Graph and download economic data for Monthly Supply of New Houses in the United States (MSACSR) from Jan 1963 to Aug 2025 about supplies, new, housing, and USA.

A commonly accepted threshold for affordable housing costs at the household level is 30% of a household's income. Accordingly, a household is considered cost burdened if it pays more than 30% of its income on housing. Households paying more than 50% are considered severely cost burdened. These thresholds apply to both homeowners and renters.

The Housing Affordability indicator only measures cost burden among the region's households, and not the supply of affordable housing. The directionality of cost burden trends can be impacted by changes in both income and housing supply. If lower income households are priced out of a county or the region, it would create a downward trend in cost burden, but would not reflect a positive trend for an inclusive housing market.

The Maryland D​epartment of Housing and Community Development is proud to be at the forefront in implementing housing policy that promotes and preserves homeownership and creating innovative community development initiatives to meet the challenges of a growing Maryland.

Through the Maryland Mortgage Program, the department has empowered thousands of Maryland families to realize the American dream of homeownership and for existing homeowners.

The department’s rental housing programs increase and preserve the supply of affordable housing and provide good choices for working families, senior citizens, and individuals with special needs.

Community development and revitalization programs like Neighborhood BusinessWorks, Community Legacy, and Main Street Maryland help our cities and towns remain rich, vibrant communities.

The Maryland Department of Housing and Community Development remains committed to building on our past successes to maintain our reputation as an innovator in community revitalization and a national leader in housing finance.

DISCLAIMER: Some of the information may be tied to the Department’s bond funded loan programs and should not be relied upon in making an investment decision. The Department provides comprehensive quarterly and annual financial information and operating data regarding its bonds and bond funded loan programs, all of which is posted on the publicly-accessible Electronic Municipal Market Access system website (commonly known as EMMA) that is maintained by the Municipal Securities Rulemaking Board, and on the Department’s website under Investor Information.

More information accessible here: http://dhcd.maryland.gov/Investors/Pages/default.aspx

Total Housing Inventory in the United States decreased to 1520 Thousands in October from 1530 Thousands in September of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.

The growth of the online short-term rental market, facilitated by platforms such as Airbnb, has added to pressure on cities’ housing supply. Without detailed data on activity levels, it is difficult to design and evaluate appropriate policy interventions. Up until now, the data sources and methods used to derive activity measures have not provided the detail and rigour needed to robustly carry out these tasks. This paper demonstrates an approach based on daily scrapes of the calendars of Airbnb listings. We provide a systematic interpretation of types of calendar activity derived from these scrapes and define a set of indicators of listing activity levels. We exploit a unique period in short-term rental markets during the UK’s first COVID-19 lockdown to demonstrate the value of this approach.

View monthly updates and historical trends for US Existing Home Months' Supply. from United States. Source: National Association of Realtors. Track econom…

Graph and download economic data for Housing Inventory: Active Listing Count in the United States (ACTLISCOUUS) from Jul 2016 to Oct 2025 about active listing, listing, and USA.

Australian Property Market Insights Dataset: Historical affordability metrics, cash rate impact on lending, supply vs demand analysis, property metrics, first home buyer trends, and lending risk assessment. Official data from ABS, APRA, and RBA (2014-present).

In 2024, the existing home sales index in Japan stood at ***** index points, reaching a decade high. The index for used home sales measures the development of the second-hand housing market based on the number of ownership transfers due to the sale and purchase of buildings. It includes data for detached houses and condominiums. Second-hand housing market in Japan Japan’s second-hand home market only accounts for a small share of the overall housing market. Despite the country’s massive housing stock, a large quantity of new homes is built every year as Japanese consumers prefer new homes over used ones. This is probably rooted in the housing policies of the post-war period, which were aimed at the rapid supply of new housing units at the cost of quality. As a result, many older homes are poor quality, and new homes quickly depreciate. These circumstances have created uncertainty about used homes and are reflected by the scrap and build approach of completely destroying and rebuilding used homes instead of reusing and renovating them. Revitalizing the existing home market In the past years, however, the government has shifted its focus to revitalizing the used housing market and utilizing the massive existing housing stock that comprises around ** million units. By implementing a reliable home inspection system, subsidizing renovations, and offering appropriate pricing models, it is trying to change people’s perception of used homes. Driven by rising prices for new homes, demand for second-hand homes, especially condominiums, has recently increased in the metropolises of Tokyo and Osaka.

This is statistical yearbook-based data showing the housing supply rate (total housing units/number of households) in Chuncheon-si, Gangwon-do. The main items include year, number of households, total housing units, number of single-family homes, number of multi-family homes, number of commercial and commercial uses, number of apartments, number of townhouses, number of multi-family homes, number of homes in non-residential buildings, housing supply rate, and data base date. This data can be used to quantitatively understand the housing supply situation by year in Chuncheon-si and analyze the balance between housing supply and demand, and can be used as important reference material for establishing urban housing policies, preparing housing market stabilization measures, and establishing housing infrastructure plans according to population changes. In addition, the data can be individually checked on the National Statistical Portal (KOSIS) of Statistics Korea as population and housing census data.

The present study deals with a special part of sectorial planning: provision of housing. The provision of housing in the Federal Republic of Germany (BRD) is divided in three different areas. Those areas are: Construction and housing industry, the social structure of the inhabitants and the physical structure of housing and housing estates. Governmental intervention measures mainly address those three areas: they try to regulate the housing provision and the rental prices through financial subsidies, the social distribution of housing through definition of target groups and the housing standards through urban planning and technical guidelines. Therefor the scientific investigation of housing provision needs to be about economic, sociological and urban and architectural aspects and needs to relate those aspects. The study of Kerstin Dornhöfer uses an integrated approach of the investigation of housing provision looking at those three aspects. The objective of the study is to develop criteria for the evaluation, planning and implementation of measures for housing provision. “The state controlled housing provision has its origin in the historical development before the Second World War. Besides the material basis of housing provision in the BRD also knowledge about and experiences with comprehensive steering instruments and its effectiveness resulted from the historical development of housing supply and its state controlled steering. This raises the question to what extent this knowledge and experiences had an impact on governmental policies concerning housing provision in the BRD. The description and analysis of the investigation is based on the following guiding questions:- Which steering instruments the BRD uses to achieve higher effectiveness concerning the socio-political postulate of improving the housing circumstances for the broad masses of people?- Could the dependence of housing provision and is governmental steering on the development of the total capital and on landed property , construction and housing construction capital be eliminated or at least gradually controlled?- What was the impact of governmental steering in the BRD?- How did it come to the current discrepancies in spite of all reform efforts and directing interventions?- What conditions were problematic for the improvement of housing circumstances for the broad masses of people? What are the relevant determinants for housing provision? The first part of this study deals with the description of housing provision for broad masses of people since the foundation of the BRD. This time is divided into four periods; each period begins with an important change in laws that indicated a change in in the governmental steering and transformations of economic and social circumstances. The description of the different periods helps to see the governmental steering instruments and its effectiveness regarding the historical circumstances. In the second part of the study the governmental objectives and steering instruments will be questioned and the circumstances of implementation will be identified based in three criteria. Those criteria are: (1) Housing standards and housing quality; (2) rental price (income-rent ratio); (3) Social distribution (broad masses of people as the target group of governmental steering). The question behind this is; if the thesis, which resulted from the historical development of housing provision before the Second World War, that governmental steering only takes place when the economic circumstances require and allow the public intervention and when public pressure forces governmental intervention, is also valid for the BRD.” (Dorhöfer, K., a. a. O., S. 11-13). Data tables in HISTAT:A. Federal Republic of Germany A.01 Development of population, housing stock and occupation density, BRD and West-Berlin (1950-1975)A.02 Ratio of housing stock and private households by size (1950-1974)A.03 Housing completions in the Federal Republic of Germany (1950-1975)A.04 Financing of housing construction in the Federal Republic of Germany, in percent (1950-1975)A.05 Building owners of housing in the Federal Republic of Germany, in percent (1950-1975)A.06 Price indices for residential buildings, cost of living, land without buildings and rents (1950-1975)A.07 Average monthly expenditures per four person worker-household with average income (1950-1975)A.08 Total cost of an apartment in social housing and average land prices in DM (1950-1975)A.09 Average living area, number of rooms per apartment, equipped with central heating system and bathroom in the BRD (1952-1975)A.10 Proportion of apartments per number of rooms per apartment in the Federal Republic of Germany (1952-1973)A.11 Construction activity of non-profit housing companies (1951-1975)A.12 Number of non-profit housing companies and number of members of housing cooperatives (1950-1975)A.13 Housing stock of the nonprofit housing companies and monthly rent (1951-1975) B. West- Berl...

This report provides information on studio apartments and officetels in Nam-gu, Gwangju Metropolitan City (including address, date of occupancy approval, primary and secondary use, and number of households). This data is provided by the Architecture Division of Nam-gu Office, Gwangju Metropolitan City (☎062-607-4113). Please note that data where household count information is not available is omitted. Public data may fluctuate depending on the reference point, so please contact the relevant department for further information. ※ This studio apartment and officetel data is used to understand the distribution of small residential properties in the region, market prices for sale, lease, and monthly rent, and supply trends. Studio apartments and officetels are becoming a popular housing option for young people, newlyweds, and working people in high-demand areas such as university areas, industrial complexes, and subway stations. This data allows local governments to develop housing welfare policies, rent stabilization measures, and housing supply plans, while citizens can make informed housing choices and compare costs based on this data. It can also be used for real estate market analysis, urban planning, and lifestyle SOC policies.

Overview

This residential real estate data set was created by Redfin, an online real estate brokerage. Published on January 9th, 2022, this data summarize the monthly housing market for every State, Metro, and Zip code in the US from 2012 to 2021. Redfin aggregated this data across multiple listing services and has been gracious enough to include property type in their reporting. Please properly cite and link to RedFin if you end up using this data for your research or project.

Source: RedFin Data Center

Property Type

Property type defined by RedFin

• All Residential: All properties defined as single-family, condominium, co-operative, townhouses, and multi-family (2-4 units) homes with a county record.
• Single Family Home (SFH): are homes built on a single lot, with no shared walls. Sometimes there’s a garage, attached or detached.
• Condominium (Condo): Usually a single unit within a larger building or community. Generally come with homeowners’ associations (HOAs), which require the residents to pay monthly or yearly dues.
• Cooperatives (Co-op): Usually a single unit within a larger building or community, but with a different way of holding a title to a shared building. You join a community and everyone in the community owns the building together.
• Townhouse: a hybrid between a condo and a single-family home. They are often multiple floors, with one or two shared walls, and some have a small yard space or rooftop deck. They’re generally larger than a condo, but smaller than a single-family home.
• Multifamily (2-4 units): They are essentially a home that has been turned into two or more units but the units cannot be purchased individually. There is one owner for the whole building.
• Land: Just land, no home of any type for sale.

Source: Building Types

Property Type

For more definitions, please visit RedFin Data Center Metrics

• Average sale to list: The mean ratio of each home's sale price divided by their list price covering all homes with a sale date during a given time period. Excludes properties with a sale price of 50%.
• Home sales: Total number of homes with a sale date during a given time period.
• Inventory: Total number of active listings on the last day of a given time period.
• Median active list ppsf: The median list price per square foot of all active listings.
• Median active list price: The median list price of all active listings.
• Median active listings: The median of how many listings were active on each day within a given time period.
• Median days on market: The number of days between the date the home was listed for sale and when the home went off-market/pending sale covering all homes with an off-market date during a given time period where 50% of the off-market homes sat longer on the market and 50% went off the market faster. Excludes homes that sat on the market for more than 1 year.
• Median days to close: The median number of days a home takes to go from pending to sold.
• Median list price: The most recent listing price covering all homes with a listing date during a given time period where 50% of the active listings were above this price and 50% were below this price.
• Median list price per square foot: The most recent listing price divided by the total square feet of the property (not the lot) covering all homes with a listing date during a given time period where 50% of the active listings were above this price per sqft and 50% were below this price per sqft.
• Median listing with price drops: The median of how many listings were active on each day and whose current list price is less than the original list price within a given time period.
• Median sale price: The final home sale price covering all homes with a sale date during a given time period where 50% of the sales were above this price and 50% were below this price.
• Median sale price per square foot: The final home sale price divided by the total square feet of the property (not the lot) covering all homes with a sale date during a given time period where 50% of the sales were above this price per sqft and 50% were below this price per sqft.
• Months of supply: When data are monthly, it is inventory divided by home sales. This tells you how long it would take supply to be bought up if no new homes came on the market.
• New listings: Total number of homes with a listing added date during a given time period.
• Off market in two weeks: The total number of homes that went under contract within two weeks of their listing date.
• Pending home sales: Total homes that went under contract during the period. Excludes homes that were on the market longer than 90 ...

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Oct 2025 about median and USA.

The data in this dataset is collected from FRED.

I decided to create this dataset while reading the research paper Factors Affecting House Prices in Cyprus: 1988-2008 by Panos Pashardes & Christos S. Savva. This research paper is extremely informative and covers a lot of details regarding the macroeconomics involved in real estate market. So I would recommend you all to go through it once.

NOTE:

This dataset will be updated over a period of time and include the following: - Macroeconomic factors with quarterly, monthly frequencies. - Microeconomic factors such as house type, age, location, size (BR, BA, carpet area/built-up area), facilities, view, disability functions, region, house prices, etc.

NOTE 2:

I recommend you all to check the file in this dataset with the title Housing_Macroeconomic_Factors_US (2).csv, it includes both the supply and demand factors associated with the housing market.

General Defintions:

1. Macroeconomic Factors

• House_Price_Index: House price change according to the index base period set (you can check the date at which this value is 100).
• Stock_Price_Index: Stock price change according to the index base period set (you can check the date at which this value is 100).
• Consumer_Price_Index: The Consumer Price Index measures the overall change in consumer prices based on a representative basket of goods and services over time.
• Population: Population of USA (unit: thousands).
• Unemployment_Rate: Unemployment rate of USA (unit: percentage).
• Real_GDP: GDP with adjusted inflation (Annual version unit: billions of chain 2012 dollars in, Monthly version unit: Annualised change).
• Mortgage_Rate: Interest charged on mortgages (unit: percentage).
• Real_Disposable_Income (Real Disposable Personal Income): Money left from salary after all the taxes are paid (unit: billions of chain 2012 dollars).
• Inflation: Decline in purchasing power over time (unit: percentage). [Forgot to remove this column in Annual version since CPI is one of the measures used to determine inflation].

What can you do with this dataset?

• Perform statistical analysis, find significant features & find the value by which these features affect the house price index (recommend to use a percentage change instead of index).
• Perform multivariate regression and predict the price of houses using microeconomic features (soon).

Thanks! If you like this dataset, I'll appreciate it if you give this dataset a vote! Discussions, suggestions & doubts are always welcome. Happy Learning!!

Housing starts in Canada represent the total number of new residential dwelling units that began construction during the year, encompassing single-detached homes, semi-detached units, row houses and apartment buildings. This metric serves as a leading indicator of residential construction activity, housing supply dynamics and broader economic confidence. Data is compiled monthly by Canada Mortgage and Housing Corporation through building permit surveys and construction site inspections across all provinces and territories.

The growth of the online short-term rental market, facilitated by platforms such as Airbnb, has added to pressure on cities’ housing supply. Without detailed data on activity levels, it is difficult to design and evaluate appropriate policy interventions. Up until now, the data sources and methods used to derive activity measures have not provided the detail and rigour needed to robustly carry out these tasks. This paper demonstrates an approach based on daily scrapes of the calendars of Airbnb listings. We provide a systematic interpretation of types of calendar activity derived from these scrapes and define a set of indicators of listing activity levels. We exploit a unique period in short-term rental markets during the UK’s first COVID-19 lockdown to demonstrate the value of this approach.

Building contractors and developers depend on various socio-economic factors, including property values, underlying sentiment in the housing market, the degree of optimism among downstream businesses and credit conditions. All of these drivers typically track in line with economic sentiment, with recent economic shocks spurring a difficult period for building contractors and developers. Nonetheless, the enduring need for building services, particularly to tackle housing shortages across the continent, ensures a strong foundation of work. Revenue is forecast to grow at a compound annual rate of 2.3% to reach €1.3 trillion over the five years through 2025. Operational and supply chain disruption caused by the pandemic reversed the fortunes of building contractors and developers in 2020, as on-site activity tumbled and downstream clients either cancelled, froze or scaled back investment plans. Aided by the release of pent-up demand and supportive government policy, building construction output rebounded in 2021. Excess demand for key raw materials led to extended lead times during this period, while input costs recorded a further surge as a result of the effects of rapidly climbing energy prices following Russia’s invasion of Ukraine. Soaring construction costs and the impact of interest rate hikes on both the housing market and investor sentiment led to a renewed slowdown in building construction activity across the continent. However, falling inflation and the start of an interest rate cutting cycle have spurred signs of a recovery in new work volumes, supporting anticipated revenue growth of 2.3% in 2025. Revenue is forecast to increase at a compound annual rate of 6.7% to €1.7 trillion over the five years through 2030. Activity is set to remain sluggish in the medium term, as weak economic growth and uncertainty surrounding the impact of the volatile global tariff environment on inflation and borrowing costs continue to weigh on investor sentiment. Contractors and developers will increasingly rely on public sector support, including measures to boost the supply of new housing, as countries seek to tackle severe housing shortages. Meanwhile, the introduction of more stringent sustainability requirements will drive demand for energy retrofits.

--- DATASET OVERVIEW --- This dataset captures detailed information about each vacation rental property listing across multiple OTAs. This report provides performance metrics and ranking insights that help users benchmark their rental properties and key in on performance drivers across all global vacation markets Key Data has to offer.

--- 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 - Historic Performance Metrics: Revenue, ADR, guest occupancy and more over the last 12 months. - Forward Looking Performance Metrics: Revenue, ADR, guest occupancy and more over the next 6 months. - Performance Tiering and Percentile Ranking amongst peer listings within the specified performance ranking groups. --How Listings Are Grouped: Listing Source (e.g., Airbnb vs. Vrbo) Market (identified by uuid) - Market type = vacation areas Property Type (house, apartment, unique stays, etc.) Number of Bedrooms (0, 1, 2, 3, 4, 5, 6, 7, 8+)

--- 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: 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: monthly

Dataset Options: • Coverage: Global (most countries) • Historic Data: Last 12 months performance • Future Looking Data: Next 6 months performance • Point-in-Time: N/A

Contact us to learn about all options.

--- 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 preprocessing requirements.

A PLH defines habitat policy in an intercommunal territory. It aims to meet housing needs (social or non-social, rental or not), to promote social diversity by ensuring a balanced and diversified distribution of housing supply within an inter-communal territory and indicates the means to achieve this. Its specific procedure implies that it is carried only by an EPCI with PLH competence in association with the State. A local habitat program typically lasts six years. The Molle Act of 29 March 2009 modifies the scope of the PLH by making it much more operational and obliges the EPCI to detail the objectives to the municipality.The programme commits the State, the municipalities, the intercommunities and, where appropriate, the general councils responsible for stone aid. The measures shall be implemented by the operators: social housing organisations, private professional operators, owner-occupiers or lenders.These data contain only the PLHs being developed or in force, the former PLHs (i.e. those completed) being archived in a cancelled state.