The cities expected by industry experts to have the highest investor demands in the United States in 2023 were chosen due to their sustained population and job growth, attraction to educated millennials, high levels of economic diversity, and white-collar employment among others. Austin, Nashville, and Dallas Fortworth ranked highest among the top 15 cities with the highest projected investor demand in real estate in the United States for 2023.

The United States home construction market, valued at approximately $700 billion in 2025, is experiencing robust growth, projected to maintain a compound annual growth rate (CAGR) exceeding 3% through 2033. This expansion is fueled by several key factors. Firstly, a persistent housing shortage, particularly in desirable urban areas like New York City, Los Angeles, and San Francisco, continues to drive demand. Secondly, favorable demographic trends, including millennial household formation and an increasing preference for homeownership, are bolstering the sector. Furthermore, low interest rates (though this is subject to change depending on economic conditions) have historically made mortgages more accessible, stimulating construction activity. However, the market isn't without its challenges. Rising material costs, labor shortages, and supply chain disruptions continue to exert upward pressure on construction prices, potentially impacting affordability and slowing growth in certain segments. The market is segmented by dwelling type (apartments & condominiums, villas, other), construction type (new construction, renovation), and geographic location, with significant activity concentrated in major metropolitan areas. The dominance of large national builders like D.R. Horton, Lennar Corp, and PulteGroup highlights the industry's consolidation trend, while the growth of multi-family construction reflects shifting urban preferences. Looking ahead, the market's trajectory will depend on macroeconomic factors, interest rate fluctuations, government policies impacting housing affordability, and the ability of the industry to address supply-chain and labor challenges. Innovation in construction technologies, sustainable building practices, and prefabricated homes are also emerging trends expected to significantly influence market dynamics over the forecast period. The competitive landscape is characterized by a mix of large publicly traded companies and smaller regional builders. While established players dominate the market share, opportunities exist for smaller firms specializing in niche markets, such as sustainable or luxury home construction, or those focused on specific geographic areas. The ongoing expansion of the market signifies significant potential for investment and growth, despite the hurdles currently impacting the sector. Addressing supply chain disruptions and labor shortages will be crucial for sustained growth. Continued demand in key urban centers and evolving consumer preferences toward specific dwelling types will be critical factors determining the market's future trajectory. Recent developments include: June 2022 - Pulte Homes - a national brand of PulteGroup, Inc. - announced the opening of its newest Boston-area community, Woodland Hill. Offering 46 new construction single-family homes in the charming town of Grafton, the community is conveniently located near schools, dining, and entertainment, with the Massachusetts Bay Transportation Authority commuter rail less than a mile away. The collection of home designs at Woodland Hill includes three two-story floor plans, ranging in size from 3,013 to 4,019 sq. ft. with four to six bedrooms, 2.5-3.5 baths, and 2-3 car garages. These spacious home designs feature flexible living spaces, plenty of natural light, gas fireplaces, and the signature Pulte Planning Center®, a unique multi-use workstation perfect for homework or a family office., December 2022 - D.R. Horton, Inc. announced the acquisition of Riggins Custom Homes, one of the largest builders in Northwest Arkansas. The homebuilding assets of Riggins Custom Homes and related entities (Riggins) acquired include approximately 3,000 lots, 170 homes in inventory, and 173 homes in the sales order backlog. For the trailing twelve months ended November 30, 2022, Riggins closed 153 homes (USD 48 million in revenue) with an average home size of approximately 1,925 square feet and an average sales price of USD 313,600. D.R. Horton expects to pay approximately USD 107 million in cash for the purchase, and the Company plans to combine the Riggins operations with the current D.R. Horton platform in Northwest Arkansas.. Notable trends are: High-interest Rates are Negatively Impacting the Market.

In a 2019 analysis, Riverside, California was the most at risk of a housing downturn in a recession out of the ** largest metro areas in the United States. The Californian metro area received an overall score of **** percent, which was compiled after factors such as home price volatility and average home loan-to-value ratio were examined.

Los Angeles, San Francisco, and San Jose had the highest share of home listings priced over *********** U.S. dollars among the biggest cities in the United States in 2023. However, considering that the median home price in these cities ranges from *** million to *** thousand U.S. dollars, housing around *********** dollars would not be considered luxurious but that of higher prices. Los Angeles city had the largest share of luxury housing above ************ U.S. dollars of **** percent, followed by New York City with *** percent.

The Mexican residential real estate market, valued at $14.51 billion in 2025, exhibits a promising growth trajectory with a Compound Annual Growth Rate (CAGR) of 4.14% projected from 2025 to 2033. This robust expansion is fueled by several key drivers. A growing middle class with increasing disposable income is a significant factor, alongside government initiatives promoting affordable housing and infrastructure development. Urbanization continues to drive demand, particularly in major metropolitan areas like Mexico City, Guadalajara, and Monterrey. Furthermore, the tourism sector's influence on secondary housing markets in coastal and resort regions contributes significantly to the overall market dynamism. However, challenges exist; fluctuations in the Mexican Peso against the US dollar can affect investment sentiment, and interest rate changes impact mortgage accessibility. Regulatory hurdles and bureaucratic processes related to land ownership and construction permits sometimes impede development. The market is segmented by property type, with apartments and condominiums likely holding the largest share, followed by landed houses and villas, reflecting diverse consumer preferences and housing needs. Competition is intense, with a mix of both large national developers like Grupo Lar and Grupo Sordo Madaleno, alongside smaller regional players vying for market share. The market's future success depends on navigating these challenges effectively while capitalizing on the underlying growth opportunities. The projected market expansion will likely see a more pronounced increase in higher-value segments (landed houses and villas) as rising incomes fuel demand for luxury properties. Geographical variations are expected; while urban centers will experience sustained growth, resort areas might see more volatile fluctuations influenced by tourism trends. The market's resilience will be tested by its ability to adapt to potential economic shifts and effectively address regulatory constraints. Continuous investment in infrastructure and supportive government policies will be pivotal in fostering sustainable and inclusive growth across all market segments within the forecast period. The presence of both large and small players ensures a competitive landscape, promoting innovation and diversification within the industry. Recent developments include: June 2023: Habi, a prominent real estate technology platform, is set to receive a substantial financial boost of USD 15 million from IDB Invest. This funding, spread over four years, aims to fuel Habi's expansion plans in Mexico. While the structured loan has the potential to reach USD 50 million, its primary focus is to cater to Habi's working capital needs. IDB Invest's strategic move is not just about bolstering Habi's growth; it also aims to leverage technology to enhance liquidity and agility in Mexico's secondary real estate markets. By addressing the housing gap in Mexico, this funding initiative is poised to elevate market efficiency, bolster transparency, encourage local contractors for home renovations, and expand Habi's corridor network., June 2023: Celaya Tequila, a premium tequila brand crafted in small batches and co-founded by brothers Matt & Ryan Kalil, is forging a philanthropic alliance with New Story, a non-profit dedicated to eradicating global homelessness. In a groundbreaking move, Celaya Tequila pledges to contribute a percentage of sales from every bottle towards an affordable housing endeavor in Jalisco, Mexico. This endeavor aims to empower underprivileged families in Jalisco by enhancing their access to homes and land ownership.. Key drivers for this market are: 4., Increasing Residential Real Estate Demand by Young People4.; Increase in Average Housing Price in Mexico. Potential restraints include: 4., Increasing Residential Real Estate Demand by Young People4.; Increase in Average Housing Price in Mexico. Notable trends are: Demand for Residential Real Estate Witnessing Notable Surge, Primarily Driven by Young Homebuyers.

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

Among the ** largest cities by population in the United States, Milwaukee, WI, had the largest homes in 2023. The average size of a home was over ***** square feet, while in Portland, OR, the average home was 1070 square feet. Since 1975, U.S. homes have grown substantially bigger.

Build-to-Rent Housing Market Outlook

As per our latest research, the global Build-to-Rent (BTR) housing market size reached USD 74.3 billion in 2024, reflecting a robust expansion driven by rising demand for professionally managed rental communities. The market is projected to grow at a CAGR of 10.1% from 2025 to 2033, reaching an estimated USD 192.2 billion by 2033. This impressive growth trajectory is primarily fueled by evolving lifestyle preferences, increasing urbanization, and a shift in housing affordability, which are collectively redefining the residential real estate landscape worldwide.

One of the most significant growth factors for the Build-to-Rent housing market is the changing demographic profile of urban populations. Young professionals and millennials increasingly prioritize flexibility and convenience over homeownership, leading to a surge in demand for rental properties that offer modern amenities and community-centric living. The BTR model, with its professionally managed services, maintenance support, and enhanced communal facilities, appeals strongly to this demographic. Additionally, the growing number of digital nomads and remote workers is further amplifying the need for adaptable, high-quality rental housing, particularly in metropolitan areas and emerging urban centers.

Another major driver for the Build-to-Rent housing market is the ongoing affordability crisis in many global cities. Escalating property prices and stringent mortgage requirements have made homeownership unattainable for a significant portion of the population, especially in North America and Europe. As a result, institutional investors and real estate developers are capitalizing on this opportunity by expanding their BTR portfolios. The stable, long-term rental income streams offered by BTR assets are particularly attractive to pension funds, insurance companies, and private equity firms seeking diversification and resilience in their investment portfolios.

Technological advancements and innovation in construction methods are also catalyzing the growth of the Build-to-Rent housing market. The adoption of modular and prefabricated construction techniques is enabling developers to accelerate project timelines, reduce costs, and improve sustainability outcomes. These methods are particularly suited to the BTR model, where speed to market and operational efficiency are critical. Furthermore, the integration of smart home technologies and digital management platforms is enhancing tenant experiences and operational transparency, thereby increasing the appeal of BTR properties to both residents and investors.

Regionally, North America and Europe continue to dominate the Build-to-Rent housing market, accounting for a combined market share of over 65% in 2024. However, Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, rising middle-class populations, and supportive government policies. Latin America and the Middle East & Africa are also witnessing growing interest in the BTR model, particularly in gateway cities with expanding expatriate communities and young workforces. The regional outlook for the BTR market remains highly positive, underpinned by favorable demographic trends and increasing investor appetite for income-generating real estate assets.

Property Type Analysis

The Build-to-Rent housing market is segmented by property type into single-family homes, multi-family apartments, townhouses, and others. Among these, multi-family apartments currently hold the largest market share, accounting for over 55% of the global BTR inventory in 2024. The preference for multi-family developments is rooted in their efficient land use, scalability, and ability to offer a wide array of amenities such as gyms, co-working spaces, and communal lounges. These features are highly attractive to young professionals and urban dwellers seeking community engagement and convenience. Furthermore, mul

The 2015 American Housing Survey marks the first release of a newly integrated national sample and independent metropolitan area samples. The 2015 release features many variable name revisions, as well as the integration of an AHS Codebook Interactive Tool available on the U.S. Census Bureau Web site. This data collection provides information on representative samples of each of the 15 largest metropolitan areas across the United States, which are also included in the integrated national sample (available as ICPSR 36801). The metropolitan area sample also features representative samples of 10 additional metropolitan areas that are not present in the national sample. The U.S. Department of Housing and Urban Development (HUD) and the U.S. Census Bureau intend to survey the 15 largest metropolitan areas once every 2 years. To ensure the sample was representative of all housing units within each metro area, the U.S. Census Bureau stratified all housing units into one of the following categories: (1) A HUD-assisted unit (as of 2013); (2) Trailer or mobile home; (3) Owner-occupied and one unit in structure; (4) Owner-occupied and two or more units in structure; (5) Renter-occupied and one unit in structure; (6) Renter-occupied and two or more units in structure; (7) Vacant and one unit in structure; (8) Vacant and two or more units in structure; and (9) Other units, such as houseboats and recreational vehicles. The data are presented in three separate parts: Part 1, Household Record (Main Record); Part 2, Person Record; and Part 3, Project Record. Household Record data includes questions about household occupancy and tenure, household exterior and interior structural features, household equipment and appliances, housing problems, housing costs, home improvement, neighborhood features, recent moving information, income, and basic demographic information. The Household Record data also features four rotating topical modules: Arts and Culture, Food Security, Housing Counseling, and Healthy Homes. Person Record data includes questions about personal disabilities, income, and basic demographic information. Finally, Project Record data includes questions about home improvement projects. Specific questions were asked about the types of projects, costs, funding sources, and year of completion.

Tiny House Market Outlook

According to our latest research, the tiny house market size reached USD 6.1 billion globally in 2024, demonstrating steady growth fueled by shifting consumer preferences and housing affordability challenges. The market is expected to expand at a robust CAGR of 6.9% from 2025 to 2033, reaching a forecasted value of approximately USD 11.5 billion by 2033. The primary growth driver is the increasing demand for affordable, sustainable, and flexible living solutions, especially among younger demographics and environmentally conscious consumers.

The growth trajectory of the tiny house market is significantly influenced by the rising cost of traditional housing and urbanization trends. As metropolitan areas become denser and real estate prices soar, consumers are increasingly seeking alternative housing options that offer both affordability and flexibility. Tiny houses, with their compact footprints and lower construction and maintenance costs, provide a compelling solution for individuals and families looking to achieve homeownership without the financial burden of conventional homes. Additionally, the rising interest in minimalist lifestyles and the desire to reduce personal carbon footprints have made tiny homes an attractive choice for those prioritizing sustainability.

Another key factor propelling the tiny house market is the increasing prevalence of remote work and digital nomadism. The shift towards flexible work arrangements, accelerated by global events such as the COVID-19 pandemic, has prompted many individuals to reconsider their housing needs. Tiny homes, especially mobile variants, enable a lifestyle that is not tied to a single location, allowing owners to travel or relocate as needed. This flexibility aligns well with the preferences of millennials and Gen Z consumers, who value experiences over material possessions and are more likely to embrace non-traditional living arrangements. Furthermore, advancements in off-grid technologies, such as solar panels and composting toilets, have enhanced the viability of tiny homes in remote or rural areas.

Government initiatives and regulatory reforms are also playing a pivotal role in shaping the tiny house market. In several regions, local authorities are amending zoning laws and building codes to accommodate tiny house developments, recognizing their potential to address affordable housing shortages and promote sustainable urban growth. These regulatory changes are encouraging both individual buyers and developers to invest in tiny house communities, further expanding the market. However, challenges remain in areas where regulations are less favorable, highlighting the importance of continued advocacy and policy innovation to unlock the full potential of the tiny house movement.

From a regional perspective, North America continues to dominate the tiny house market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The popularity of tiny homes in the United States and Canada can be attributed to high housing costs, a strong DIY culture, and widespread media coverage. In Europe, growing environmental awareness and government incentives for sustainable housing are driving adoption, while in Asia Pacific, rapid urbanization and the need for space-efficient solutions are fueling market growth. Emerging markets in Latin America and the Middle East & Africa are also beginning to show interest, particularly in the context of affordable housing initiatives and tourism-related applications.

Product Type Analysis

The tiny house market is segmented by product type into mobile tiny houses and stationary tiny houses, each catering to distinct consumer preferences and lifestyle needs. Mobile tiny houses, often built on trailers, offer unparalleled flexibility and mobility, making them especially popular among digital nomads, retirees, and adventure seekers. Their ability to be relocated with ease appeals to those who value freedom of movement and the opportunity to

A Lower Density Growth Management Area is an area designated in the Zoning Resolution where new developments must provide more off-street parking, larger yards and more open space than would otherwise be required in the applicable zoning districts In Staten Island and Bronx Community District 10. All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

The HOME Investment Partnerships Program (HOME) provides formula grants to states and localities that communities use - often in partnership with local nonprofit groups - to fund a wide range of activities including building, buying, and/or rehabilitating affordable housing for rent or homeownership or providing direct rental assistance to low-income people. HOME is the largest federal block grant to state and local governments designed exclusively to create affordable housing for low-income households.Authorized under Title II of the Cranston-Gonzalez National Affordable Housing Act, the HOME Investment Partnership Program (HOME) is designed exclusively to create affordable housing for low-income households. Each year the HOME Program allocates approximately $2 billion to fund the development, purchase, or rehabilitation of affordable housing, and to provide direct rental assistance. To learn more about the HOME program visit: https://www.hud.gov/program_offices/comm_planning/home, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HOME Grantee Areas

Date of Coverage: Q1 FY 2025

1. Biodiversity conservation is a primary function of protected areas. However, protected areas also attract people, and therefore, land use has intensified at the boundaries of these lands globally. In the USA, since the 1970s, housing growth at the boundaries (<1 km) of protected areas has increased at a rate far higher than on more distant private lands. Here, we designed our analyses to address our central hypothesis that increasing housing density in and near protected areas will increasingly alter their avian communities. 2. We quantified the relationship between abundance and richness of protected-area avian species of greatest conservation need, land-cover affiliates (e.g. species associated with natural land cover such as forest breeders) and synanthropes (e.g. species associated with humans) with housing density on the boundary of protected areas and on more distant private lands from 1970 to 2010 in three ecoregions of the USA. We accomplished this using linear mixed-model analyses, data from the US Census Bureau and 90 routes of the North American Breeding Bird Survey. 3. Housing density at the boundary of protected areas tended to be strongly negatively related with the abundance and richness of species of greatest conservation need and land-cover affiliates (upwards of 88% of variance explained) and strongly positively related with synanthropes (upwards of 83% of variance explained). The effect size of these relationships increased in most cases from 1970 to 2010 and was greatest in the densely developed eastern forests. In the more sparsely populated West, we found similar, though weaker, associations. 4. Housing density on private lands more distant from protected areas had similar, but more muted negative effects. 5. Synthesis and applications. Our results illustrate that as housing density has increased along the boundary of protected areas, the conservation benefit of these lands has likely diminished. We urge conservation planners to prioritize the purchase of private-land inholdings in order to maximize the extent of unfragmented natural lands within protected areas. Further, we strongly recommend that land-use planners implement boundary management strategies to alter the pattern of human access to protected areas, cluster development to concentrate the footprint of rural housing, and establish conservation agreements through local land trusts to buffer protected areas from the effects of development along protected-area boundaries. To maximize the conservation benefit of protected areas, we suggest that housing development should be restricted within 1 km of their boundaries.

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 06/2025

The NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts.

Data are updated semiannually, at the end of the second and fourth quarters of each year.

Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available.

The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.

This dataset contains replication files for "The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment" by Raj Chetty, Nathaniel Hendren, and Lawrence Katz. For more information, see https://opportunityinsights.org/paper/newmto/. A summary of the related publication follows. There are large differences in individuals’ economic, health, and educational outcomes across neighborhoods in the United States. Motivated by these disparities, the U.S. Department of Housing and Urban Development designed the Moving to Opportunity (MTO) experiment to determine whether providing low-income families assistance in moving to better neighborhoods could improve their economic and health outcomes. The MTO experiment was conducted between 1994 and 1998 in five large U.S. cities. Approximately 4,600 families living in high-poverty public housing projects were randomly assigned to one of three groups: an experimental voucher group that was offered a subsidized housing voucher that came with a requirement to move to a census tract with a poverty rate below 10%, a Section 8 voucher group that was offered a standard housing voucher with no additional contingencies, and a control group that was not offered a voucher (but retained access to public housing). Previous research on the MTO experiment has found that moving to lower-poverty areas greatly improved the mental and physical health of adults. However, prior work found no impacts of the MTO treatments on the earnings of adults and older youth, leading some to conclude that neighborhood environments are not an important component of economic success. In this study, we present a new analysis of the effect of the MTO experiment on children’s long-term outcomes. Our re-analysis is motivated by new research showing that a neighborhood’s effect on children’s outcomes may depend critically on the duration of exposure to that environment. In particular, Chetty and Hendren (2015) use quasi-experimental methods to show that every year spent in a better area during childhood increases a child’s earnings in adulthood, implying that the gains from moving to a better area are larger for children who are younger at the time of the move. In light of this new evidence on childhood exposure effects, we study the long-term impacts of MTO on children who were young when their families moved to better neighborhoods. Prior work has not been able to examine these issues because the younger children in the MTO experiment are only now old enough to be entering the adult labor market. For older children (those between ages 13-18), we find that moving to a lower-poverty neighborhood has a statistically insignificant or slightly negative effect. More generally, the gains from moving to lower-poverty areas decline steadily with the age of the child at the time of the move. We do not find any clear evidence of a “critical age” below which children must move to benefit from a better neighborhood. Rather, every extra year of childhood spent in a low-poverty environment appears to be beneficial, consistent with the findings of Chetty and Hendren (2015). The MTO treatments also had little or no impact on adults’ economic outcomes, consistent with previous results. Together, these studies show that childhood exposure plays a critical role in neighborhoods’ effects on economic outcomes. The experimental voucher increased the earnings of children who moved at young ages in all five experimental sites, for Whites, Blacks, and Hispanics, and for boys and girls. Perhaps most notably, we find robust evidence that the experimental voucher improved long-term outcomes for young boys, a subgroup where prior studies have found little evidence of gains. Our estimates imply that moving a child out of public housing to a low-poverty area when young (at age 8 on average) using a subsidized voucher like the MTO experimental voucher will increase the child’s total lifetime earnings by about $302,000. This is equivalent to a gain of $99,000 per child moved in present value at age 8, discounting future earnings at a 3% interest rate. The additional tax revenue generated from these earnings increases would itself offset the incremental cost of the subsidized voucher relative to providing public housing. We conclude that offering low-income families housing vouchers and assistance in moving to lowerpoverty neighborhoods has substantial benefits for the families themselves and for taxpayers. It appears important to target such housing vouchers to families with young children – perhaps even at birth – to maximize the benefits. Our results provide less support for policies that seek to improve the economic outcomes of adults through residential relocation. More broadly, our findings suggest that efforts to integrate disadvantaged families into mixed-income communities are likely to reduce the persistence of poverty across generations. The opinions expressed in this paper are...

From the perspective of real estate industry experts, such as investors, fund managers, developers, and property companies, the development prospects for real estate in the United States in 2023 were highest in Dallas - Fort Worth (****). Other areas with similarly high prospects for the development of real estate were Nashville and Tampa - St. Petersburg. The leading cities for development prospects in the United States scored at least *** out of 5 ratings where '5' reflected strong and '1' reflected weak prospects.

BASE YEAR	2024
HISTORICAL DATA	2019 - 2023
REGIONS COVERED	North America, Europe, APAC, South America, MEA
REPORT COVERAGE	Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
MARKET SIZE 2024	223.7(USD Billion)
MARKET SIZE 2025	231.1(USD Billion)
MARKET SIZE 2035	320.7(USD Billion)
SEGMENTS COVERED	Land Development Type, Project Scale, Development Methodology, Service Type, Regional
COUNTRIES COVERED	US, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA
KEY MARKET DYNAMICS	Urbanization trends, Infrastructure development investments, Regulatory policies and zoning, Sustainable land use practices, Housing demand fluctuations
MARKET FORECAST UNITS	USD Billion
KEY COMPANIES PROFILED	Groupe Geraud, Meritage Homes, M/I Homes, Century Communities, Tri Pointe Homes, PulteGroup, KB Home, Centrica, NVR, D.R. Horton, Lennar, Beazer Homes, Lennox International, Taylor Morrison, Hovnanian Enterprises
MARKET FORECAST PERIOD	2025 - 2035
KEY MARKET OPPORTUNITIES	Sustainable urban development initiatives, Affordable housing demand surge, Mixed-use development projects, Smart city integration solutions, Resilient infrastructure investments
COMPOUND ANNUAL GROWTH RATE (CAGR)	3.3% (2025 - 2035)

The data provided herein denotes the estimated service areas for all Public Housing Authorities (PHA) receiving assistance through the U.S. Department of Housing and Urban Development (HUD) (excluding Guam, the Marshall Islands, and the U.S. Virgin Islands). HUD’s Office of Policy Development and Research (PD&R) developed this dataset in response to repeated requests from HUD staff, researchers, and external partners. This is an experimental dataset that is designed to aid researchers in studying the HUD-funded Public Housing and Housing Choice Voucher programs. The methodology and the service areas themselves have not been validated by HUD’s Office of Public and Indian Housing (PIH) or the Public Housing Agencies. PD&R welcomes engagement from internal and external stakeholders on the continued refinement and development of this dataset. Please send any comments or questions to GISHelpDesk@hud.gov. Standards used to estimate PHA primary service areas are as follows: State-Level Public Housing Authorities:For the purposes of this dataset State-Level PHAs are identified through either their name, or their PHA Code also known as the Participant Code. Any PHA whose name contains the word “State”, or whose PHA Code begins with a ‘9’ (not including the two-character state code that begins the PHA code) is considered a State-Level PHA, and the service area therefore includes the entirety of that state. County-Level Public Housing Authorities:For the purposes of this dataset County-Level PHAs are identified as any PHA containing the word ‘County’ or ‘COUNTY’ in the organization’s formal name. The service area of a County-Level PHA includes the entire county after which the PHA is named, or the county which contains the majority of the units (combined low-rent and voucher) administered by the PHA. Moreover, a PHA that administers units located in jurisdictions outside the county for which the PHA is named, or the county which contains the majority of the units administered by the PHA, does not include those extraterritorial jurisdictions as part of its service area . Subsequently, the estimated service areas of housing authorities operating at a regional level, that is operating in multiple counties (contiguous or otherwise), are relegated to a single county. Local-Level Public Housing Authorities:For the purposes of this dataset Local-Level PHAs are identified as any PHA that does not fall into the category of State-Level or County-Level Public Housing Authority as described above. The service area for a Local-Level PHA is first defined as the primary Unit of General Local Government (UGLG) served by the PHA. The primary local government jurisdiction is defined as the UGLG that contains the largest share of total units (combined low-rent and voucher) administered by that PHA. However, in cases where greater than 20% of units administered by that PHA are located outside of the primary local government jurisdiction served by the PHA, the PHA’s service area is defined as the entirety of the county that the primary local government is located in.Please note, that the methods used to compile the estimated local PHA service areas illustrated in this dataset remain the same regardless of a state’s allowance for state-wide voucher portability.