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.

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/

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

The quarterly pulse monitor expects the Dutch house prices to climb by *** percent in 2025 due to the decline in purchasing power, higher cost of borrowing and worsening economic conditions. The price of Dutch residential property in 2025 was approximately ******* euros. These developments came on top of other issues that were already prevalent in the Dutch housing market, such as the discussion about nitrogen and its effect on housing construction. The effects of nitrogen on the price of a house At the end of 2019, months before the coronavirus, there was already a lot of uncertainty whether their predictions would hold true. This had to do with the so-called “nitrogen decision” (in Dutch: stikstofbesluit) in May 2019. Simply put, a Dutch advisory body found that the domestic policy for nitrogen emission (formally known as Programmatische Aanpak Stikstof or Programmatic Approach Nitrogen) went against European rules. As of August 2019, a sizable share of the Dutch population was not familiar with this nitrogen policy. However, the advisory body’s decision led to an immediate stop to all construction in the country (amongst other things). By the end of 2019, this stop was still in place. For 2020, newly to be constructed houses have to comply to new rules regarding nitrogen emission. This puts new pressure on a housing market that already had to keep with increasing demand. How about the housing market in Amsterdam? In the year 2022, Amsterdam ranked as the most expensive city in the Netherlands to acquire an apartment, with an average price per square meter that was ***** euros more expensive than in Utrecht. Amsterdam was also well above the average rents found in other cities. A house in Amsterdam had a rent of approximately ** euros per square meter in 2023, whereas rents in Rotterdam cost roughly ** euros per square meter. It should be noted, however, that rent changes in the Dutch capital are significantly lower than those found in Rotterdam and especially Utrecht.

Context

This dataset contains Real Estate listings in the US broken by State and zip code.

Download

kaggle API Command !kaggle datasets download -d ahmedshahriarsakib/usa-real-estate-dataset

Content

The dataset has 1 CSV file with 10 columns -

1. realtor-data.csv (2,226,382 entries)
   • brokered by (categorically encoded agency/broker)
   • status (Housing status - a. ready for sale or b. ready to build)
   • price (Housing price, it is either the current listing price or recently sold price if the house is sold recently)
   • bed (# of beds)
   • bath (# of bathrooms)
   • acre_lot (Property / Land size in acres)
   • street (categorically encoded street address)
   • city (city name)
   • state (state name)
   • zip_code (postal code of the area)
   • house_size (house area/size/living space in square feet)
   • prev_sold_date (Previously sold date)

NB: 1. brokered by and street addresses were categorically encoded due to data privacy policy 2. acre_lot means the total land area, and house_size denotes the living space/building area

Acknowledgements

Data was collected from - - https://www.realtor.com/ - A real estate listing website operated by the News Corp subsidiary Move, Inc. and based in Santa Clara, California. It is the second most visited real estate listing website in the United States as of 2024, with over 100 million monthly active users.

Cover Image

Image by Mohamed Hassan from Pixabay

Disclaimer

The data and information in the data set provided here are intended to use for educational purposes only. I do not own any data, and all rights are reserved to the respective owners.

Inspiration

• Can we predict housing prices based on the features?
• How are housing price and location attributes correlated?
• What is the overall picture of the USA housing prices w.r.t. locations?
• Do house attributes (bedroom, bathroom count) strongly correlate with the price? Are there any hidden patterns?

Techsalerator’s Real Estate & Airbnb Data for Portugal

Techsalerator’s Real Estate & Airbnb Data for Portugal 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 Portugal’s major cities and tourism-driven regions.

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, District, Latitude & Longitude)
   Identifies precise geographic placement of residential and short-term rental properties.

2. Property Type
   Categorizes listings by apartment, villa, house, holiday 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 and regions.

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

Top 5 Real Estate & Airbnb Market Trends in Portugal

1. High Demand in Coastal & Urban Areas
   Lisbon, Porto, and the Algarve region lead in both real estate investment and Airbnb activity.

2. Tourism-Driven Short-Term Rental Growth
   Strong international tourism continues to drive high occupancy rates, especially in coastal destinations.

3. Regulatory Controls on Short-Term Rentals
   Government policies and licensing restrictions are shaping Airbnb supply in key cities.

4. Rising Property Prices in Prime Locations
   Increased foreign investment has driven up property values in popular areas.

5. Growth of Long-Stay & Digital Nomad Rentals
   Portugal’s popularity among remote workers is increasing demand for mid- to long-term stays.

Top 5 Applications of Real Estate & Airbnb Data in Portugal

1. Investment & Market Feasibility Studies
   Helps investors identify high-yield locations, pricing benchmarks, and demand patterns.

2. Tourism & Hospitality Strategy Optimization
   Enables businesses to align offerings with peak travel seasons and visitor 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 Airbnb laws and their impact on property availability and pricing.

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

Accessing Techsalerator’s Real Estate & Airbnb Data

To obtain Techsalerator’s Real Estate & Airbnb Data for Portugal, 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 & District 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 Portugal, Techsalerator’s Real Estate & Airbnb Data empowers investors, researchers, developers, hospitality platforms, and policymakers with reliable, structured, and scalable intelligence.

📩 Email: info@techsalerator.com

This is a compilation of the current status of the standard market value for buildings other than housing in Pyeongtaek-si, Gyeonggi-do. It provides information that can be used to determine the building asset value calculated according to the purpose, structure, area, etc. of the building. The standard market value is the amount that serves as the tax base for property tax, acquisition tax, etc. based on the Local Tax Act, etc., and is announced every year for fair taxation and real estate value calculation. This data consists of items such as building type, property address, total floor area, exclusive area, land use on the register, and standard market value, and can be used to compare and analyze the value of various types of non-residential buildings. This data can be used for various administrative and policy purposes such as local tax imposition, real estate statistical analysis, property evaluation, and administrative plan establishment.

Unit root test for variables after first order differencing.

Develop Louisville Focuses on the full range of land development activities, including planning and design, vacant property initiatives, advanced planning, housing & community development programs, permits and licensing, land acquisition, public art and clean and green sustainable development partnerships.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

The data compilation is a review of the state-aided housing construction development in the Federal Republic of Germany. Public housing aims at the supply of cost-saving housing space for a special group of people, specified by law. In addition to the creation of low-cost living space the acquisition of owner-occupied real estate has been funded according to the second housing act, so that real estate was possible for broad levels of the population. Using different forms of subsidies (construction cost and expense subsidy, interest subsidy), the rents could be reduced below a rent needed to cover the costs of the residence and thereby opened for the legitimate low-income groups (direct funding of the projected buildings or flats: object-based aid or object support). This law has been replaced in 2001 by the reform of the social housing law. It regulates the housing and other measures to support households with rental housing, including housing cooperatives and the creation of owner-occupied real estate property for households that cannot adequately supply themselves with living space on the housing market. The construction of social housing in Germany on the basis of the second housing act has been a form of state transfer payment. Additionally social housing policy up to the 90s with its comprehensive public investments has been an important element of the state’s impact on the economy and urban development policy. This social housing act has been replaced by the new social housing law in 2001, a housing policy support instrument of the federal government and the governments of the single German federal ‘Länder’ (federal states), which consists of actions on several levels: social housing assistance, housing benefits, property development, building society promotion, housing bonus, pension fund law, residential support programs of the KfW development bank "initiative to build cost-effective and quality conscious”. The statistics of the grants for social housing applies to housing construction projects that are funded by the public sector in social housing. In addition, the purchase of existing homes, which were funded by public sector, is included. The statistics on permits included until 1999 the following information: (1) Funded apartments and buildings (new building), without / with condominiums, (2) grant funds by purpose (promoting pathways), (3) financial ressources and (4) structure in the fully funded housing construction (building size, number of buildings, number of apartments, living space, estimated cost). Depending on the purpose of the granted funding between the following cases has been distinguished: Cases funded by means of the so called first funding procedure. These are cases of the traditional publicly funded social housing.Since 1966 further cases were funded by means of the so called second funding procedure. Funding of housing units in the frame of tax-advantaged housing construction for people with higher incomes are cases belonging to the second funding procedure. Finally, from 1989 cases funded by means of the agreed funding, the so called third funding procedure. Those building projects, in which between lender, grant donor, and builder an agreement is made on amount and use of the funding, covered by the third funding procedure. According to the act on the reform of the social housing law (2001) an annual statistics on the governmental granted funding is produced. Data tables in HISTAT (Topic: Bautätigkeit, Wohnungen): A. Bewilligungen, im öffentlich geförderten sozialen Wohnungsbau (1960-1999) A.01a Übersicht: Öffentlich geförderte Wohnungen im sozialen Wohnungsbau, Früheres Bundesgebiet, Deutschland (1950-2003)A.01b Bewilligungen im öffentlich geförderten sozialen Wohnungsbau: Gebäude und Wohnungen, Früheres Bundesgebiet (1950-1999)A.01c Bewilligungen im öffentlich geförderten sozialen Wohnungsbau: Gebäude und Wohnungen, Neue Länder (1991-1999)A.01d Bewilligungen im öffentlich geförderten sozialen Wohnungsbau: Gebäude und Wohnungen, Deutschland (1991-1999)A.02a Förderungsmittel nach Art der Förderung (Förderungswege), Früheres Bundesgebiet (1960-1998)A.02b Förderungsmittel nach Art der Förderung (Förderungswegen), Neue Länder (1991-1998)A.02c Förderungsmittel nach Art der Förderung (Förderungswegen), Deutschland (1991-1998)A.03a Veranschlagte Finanzierungsmittel insgesamt nach Finanzquellen, Früheres Bundesgebiet (1960-1998)A.03b Veranschlagte Finanzierungsmittel insgesamt nach Finanzquellen, Neue Länder (1991-1998)A.03c Veranschlagte Finanzierungsmittel insgesamt nach Finanzquellen, Deutschland (1991-1998)A.04 Veranschlagte Finanzierungsmittel insgesamt nach Förderungswegen (1960-1999) B. Struktur im voll geförderten reinen Wohnungsbau (1960-1999) B.01a Wohngebäude mit 1 und 2 Wohnungen (Förderung insgesamt): Gebäudezahl, Wohnungsgröße und veranschlagte Gesamtkosten nach Kostenarten, Früheres Bundesgebiet (1962-1998)B.01b Wohngebäude mit 1 und 2 Wohnungen (Förderung in...

This data is public data that organizes the housing supply rate and housing type distribution by year in Seosan-si, Chungcheongnam-do. The items are composed of year, number of general households, total, single-family homes, multi-family homes, apartments, townhouses, multi-family homes, non-residential buildings, housing supply rate, and data base date. The year refers to the base point in time, and the number of general households refers to the total number of households in the corresponding year. Each housing type item classifies the number of houses by structure, and the housing supply rate indicates the supply level as the ratio of the number of houses to general households. This data is used for establishing housing policies, analyzing housing conditions, real estate statistics, and urban planning data, and also contributes to understanding citizens' housing environments and predicting policy demands.

--- DATASET OVERVIEW --- This dataset captures detailed information about each vacation rental property listing, providing insights that help users understand property distribution, characteristics, management styles, and guest preferences across different regions. With extensive global coverage and regular weekly updates, this dataset offers in-depth snapshots of vacation rental supply traits at scale.

The data is sourced directly from major OTA platforms using advanced data collection methodologies that ensure high accuracy and reliability. Each property listing is tracked over time, enabling users to observe changes in supply, amenity offerings, and host practices.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Geographic Information: Location data including neighborhood, city, region, and country - Listing Characteristics: Property type, bedroom count, bathroom count, in-service dates. - Amenity Inventory: Comprehensive list of available amenities, including essential facilities, luxury features, and safety equipment. - Host Information: Host details, host types, superhost status, and portfolio size - Guest Reviews: Review counts, average ratings, detailed category ratings (cleanliness, communication, etc.), and review timestamps - Property Rules: House rules, minimum stay requirements, cancellation policies, and check-in/check-out procedures

--- USE CASES --- Market Research and Competitive Analysis: VR professionals and market analysts can use this dataset to conduct detailed analyses of vacation rental supply across different markets. The data enables identification of property distribution patterns, amenity trends, pricing strategies, and host behaviors. This information provides critical insights for understanding market dynamics, competitive positioning, and emerging trends in the short-term rental sector.

Property Management Optimization: Property managers can leverage this dataset to benchmark their properties against competitors in the same geographic area. By analyzing listing characteristics, amenity offerings and guest reviews of similar properties, managers can identify optimization opportunities for their own portfolio. The dataset helps identify competitive advantages, potential service gaps, and management optimization strategies to improve property performance.

Investment Decision Support: Real estate investors focused on the vacation rental sector can utilize this dataset to identify investment opportunities in specific markets. The property-level data provides insights into high-performing property types, optimal locations, and amenity configurations that drive guest satisfaction and revenue. This information enables data-driven investment decisions based on actual market performance rather than anecdotal evidence.

Academic and Policy Research: Researchers studying the impact of short-term rentals on housing markets, urban development, and tourism trends can use this dataset to conduct quantitative analyses. The comprehensive data supports research on property distribution patterns and the relationship between short-term rentals and housing affordability in different markets.

Travel Industry Analysis: Travel industry analysts can leverage this dataset to understand accommodation trends, property traits, and supply and demand across different destinations. This information provides context for broader tourism analysis and helps identify connections between vacation rental supply and destination popularity.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: weekly | monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: weekly

Dataset Options: • Coverage: Global (most countries) • Historic Data: N/A • Future Looking Data: N/A • Point-in-Time: N/A • Aggregation and Filtering Options: • Area/Market • Time Scales (weekly, monthly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by owner)

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--- DATA QUALITY AND PROCESSING --- Our data collection and processing methodology ensures high-quality data with comprehensive coverage of the vacation rental market. Regular quality assurance processes verify data accuracy, completeness, and consistency.

The dataset undergoes continuous enhancement through advanced data enrichment techniques, including property categorization, geographic normalization, and time series alignment. This processing ensures that users receive clean, structured data ready for immediate analysis without extensive preprocess...

New Zealand’s Real Estate Services industry has experienced a sharp cycle over the past few years, shaped largely by shifts in monetary and lending policy. Record-low interest rates and relaxed lending rules in 2020 and 2021 fuelled borrowing and pushed house prices to historic highs in 2021-22, lifting commission income. Subsequently, extended bright-line rules, tighter LVR settings and rising interest rates reversed momentum. Transactions fell heavily in 2022-23 as affordability deteriorated. Since then, the market has moved through a gradual adjustment phase. Volumes improved in 2023-24 and 2024-25, but flat prices and cautious investors kept revenue under pressure. Rate cuts from late 2024 have supported a modest recovery in activity and led to profit growth, yet the lagged impact of earlier tightening means industry revenue is projected to contract at an annualised 4.6% over the five years through 2026-27 to $6.6 billion, despite an expected 1.6% rebound in 2026-27. The commercial market has faced shifting tenant preferences, particularly around remote work arrangements, which have contributed to elevated office vacancy rates. Nonetheless, booming demand for industrial space and interest in green buildings has yielded new opportunities. Concurrently, the widespread adoption of artificial intelligence has boosted operational efficiency for many real estate agencies, underpinning recent growth in their profit margins. Over the outlook period, policy changes and lower interest rates are expected to support a gradual recovery in New Zealand’s real estate services market. The return of full mortgage interest deductibility and a shorter bright-line test should improve investor returns and draw buyers back into the market. Rate cuts through 2024 and 2025 have eased borrowing costs and lifted enquiry levels. However, housing shortages and steady population growth are set to keep prices elevated, limiting affordability despite lower mortgage rates. Although planning reforms and new social housing projects will increase supply, they're unlikely to fully close the demand-supply gap in the medium term. That's why housing prices are expected to remain above pre-pandemic levels, limiting transaction growth. Overall, revenue is forecast to climb at an annualised 1.0% through the end of 2031-32 to $6.9 billion.

This dataset tracks nominal house price indices across Europe, measuring how residential property values have evolved annually from 2000 to 2025. Eurostat compiles these indices to help policymakers, investors, and researchers understand housing market trends across member states and the broader EU economy. The index provides a standardized way to compare price movements over time and across countries.

The dataset contains 2,123 rows of data covering 30 European entities, including individual countries and regional aggregates like the eurozone and EU27. Data spans a quarter-century from 2000 through 2025, with the most recent observations showing values indexed to 2025. Austria, Belgium, Bulgaria, Cyprus, and Czechia represent the leading edge of the latest reporting, while Sweden, Slovenia, Slovakia, Romania, and Portugal round out the complete coverage. This comprehensive temporal and geographic breadth enables analysis of long-term housing market cycles.

Analysts can use this data to identify regional patterns in property appreciation, compare housing affordability trends across borders, and understand how financial crises affected residential real estate differently in each country. Economists studying inflation, monetary policy transmission, and wealth inequality find house price indices essential for modeling consumption patterns and financial stability. The 25-year time horizon captures multiple economic cycles, from the pre-crisis boom through the 2008 financial crisis and subsequent recovery phases, making it valuable for stress-testing and scenario analysis in financial institutions.

BODY:

Because of the availability of data, we select Beijing, Shanghai, Tianjin, and Chongqing as the research objects. The time span is from 2005Q3 to 2018Q4. Data are obtained from the China Economic Network Statistics Database, China Economic Network Industry Database, Wind Information, and the National Bureau of Statistics. On the basis of existing studies on the real estate market in China, we select 13 economic variables as individual indicators. These include the M2 growth rate, the exchange rate, the SSE Real Estate, the inflation rate, the medium-term and long-term loan interest rates, the ratio of the completed residential investment in real estate enterprises to the completed residential investment in fixed assets, the ratio of residential property sales to the GDP, the ratio of residential area for sales to the completed residential area, the ratio of residential area under construction to the completed residential area, the residential CPI, the funds in place for real estate enterprises, the land transaction price for real estate enterprises, and the GDP growth rate. We integrate the early warning information from those individual indicators into four comprehensive indicators. The reliability of the early warning system for crises in the urban housing market is verified through the in-sample early warning results. In addition, current housing price movements in the four urban housing markets are analyzed through the out-of-sample results and the crisis prediction probability curves. Because some of the selected individual indicators have both monthly and quarterly data, some individual indicators only have monthly data, and others only have quarterly data, we use quarterly data only in order to ensure the accuracy and reliability of the data. For individual indicators with only monthly data, we adopt the price index conversion method with a fixed base. We first convert monthly chain data to monthly fixed data (with the base period being December 2005), then convert the monthly fixed data to quarterly fixed data (with the base period being 2005Q4), and finally we calculate quarterly year-on-year data. Because the absolute value of the indicator variable is relatively large, in order to facilitate comparison, we convert all indicator variables into relative values of year-on-year or comparison with other variables. We take the first three policy cycle time periods of China’s real estate market (from 2005Q3 to 2014Q2) as the in-sample time for building an early warning system for urban housing price crises in China. We use the fourth policy cycle time period (from 2014Q3 to 2018Q4) to evaluate the out-of-sample performance of the early warning system for urban housing price crises.

Garage Conversion Market Outlook

According to our latest research, the global garage conversion market size reached USD 8.6 billion in 2024, driven by robust demand for affordable housing solutions and the growing trend of maximizing underutilized spaces. The market is expanding at a notable CAGR of 7.2% and is projected to reach USD 16.2 billion by 2033. This growth is primarily propelled by urbanization, rising property values, and the increasing need for flexible living and working environments. As per the latest research, the garage conversion market continues to gain traction globally, with consumers and investors seeking innovative ways to enhance property value and functionality.

One of the primary growth factors for the garage conversion market is the surge in urban population, which has led to a scarcity of affordable housing in major metropolitan areas. As cities become more crowded and property prices soar, homeowners and property developers are turning to garage conversions as a cost-effective method to create additional living spaces without the need for new land acquisition or large-scale construction. This trend is particularly pronounced in North America and Europe, where urban density is high and regulations increasingly favor adaptive reuse of existing structures. Additionally, the rise of remote work and home-based businesses has further fueled demand for versatile spaces, making garage conversions an attractive option for creating home offices, studios, or rental units.

Another significant driver is the growing awareness of sustainability and resource efficiency in the construction industry. Garage conversions represent a sustainable approach to expanding usable space, as they typically involve repurposing existing structures rather than demolishing and rebuilding. This not only reduces construction waste but also minimizes the environmental impact associated with new builds. Furthermore, advancements in prefabricated and modular construction methods have made garage conversions quicker, more affordable, and less disruptive for homeowners. These innovations are enabling a wider range of applications, from residential to commercial, and are attracting interest from a diverse set of end-users, including property developers and real estate investors.

The evolving regulatory landscape is also shaping the growth trajectory of the garage conversion market. Many local governments are revising zoning laws and building codes to encourage the development of accessory dwelling units (ADUs) and other forms of secondary housing. These policy changes are aimed at addressing housing shortages and promoting urban densification, particularly in high-demand regions. As a result, garage conversions are becoming increasingly mainstream, with more streamlined permitting processes and greater access to financing options. This regulatory support is expected to further boost market expansion over the forecast period, making garage conversions a pivotal component of urban development strategies.

Regionally, North America remains the largest market for garage conversions, accounting for a significant share of global revenues in 2024. The United States, in particular, has seen a surge in garage conversion projects, driven by favorable legislation in states like California and Texas. Europe follows closely, with the United Kingdom and Germany emerging as key markets due to their focus on sustainable urban development and adaptive reuse. The Asia Pacific region is also witnessing rapid growth, fueled by rising urbanization and increasing disposable incomes in countries such as China and Australia. While Latin America and the Middle East & Africa currently represent smaller shares, these regions are expected to experience steady growth as awareness and investment in alternative housing solutions increase.

Type Analysis

The garage conversion market is segmented by type into attached garage conversion and detached garage conversion, each presenting distinct opportunities and challenges. Attach

People typically purchase residential properties for two reasons: to live in or invest. However, both purposes necessitate careful consideration before deciding because high financial costs are involved, and housing loans are typically considered necessary for this purpose. Customers’ demands are constantly changing, becoming more complicated with higher requirements. The focus of this research is on tourism real estate selection. This market in Vietnam is still new and emerging and has encountered numerous issues regarding government policy, finance, and land authorization for constructing, owning, and managing. Because the form of tourism real estate is still new, customers are hesitant about investing in or buying these properties. Hence, to compete in the current fiercely real estate industry, real estate firms must understand their customers’ expectations by frequently involving customer research in the company’s strategy. However, there is still a lack of research on the connection between these factors and individual expectations in the well-known philosophy of the Theory of Planned Behavior (TPB), leading to behavioral intentions. Therefore, to fulfill the gap in the previous literature, this paper aims to investigate the connection between these factors with core variables of TPB, hence, addressing the current problems in the real estate industry. 471 valid respondents in Vietnam were collected for data analysis through two survey approaches. PLS-SEM was used to test hypotheses due to the relationship complication in the conceptual models. The results show that government policy influences attitudes and perceived behavioral control, whereas social infrastructure affects social norms and perceived behavioral control. Moreover, Fengshui ambient condition also positively influences all three core factors: attitudes, social norms, and perceived behavioral control. Finally, these factors impact on intention to buy tourism real estate. Through results, this paper has developed a purchase intention model through social aspects of the tourism real estate industry. In addition, this paper demonstrates the connection between social factors and individuals’ expectations for a purchase intention, providing the importance of the government’s role, architecture style, and social infrastructure in the marketing literature of the real estate industry. As a result, managers and governments need to take advantage of new releases of government regulations in time to enhance customers’ positive attitudes toward purchasing tourism real estate. Moreover, social infrastructure and Fengshui conditions are crucial to establishing social norms and perceived control, aiming to leverage the intention to purchase tourism real estate. Thereby, recommendations of marketing strategies based on these findings were suggested to attain the optimal result for sales. Finally, this research also includes some limitations. Hence, suggestions for further research were also provided, such as possible moderation, possible mediating effects, or control of data bias.

Explore Dallas, TX rental market 2026. The average long-term prices $1,636 and short-term $2,272, with trends shaping housing in a city of 1,299,553 residents.

In this paper we study the effect of monetary policy shocks on housing rents. Our main finding is that, in contrast to house prices, housing rents increase in response to contractionary monetary policy shocks. We also find that, after a contractionary monetary policy shock, rental vacancies and the homeownership rate decline. This combination of results suggests that monetary policy may affect housing tenure decisions (own versus rent). In addition, we show that, with the exception of the shelter component, all other main components of the consumer price index (CPI) either decline in response to a contractionary monetary policy shock or are not responsive. These findings motivated us to study the statistical properties of alternative measures of inflation that exclude the shelter component. We find that measures of inflation that exclude shelter have most of the statistical properties of the widely used measures of inflation, such as the CPI and the price index for personal consumption expenditures, but have higher standard deviations and react more to monetary policy shocks. Finally, we show that the response of housing rents accounts for a large proportion of the price puzzle found in the literature.

This dataset provides a comprehensive overview of Housing Association properties in Glasgow, detailed up to the neighborhood level. It includes essential information such as the number of properties, ownership types, and geographical coordinates. Key attributes include the total number of properties and Housing Association properties per postcode, alongside the respective proportions of these properties in comparison to others in the same area. Additionally, it contains precise location data like postcodes, addresses, latitude, longitude, and grid coordinates (Easting and Northing), as well as relevant administrative divisions such as constituencies, wards, and Lower Layer Super Output Areas (LSOAs). Updated as of May 2022, this dataset is a valuable resource for understanding the distribution and characteristics of housing in Glasgow, particularly useful for urban planning, real estate analysis, and policy development.

This dataset is sourced from Glasgow Open Data, specifically the GlasgowGIS: Housing Association Properties dataset.