In the United States, Hawaii was the state with the most expensive housing, with the typical value of single-family homes in the 35th to 65th percentile range exceeding ******* U.S. dollars. Unsurprisingly, Hawaii also ranked top as the state with the highest cost of living. Meanwhile, a property was the least expensive in West Virginia, where it cost under ******* U.S. dollars to buy the typical single-family home. Single-family home prices increased across most states in the United States between December 2023 and December 2024, except in Louisiana, Florida, and the District of Colombia. According to the Federal Housing Association, house appreciation in 13 states exceeded **** percent in 2023.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

This layer represents the percent householders spend on housing costs. It uses ACS table DP04 Housing Characteristics. There are three categories under " Selected Monthly Costs as as percentage of household income" for households with mortgages, without mortgages and rentals. Percentages are presented for households spending great than 30 percent of their income on housing costs. A detailed description is available here: https://fortress.wa.gov/doh/wtn/WTNPortal#!q0=4742

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q3 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

Mexico's housing market demonstrates significant regional price variations, with Mexico City emerging as the most expensive area for residential property in the third quarter of 2025. The capital city's average house price of 3.93 million Mexican pesos far exceeds the national average of 1.86 million pesos, highlighting the stark contrast in property values across the country. This disparity reflects broader economic and demographic trends shaping Mexico's real estate landscape. Sustained growth in housing prices The Mexican housing market has experienced substantial growth over the past decade, with home prices more than doubling since 2010. By the second quarter of 2025, the nominal house price index reached 287 points, representing a 187 percent increase from the baseline year. Even when adjusted for inflation, the real house price index showed a notable 50 percent growth, underscoring the market's resilience and attractiveness to investors. The mortgage market is dominated by three main player types: Infonavit, Fovissste, and commercial banks including Sofomes. In 2023, Infonavit, a scheme by Mexico's National Housing Fund Institute which provides lending to workers in the formal sector, was responsible for the majority of mortgages granted to individuals. Challenges in mortgage lending Despite the overall growth in housing prices, Mexico's mortgage market has faced challenges in recent years. The number of new mortgage loans granted has declined over the past decade, falling by approximately 200,000 loans between 2008 and 2023. This decrease in lending activity may be attributed to various factors, including economic uncertainties and changing consumer preferences. The state of Mexico, which is home to 13 percent of the country's population, likely plays a significant role in shaping these trends given its large demographic influence on the national housing market.

The exercise after this contains questions that are based on the housing dataset.

1. How many houses have a waterfront? a. 21000 b. 21450 c. 163 d. 173

2. How many houses have 2 floors? a. 2692 b. 8241 c. 10680 d. 161

3. How many houses built before 1960 have a waterfront? a. 80 b. 7309 c. 90 d. 92

4. What is the price of the most expensive house having more than 4 bathrooms? a. 7700000 b. 187000 c. 290000 d. 399000

5. For instance, if the ‘price’ column consists of outliers, how can you make the data clean and remove the redundancies? a. Calculate the IQR range and drop the values outside the range. b. Calculate the p-value and remove the values less than 0.05. c. Calculate the correlation coefficient of the price column and remove the values less than the correlation coefficient. d. Calculate the Z-score of the price column and remove the values less than the z-score.

6. What are the various parameters that can be used to determine the dependent variables in the housing data to determine the price of the house? a. Correlation coefficients b. Z-score c. IQR Range d. Range of the Features

7. If we get the r2 score as 0.38, what inferences can we make about the model and its efficiency? a. The model is 38% accurate, and shows poor efficiency. b. The model is showing 0.38% discrepancies in the outcomes. c. Low difference between observed and fitted values. d. High difference between observed and fitted values.

8. If the metrics show that the p-value for the grade column is 0.092, what all inferences can we make about the grade column? a. Significant in presence of other variables. b. Highly significant in presence of other variables c. insignificance in presence of other variables d. None of the above

9. If the Variance Inflation Factor value for a feature is considerably higher than the other features, what can we say about that column/feature? a. High multicollinearity b. Low multicollinearity c. Both A and B d. None of the above

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

Prices & Characteristics of Spanish Homes

Uncovering Market Trends in Spain

By [source]

About this dataset

This dataset provides a wealth of information about the current Spanish housing market for potential buyers. This comprehensive data set includes research-level information about region, number of rooms, size, price, photos and more for different available properties across the country. This data can help researchers understand the wide pricing range and characteristics associated with these homes in great detail. For example, it allows us to uncover average price per square meter as well as differences in prices between larger and smaller locations. Further exploration also reveals correlations between price and surface area as well as number of rooms and pricing models - all immensely helpful to those wishing to purchase or rent properties in Spain! By further investigating this rich set of information provided by this dataset, prospective property buyers can be more informed when making decisions regarding their next home or investment opportunities within the Spanish housing market

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Welcome to the Prices and Characteristics of Spanish Houses for Sale dataset! This data set contains comprehensive information about Spanish houses for sale, including location, price, size, and number of rooms. Here’s a guide to help you get started.

• Explore the columns included in this dataset: the summary column provides an overview of the property while description provides more in-depth details. The location column offers geographical details about each house; photo displays a picture of each property; recomendado indicates whether or not it has been recommended; price gives you an idea of how much each house costs; size determines how large or small it is; rooms tells you how many bedrooms it has to offer; price/m2 states the Square Meter Price for each home; bathrooms lets you know how many bathrooms it has on the premises; Num Photos shows you the exact number of images available for that home and type directs which type it is (apartment); region helps pinpoint exactly where these homes are located.

• Analyze relationships between variables: use this dataset to uncover interesting correlations between pricing and other characteristics such as size and number of rooms, or between prices in different regions within Spain. You can also gain insight into average pricing by square meter across various locations - this data might be useful if you're looking at making a real estate investment decision based on market trends around Spain's housing sector!

• Research current market trends: review historical data points from within this dataset with regards to pricing changes over time, as well as differences in supply/demand dynamics across distinct locations within Spain's housing market - all these insights can be used when deciding whether or not now would be an ideal time to purchase property in certain areas!
  Overall, we hope that with this information at hand your research into Spain's current housing market will provide useful results and lend insight that may assist your purchase decision process when considering buying S[anish homes!

Research Ideas

• Comparing the average Spanish house price in different regions to determine if prices are more expensive in certain regions.
• Examining the correlation between size and number of rooms to understand which properties would be a better investment given their size.
• Analyzing the relationship between number of photos uploaded for a property and its price, to determine if there is any correlation between them or not

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: pisos.csv | Column name | Description | |:----------------|:------------------------------------------------------------| | summary | A brief description of the property. (Text) | | location | The geographical area or postcode of the property. (Text) | | photo...

The Canadian housing market, particularly in major urban centers, has experienced a prolonged period of rapid price appreciation, driven by factors such as low interest rates, strong population growth, and limited supply. According to the Canada Mortgage and Housing Corporation (CMHC), the national average house price rose by more than 50% between 2020 and 2022, with prices in some major cities, such as Toronto and Vancouver, increasing by even more. This rapid price growth has made it increasingly difficult for many Canadians to afford a home, especially in the country's most desirable markets. However, the Canadian housing market is starting to show signs of cooling in 2023, as rising interest rates and stricter mortgage lending rules from the government begin to take effect. The CMHC predicts that the national average house price will decline by 7.6% in 2023, with prices in some markets, such as Toronto and Vancouver, expected to fall by even more. This cooling is expected to continue in 2024, with the CMHC predicting a further decline in the national average house price of 3.2%. The long-term outlook for the Canadian housing market is more uncertain, but the CMHC expects that prices will continue to rise, albeit at a more moderate pace. The Canadian housing market is one of the most expensive in the world, with prices in major cities like Toronto and Vancouver soaring to record highs in recent years. This has led to a growing concern about affordability, as many Canadians are being priced out of the market. Key drivers for this market are: Increasing Adoption of Remote and Hybrid Work Model. Potential restraints include: Lack of Privacy. Notable trends are: Pandemic Accelerated Luxury Home Sales in Major Canadian Markets.

Reference: https://www.zillow.com/research/zhvi-methodology/

Official Background

In setting out to create a new home price index, a major problem Zillow sought to overcome in existing indices was their inability to deal with the changing composition of properties sold in one time period versus another time period. Both a median sale price index and a repeat sales index are vulnerable to such biases (see the analysis here for an example of how influential the bias can be). For example, if expensive homes sell at a disproportionately higher rate than less expensive homes in one time period, a median sale price index will characterize this market as experiencing price appreciation relative to the prior period of time even if the true value of homes is unchanged between the two periods.

The ideal home price index would be based off sale prices for the same set of homes in each time period so there was never an issue of the sales mix being different across periods. This approach of using a constant basket of goods is widely used, common examples being a commodity price index and a consumer price index. Unfortunately, unlike commodities and consumer goods, for which we can observe prices in all time periods, we can’t observe prices on the same set of homes in all time periods because not all homes are sold in every time period.

The innovation that Zillow developed in 2005 was a way of approximating this ideal home price index by leveraging the valuations Zillow creates on all homes (called Zestimates). Instead of actual sale prices on every home, the index is created from estimated sale prices on every home. While there is some estimation error associated with each estimated sale price (which we report here), this error is just as likely to be above the actual sale price of a home as below (in statistical terms, this is referred to as minimal systematic error). Because of this fact, the distribution of actual sale prices for homes sold in a given time period looks very similar to the distribution of estimated sale prices for this same set of homes. But, importantly, Zillow has estimated sale prices not just for the homes that sold, but for all homes even if they didn’t sell in that time period. From this data, a comprehensive and robust benchmark of home value trends can be computed which is immune to the changing mix of properties that sell in different periods of time (see Dorsey et al. (2010) for another recent discussion of this approach).

For an in-depth comparison of the Zillow Home Value Index to the Case Shiller Home Price Index, please refer to the Zillow Home Value Index Comparison to Case-Shiller

Each Zillow Home Value Index (ZHVI) is a time series tracking the monthly median home value in a particular geographical region. In general, each ZHVI time series begins in April 1996. We generate the ZHVI at seven geographic levels: neighborhood, ZIP code, city, congressional district, county, metropolitan area, state and the nation.

Underlying Data

Estimated sale prices (Zestimates) are computed based on proprietary statistical and machine learning models. These models begin the estimation process by subdividing all of the homes in United States into micro-regions, or subsets of homes either near one another or similar in physical attributes to one another. Within each micro-region, the models observe recent sale transactions and learn the relative contribution of various home attributes in predicting the sale price. These home attributes include physical facts about the home and land, prior sale transactions, tax assessment information and geographic location. Based on the patterns learned, these models can then estimate sale prices on homes that have not yet sold.

The sale transactions from which the models learn patterns include all full-value, arms-length sales that are not foreclosure resales. The purpose of the Zestimate is to give consumers an indication of the fair value of a home under the assumption that it is sold as a conventional, non-foreclosure sale. Similarly, the purpose of the Zillow Home Value Index is to give consumers insight into the home value trends for homes that are not being sold out of foreclosure status. Zillow research indicates that homes sold as foreclosures have typical discounts relative to non-foreclosure sales of between 20 and 40 percent, depending on the foreclosure saturation of the market. This is not to say that the Zestimate is not influenced by foreclosure resales. Zestimates are, in fact, influenced by foreclosure sales, but the pathway of this influence is through the downward pressure foreclosure sales put on non-foreclosure sale prices. It is the price signal observed in the latter that we are attempting to measure and, in turn, predict with the Zestimate.

Market Segments Within each region, we calculate the ZHVI for various subsets of homes (or mar...

According to Cognitive Market Research, the global Residential Real Estate market size was USD 32651.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 5.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 13060.64 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 9795.48 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 7509.87 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1632.58 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 653.03 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2024 to 2031.
The single-family homes category is the fastest growing segment of the Residential Real Estate industry

Market Dynamics of Residential Real Estate Market

Key Drivers for Residential Real Estate Market

Increasing population drives housing demand to Boost Market Growth

Increasing population drives housing demand by creating a need for more residential spaces to accommodate growing numbers of people. As population rises, particularly in urban and suburban areas, demand for housing expands, fueling the residential real estate market. This is especially evident in countries experiencing rapid urbanization, where people move to cities seeking better job opportunities, education, and lifestyle options, further increasing housing needs. Additionally, population growth often correlates with the formation of new households, such as young families or individuals moving out on their own, intensifying the demand for housing units. In response, developers and investors are motivated to build more residential properties, ranging from single-family homes to multifamily units, contributing to market growth and driving real estate values upward. For instance, The Ashwin Sheth Group aims to broaden its residential and commercial offerings in the Mumbai Metropolitan Region (MMR) of India.

Rising incomes and economic stability to Drive Market Growth

Rising incomes and economic stability drive the residential real estate market by boosting consumers’ purchasing power and confidence in long-term investments like homeownership. As incomes increase, people can afford larger down payments, qualify for higher loan amounts, and manage mortgage payments more comfortably, making home buying a more viable option. Economic stability, characterized by low unemployment rates and steady GDP growth, reinforces this confidence, as individuals feel secure in their financial situations. With greater disposable income, many consumers seek to upgrade to larger homes, buy second properties, or invest in luxury real estate, further fueling demand. This economic backdrop attracts both local and foreign investors, leading to more housing developments, increased property values, and a flourishing residential real estate market.

Restraint Factor for the Residential Real Estate Market

High Property Prices will Limit Market Growth

High property prices restrain the residential real estate market by making homeownership unaffordable for a significant portion of the population. As prices rise, potential buyers, particularly first-time homeowners and low- to middle-income families, may find it challenging to secure adequate financing or meet the necessary down payment requirements. This affordability crisis limits the pool of qualified buyers, leading to slower sales and potential stagnation in market growth. Additionally, high property prices can prompt increased demand for rental properties, shifting focus away from home purchases. In markets where prices escalate rapidly, even affluent buyers may hesitate, fearing potential market corrections. Consequently, elevated property values can create a barrier to entry, ultimately restricting the overall health and vibrancy of the residential real estate market.

Impact of Covid-19 on the Residential Real Estate Market

The COVI...

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

In 2020, Hong Kong had the most expensive residential property market worldwide, with an average property price of 1.25 million U.S. dollars. The government of Hong Kong provide public housing for lower-income residents and almost 45 percent of the Hong Kong population lived in public permanent housing in 2018.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

Housing Index in Portugal increased to 258.78 points in the second quarter of 2025 from 247.05 points in the first quarter of 2025. This dataset provides - Portugal House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This map shows households that spend 30 percent or more of their income on housing, a threshold widely used by many affordable housing advocates and official government sources including Housing and Urban Development. Census asks about income and housing costs to understand whether housing is affordable in local communities. When housing is not sufficient or not affordable, income data helps communities: Enroll eligible households in programs designed to assist them.Qualify for grants from the Community Development Block Grant (CDBG), HOME Investment Partnership Program, Emergency Solutions Grants (ESG), Housing Opportunities for Persons with AIDS (HOPWA), and other programs.When rental housing is not affordable, the Department of Housing and Urban Development (HUD) uses rent data to determine the amount of tenant subsidies in housing assistance programs.Map opens in Atlanta. Use the bookmarks or search bar to view other cities. Data is symbolized to show the relationship between burdensome housing costs for owner households with a mortgage and renter households:This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

The manufactured home dealer industry has been navigating a dynamic landscape, influenced by volatile lending conditions and changing consumer preferences. In 2025, the industry’s revenue will stand at $11.4 billion, growing by 3.6% from the previous year. With a focus on affordability, the industry's appeal is growing as traditional housing becomes less accessible due to soaring prices and high mortgage rates. Recent trends highlight dealers' efforts to align with market shifts by investing in quality enhancements and strategic partnerships to bolster occupancy rates. As traditional homeownership becomes increasingly elusive, manufactured homes are emerging as an attractive alternative, catering to a widespread need for cost-effective housing solutions. Over the past five years, the manufactured home industry has faced many challenges. Revenue’s expansion at a CAGR of 5.3% over the past five years signals resilience, driven largely by low interest rates post-pandemic, which initially fueled financing options. However, rising inflation and corresponding interest rate hikes tightened borrowing conditions, impacting sales among core low- to moderate-income buyers. This period also saw significant industry consolidation, with major entities like Skyline Champion Corporation and Cavco Industries expanding their footprint through strategic acquisitions. Despite these hurdles, the industry capitalized on the surging prices of traditional homes, emphasizing affordability and improved product quality, drawing in a broader customer base, including middle- and high-income consumers. Looking ahead, the industry’s growth will moderate to a projected CAGR of 1.3%, reaching a revenue of $12.2 billion by 2030. Dealers are poised to capture an expanded market share as traditional homes remain unaffordable for many, and the aging population seeks budget-friendly, low-maintenance housing options. Technological advancements in production and virtual sales processes promise to cut costs and boost profitability. Meanwhile, customization trends are set to further revolutionize the industry, attracting discerning buyers with personalized offerings that meet diverse needs. As inflation tempers and purchase costs stabilize, the industry's profitability outlook remains strong, offering an enticing proposition for both consumers and investors.