResults.htm. We anonymized the property address and ZPID fields.

China Real Estate Industry: 35 City: Revenue: Other Revenue data was reported at 202,408.990 RMB mn in 2017. This records an increase from the previous number of 163,668.420 RMB mn for 2016. China Real Estate Industry: 35 City: Revenue: Other Revenue data is updated yearly, averaging 55,093.835 RMB mn from Dec 1988 (Median) to 2017, with 30 observations. The data reached an all-time high of 202,408.990 RMB mn in 2017 and a record low of 590.630 RMB mn in 1990. China Real Estate Industry: 35 City: Revenue: Other Revenue data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Real Estate Sector – Table CN.RKE: Real Estate Enterprise Financial Data: City.

In late 2016, the URA, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for the City of Pittsburgh. The Market Value Analysis (MVA) offers an approach for community revitalization; it recommends applying interventions not only to where there is a need for development but also in places where public investment can stimulate private market activity and capitalize on larger public investment activities. The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional neighborhood boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies.

Pittsburgh’s 2016 MVA utilized data that helps to define the local real estate market between July, 2013 and June, 2016:

• Median Sales Price

• Variance of Sales Price

• Percent Households Owner Occupied

• Density of Residential Housing Units

• Percent Rental with Subsidy

• Foreclosures as a Percent of Sales

• Permits as a Percent of Housing Units

• Percent of Housing Units Built Before 1940

• Percent of Properties with Assessed Condition “Poor” or worse

• Vacant Housing Units as a Percentage of Habitable Units

The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources.

During the research process, staff from the URA and Reinvestment Fund spent an extensive amount of effort ensuring the data and analysis was accurate. In addition to testing the data, staff physically examined different areas to verify the data sets being used were appropriate indicators and the resulting MVA categories accurately reflect the market.

The Scandinavian real estate market, encompassing countries like Sweden, Norway, Denmark, and Finland, exhibits robust growth potential, fueled by a confluence of factors. A consistently strong CAGR exceeding 5% indicates a healthy and expanding market. Key drivers include increasing urbanization, a growing population, particularly in major cities like Stockholm, Oslo, and Copenhagen, and a rising demand for both residential and commercial properties. The market is segmented into villas and landed houses, catering to affluent buyers seeking larger spaces and more privacy, and apartments and condominiums, which represent a more significant portion of the market due to higher population density in urban centers and appeal to a wider range of buyers. Furthermore, government initiatives aimed at improving infrastructure and boosting sustainable housing contribute positively to market expansion. While fluctuating interest rates and potential economic downturns pose challenges, the Scandinavian region's strong economic fundamentals and consistently high demand suggest sustained growth in the medium to long term. Specific market segments like luxury properties and sustainable building designs are experiencing accelerated growth. The presence of established and well-regarded players, including Riksbyggen, Balder, and others, underscores the market's maturity and competitiveness. The strong performance of the Scandinavian economies, coupled with a focus on quality of life and attractive urban landscapes, further enhances the appeal of the region's real estate sector, ensuring sustained growth prospects for the coming years. The regional distribution of this growth is varied. While the Nordics dominate the market currently, other European regions may experience increased investment due to spillover effects and cross-border investments. International investors are actively participating, drawn by the stable political climate, transparent regulatory frameworks, and potential for long-term appreciation. However, challenges exist in the form of rising construction costs and limited land availability in prime urban areas. These constraints, while present, are unlikely to significantly impede the overall market growth trajectory, given the underlying demand and continued governmental support for the sector. Looking ahead, the Scandinavian real estate market is positioned for continued expansion, driven by demographic trends, economic stability, and ongoing efforts to create attractive and sustainable living environments. The diverse range of property types and significant involvement of major players suggest a robust and resilient market poised for further growth in the years to come. Recent developments include: April 2022: Trivselhus developed a new product called Stella 131. Stella 131 is a well-planned house that fits perfectly on narrower plots as the entrance is located on the gable. Exits for four directions make the house easy to place on the plot and provide the opportunity to create several patios for both sun and shade. The slightly elevated wall life on the façade allows for space for an awning or pergola., April 2022: The Lindbacks has signed an agreement with K-fast, Eskilstuna's municipal properties. The agreement includes building of 86 rental apartments in three wooden buildings with geothermal heating and solar cells. . Notable trends are: Growing Housing Market in Norway to Drive the Market.

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