Housing Starts in the United States decreased to 1307 Thousand units in August from 1429 Thousand units in July of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Total Shipments of New Manufactured Homes: Total Homes in the United States (SHTSAUS) from Jan 1959 to Jul 2025 about shipments, new, housing, manufacturing, and USA.

Graph and download economic data for Housing Inventory Estimate: Total Housing Units in the United States (ETOTALUSQ176N) from Q2 2000 to Q2 2025 about inventories, housing, and USA.

View monthly updates and historical trends for US Housing Starts. from United States. Source: Census Bureau. Track economic data with YCharts analytics.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in nine separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Housing Unit Record (Main Record), Part 3, Worker Record, Part 4, Mortgages (Owners Only), Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Mover Group Record, Part 8, Recodes (One Record per Housing Unit), and Part 9, Weights. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR02912.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

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.

Our US Home Ownership Data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes various data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

This USA Housing Market Dataset (Synthetic) contains 300 rows and 10 columns of real estate-related data designed for housing price prediction, trend analysis, and investment insights. It includes key property details such as price, number of bedrooms and bathrooms, square footage, year built, garage spaces, lot size, zip code, crime rate, and school ratings.

This dataset is ideal for: ✅ Machine Learning Models for predicting housing prices ✅ Market Research & Investment Analysis ✅ Exploring Property Trends in the USA ✅ Educational Purposes for Data Science and Analytics

This dataset provides a realistic yet synthetic view of the real estate market, making it useful for data-driven decision-making in the housing industry.

Let me know if you need any modifications!

Probate, pre-probate, and divorce real estate data offers valuable insights and opportunities for real estate professionals to identify and pursue potential leads. These datasets provide information about properties involved in probate, pre-probate, and divorce cases, enabling professionals to target motivated sellers and navigate specialized market niches. In this brief, we will explore the concept of probate, pre-probate, and divorce data, and discuss their applications across various industries.

What is Probate, Pre-Probate, and Divorce Data?

Probate Data refers to the legal process of settling the estate of a deceased person. Probate data includes information about properties owned by individuals who have passed away and are being transferred to their heirs or beneficiaries through a court-supervised process. This dataset contains details about properties that may be sold to distribute the deceased person’s assets or resolve any outstanding debts.

Pre-Probate Data includes properties owned by individuals who are alive but have designated their assets to be transferred to their heirs upon their passing. This dataset allows real estate professionals to identify potential sellers who may be interested in selling their properties before going through the probate process.

Divorce Data pertains to properties involved in divorce proceedings. When couples go through a divorce, the division of assets often includes the sale or transfer of properties. This dataset provides information on properties that may become available for sale due to a divorce settlement, providing real estate professionals with opportunities to target motivated sellers.

Gain an in-depth view of probate, pre-probate and divorce characteristics for more than 155 million properties across the country (or at the state- and country-level), including: - Property Address - Owner First & Last Name - Mailing Address - Legal Description - Property Value - Property Use - Parcel ID - Year Built - Date Of Death (Probate & Pre-Probate) - Defendant Information (Divorce) - Plaintiff Information (Divorce) - Defendant Attorney Information (Divorce) - Plaintiff Attorney Information (Divorce)

This data collection provides information on the characteristics of a national sample of housing units. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who have recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of an exterminator service, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy.

This dataset provides information for Cambridgeshire, West Suffolk and Peterborough, showing the age of homes as provided by the Valuation Office Agency, based on council tax records. The first file of data is specifically focussed on identifying homes with solid walls, as part of the Warm Homes project, so the data has been grouped to highlight the small areas (known as Lower Super Output Areas) where homes fall into three specific age bands: Built before 1929 Built between 1930 to 1939 Built between 1945 and 2015 Some build dates are unknown, there are also counted in the data. Homes built before 1929 all tend to have solid walls, as cavity walls had not been invented. Homes built between 1930 and 1939 may or may not have solid walls, in the period when cavity walls were becoming more popular but not yet "the norm". Few homes were built 1939 to 1945. On the whole, homes built since 1945 tend to have cavity walls. So this data set helps us identify where we can find homes which are most likely to have solid walls, and therefore where a solid wall insulation project might want ot focus its attention. The second data file is more detailed and adds further information on the date homes were built, grouped into ten-year bands, and adds the number of homes in each LSO which falls into council tax bands A to H as well.

In the realm of real estate data solutions, BatchData Property Data Search API emerges as a technical marvel, tailored for product and engineering leadership seeking robust and scalable solutions. This purpose-built API seamlessly integrates diverse datasets, offering over 600 data points, to provide a holistic view of property characteristics, valuation, homeowner information, listing data, county assessor details, photos, and foreclosure information. With state-of-the-art infrastructure and performance features, BatchData sets the standard for efficiency, reliability, and developer satisfaction.

Unraveling the Technical Prowess of BatchData Property Data Search API:

State-of-the-Art Infrastructure: At the heart of BatchData lies a state-of-the-art infrastructure that leverages the latest technologies available. Our systems are engineered to handle increased loads and growing datasets with ease, ensuring optimal performance without significant degradation. This commitment to technological advancement ensures that our data infrastructure and API systems operate at peak efficiency, even in the face of evolving demands and complexities.

Integration Capabilities: BatchData boasts integration capabilities that are second to none, thanks to our innovative data lake house architecture. This architecture empowers us to seamlessly integrate our data with any data platforms or pipelines in a matter of minutes. Whether it's connecting with existing data systems, third-party applications, or internal pipelines, our API offers limitless integration possibilities, enabling product and engineering teams to unlock the full potential of property data with minimal effort.

Developer Documentation: One of the hallmarks of BatchData is our clear and comprehensive developer documentation, which developers love. We understand the importance of providing developers with the resources they need to integrate our API seamlessly into their projects. Our documentation offers detailed guides, code samples, API reference materials, and best practices, empowering developers to hit the ground running and leverage the full capabilities of BatchData with confidence.

Performance Features: BatchData Property Search API is engineered for performance, delivering lightning-fast response times and seamless scalability. Our API is designed to efficiently handle increased loads and growing datasets, ensuring that users experience minimal latency and maximum reliability. Whether it's retrieving property data, conducting complex queries, or accessing real-time updates, our API delivers exceptional performance, empowering product and engineering teams to build high-performance applications and systems with ease. BatchData's APIs work for both residential real estate data and commercial real estate data.

Common Use Cases for BatchData Property Data Search API:

Powering Data-Driven Applications: Product and engineering teams can leverage BatchData Property Data Search API to power data-driven applications tailored for the real estate industry. Whether it's building real estate websites, mobile applications, or internal tools, our API offers comprehensive property data that can drive informed decision-making, enhance user experiences, and streamline operations.

Enabling Advanced Analytics: With BatchData, product and engineering leaders can unlock the power of advanced analytics and reporting capabilities. Our API provides access to rich property data, enabling analysts and researchers to uncover insights, identify trends, and make data-driven recommendations with confidence. Whether it's analyzing market trends, evaluating investment opportunities, or conducting competitive analysis, BatchData empowers teams to derive actionable insights from vast property datasets.

Optimizing Data Infrastructure: BatchData Property Data Search API can play a pivotal role in optimizing data infrastructure within organizations. By seamlessly integrating our API with existing data platforms and pipelines, product and engineering teams can streamline data workflows, improve data accessibility, and enhance overall data infrastructure efficiency. Our API's integration capabilities and performance features ensure that organizations can leverage property data seamlessly across their data ecosystem, driving operational excellence and innovation.

Conclusion: BatchData Property Data Search API stands at the forefront of real estate data solutions, offering product and engineering leaders a comprehensive, scalable, and high-performance API for accessing property data. With state-of-the-art infrastructure, seamless integration capabilities, clear developer documentation, and exceptional performance features, BatchData empowers teams to build data-driven applications, optimize data infrastructure, and unlock actionable insights with ease. As the real estate industry continues to evolve, BatchData remains committed to delivering innovative sol...

This data collection provides information on the characteristics of a national sample of housing units. Data include the year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air conditioning equipment, and major additions, alterations or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Similar data are provided for housing units previously occupied by recent movers. Indicators of housing and neighborhood quality are also supplied. For housing quality, indicators include such things as privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, indicators include exterminator service, boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data are provided on the householder, including years of school completed, Spanish origin, length of residence, and length of occupancy. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09091.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in ten separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Housing Unit Record (Main Record), Part 3, Worker Record, Part 4, Mortgages (Owners Only), Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Ratio Verification, Part 8, Mover Group Record, Part 9, Recodes (One Record per Housing Unit), and Part 10, Weights. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy. (Source: ICPSR, retrieved 06/28/2011)

This CD-ROM contains software that permits users to create their own tabulations from the 1999 American Housing Survey microdata. Data files include statistics on the physical and economic characteristics of housing from the 1999 American Housing Survey. Data include year structure built, type and number of living quarters, occupancy status, number of rooms, and property value. Additional data focus on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposa l, heating and air-conditioning equipment, and major additions, alterations, or repairs made to the property. Also furnished is data relating to housing expenses including monthly mortgage or rent payments, utility costs, property insurance costs, and the amount of real estate taxes paid. Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in eight separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Worker Record, Part 3, Mortgages (Owners Only), Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Ratio Verification, and Part 8, Mover Group Record. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy.

This data collection contains data focusing on housing characteristics from 15 Standard Metropolitan Statistical Areas (SMSAs). Data include year the structure was built, type and number of living quarters, occupancy status, presence of commercial establishments on the property, presence of a garage, and property value. Additional data focus on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air conditioning equipment. Information about housing expenses includes mortgage or rent payments, utility costs, garbage collection fees, property insurance, and real estate taxes as well as repairs, additions, or alterations to the property. Similar data are provided for housing units previously occupied by respondents who had recently moved. Indicators of housing and neighborhood quality are also supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, presence of cracks or holes in walls, ceilings, or floor, reliability of plumbing and heating equipment, and concealed electrical wiring. The presence of storm doors and windows and insulation was also noted. Neighborhood information is provided on the presence of and objection to noise, traffic, odors, trash and litter, abandoned structures, rundown housing, commercial or industrial activity, and the adequacy of services, including public transportation, schools, shopping, and police and fire protection. In addition to housing characteristics, demographic data for household members are provided, including sex, age, race, income, marital status, and household relationship. Additional data are available for the household head, including Hispanic origin, length of residence, and travel-to-work information.

Snapshot img

Tiny Homes Market Size 2025-2029

The tiny homes market size is valued to increase USD 3.71 billion, at a CAGR of 4.2% from 2024 to 2029. Affordable by mass section of population will drive the tiny homes market.

Major Market Trends & Insights

North America dominated the market and accounted for a 55% growth during the forecast period.
By Product - Mobile tiny homes segment was valued at USD 9.64 billion in 2023
By Application - Home use segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 37.94 million
Market Future Opportunities: USD 3713.10 million
CAGR : 4.2%
North America: Largest market in 2023

Market Summary

The market represents a burgeoning sector in the residential real estate industry, characterized by its focus on compact, affordable living solutions. This market encompasses a range of core technologies and applications, from innovative building materials and modular construction methods to renewable energy systems and smart home automation. Service types and product categories include design and construction services, as well as the sale of prefabricated tiny homes and accessories. Despite regulatory challenges in some regions, the market continues to expand, driven by the growing trend of customization and the affordable nature of tiny homes, making them an attractive option for a mass section of the population. However, limited demand from developing economies presents a significant challenge. In the United States, for instance, the American Tiny House Association reports that the number of tiny homes registered with the organization has grown by over 50% since 2019. This underscores the evolving nature of the market and the opportunities it presents for businesses and consumers alike.

What will be the Size of the Tiny Homes Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Tiny Homes Market Segmented and what are the key trends of market segmentation?

The tiny homes industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductMobile tiny homesStationary tiny homesApplicationHome useCommercial useAreaLess Than 130 Sq. Ft.130-500 Sq. Ft.More Than 500 Sq. Ft.Less Than 130 Sq. Ft.130-500 Sq. Ft.More Than 500 Sq. Ft.Price RangeBudgetMid-rangePremiumMaterialWoodMetalRecycledGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalySpainUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Product Insights

The mobile tiny homes segment is estimated to witness significant growth during the forecast period.

The market has experienced significant expansion in recent years, with mobile tiny homes, characterized by permanently affixed chassis, witnessing substantial growth. These homes are manufactured in factories and transported to their intended sites via wheels or trucks. The affordability of mobile tiny homes makes them a popular solution in the affordable housing sector in various countries. The increasing cost of conventional houses in numerous nations is driving the demand for these compact living solutions. Young adults and retirees, seeking to save on housing expenses, are the primary consumer groups fueling the market's growth. According to recent data, the adoption of tiny homes has risen by approximately 18%, and it is projected to expand further, reaching around 25% in the upcoming five years. In terms of market trends, green building practices and energy-efficient appliances are gaining traction. Interior finishing materials, such as reclaimed wood and recycled materials, are increasingly popular. Water conservation methods, like rainwater harvesting and greywater recycling, are being integrated into tiny home designs. Site preparation techniques, like minimal excavation and foundation system designs, are being optimized for efficient construction. Structural engineering designs focus on maximizing space through innovative layouts and smart home integration. Prefabricated housing and alternative building methods, like modular construction, are streamlining the construction process. Plumbing system installations and wastewater treatment systems are being designed for off-grid living. Insulation techniques, transportation logistics, permitting and approvals, and building code compliance are all crucial aspects of the market. The durability and longevity of tiny homes are essential considerations, with sustainable building materials and hvac system optimization being key factors. Cost estimation models, downsizing and minimalism, and mobile home foundations are also significant market trends. Electrical system designs prioritize fire s

This data collection contains 132 Public Use Microdata Samples (PUMS) files from the 1970 Census of Population and Housing. Information is provided in these files on the housing unit, such as occupancy and vacancy status of house, tenure, value of property, commercial use, year structure was built, number of rooms, availability of plumbing facilities, sewage disposal, bathtub or shower, complete kitchen facilities, flush toilet, water, telephone, and air conditioning. Data are also provided on household characteristics such as the number of persons aged 18 years and younger in the household, the presence of roomers, boarders, or lodgers, the presence of other nonrelative and of relative other than wife or child of head of household, the number of persons per room, the rent paid for unit, and the number of persons with Spanish surnames. Other demographic variables provide information on age, race, marital status, place of birth, state of birth, Puerto Rican heritage, citizenship, education, occupation, employment status, size of family, farm earnings, and family income. This hierarchical data collection contains approximately 214 variables for the 15-percent sample, 227 variables for the 5-percent sample, and 117 variables for the neighborhood characteristics sample.